Optimization of Fiber Bragg Gratings Using a Hybrid Optimization Algorithm
NASA Astrophysics Data System (ADS)
Ngo, Nam Quoc; Zheng, Rui Tao; Ng, J. H.; Tjin, S. C.; Binh, L. N.
2007-03-01
A new hybrid optimization algorithm is proposed for the design of a fiber Bragg grating (FBG) with complex characteristics. The hybrid algorithm is a two-tier search that employs a global optimization algorithm (i.e., the Staged Continuous Tabu Search (SCTS) algorithm) and a local optimization method (i.e., the Quasi-Newton method). First, the SCTS global optimization algorithm is used to find a “promising” FBG structure that has a spectral response as close as possible to the targeted spectral response. Then, a local optimization method, namely, the Quasi-Newton method, is applied to further optimize the promising FBG structure obtained from the SCTS algorithm to arrive at a targeted spectral response. To demonstrate the effectiveness of the method, the design and fabrication of an optical bandpass filter are presented.
An Island Based Hybrid Evolutionary Algorithm for Optimization
Yang, Shengxiang
evolutionary algorithm (IHEA) for op- timization, which is based on Particle swarm optimization (PSO), Fast for a simulation optimization problem. A hybrid evolutionary Particle Swarm Op- timization (PSO) method of being selected. A hybrid technique that com- bines GA and PSO, called genetic swarm optimization (GSO
Improved hybrid optimization algorithm for 3D protein structure prediction.
Zhou, Changjun; Hou, Caixia; Wei, Xiaopeng; Zhang, Qiang
2014-07-01
A new improved hybrid optimization algorithm - PGATS algorithm, which is based on toy off-lattice model, is presented for dealing with three-dimensional protein structure prediction problems. The algorithm combines the particle swarm optimization (PSO), genetic algorithm (GA), and tabu search (TS) algorithms. Otherwise, we also take some different improved strategies. The factor of stochastic disturbance is joined in the particle swarm optimization to improve the search ability; the operations of crossover and mutation that are in the genetic algorithm are changed to a kind of random liner method; at last tabu search algorithm is improved by appending a mutation operator. Through the combination of a variety of strategies and algorithms, the protein structure prediction (PSP) in a 3D off-lattice model is achieved. The PSP problem is an NP-hard problem, but the problem can be attributed to a global optimization problem of multi-extremum and multi-parameters. This is the theoretical principle of the hybrid optimization algorithm that is proposed in this paper. The algorithm combines local search and global search, which overcomes the shortcoming of a single algorithm, giving full play to the advantage of each algorithm. In the current universal standard sequences, Fibonacci sequences and real protein sequences are certified. Experiments show that the proposed new method outperforms single algorithms on the accuracy of calculating the protein sequence energy value, which is proved to be an effective way to predict the structure of proteins. PMID:25069136
Genetic Algorithm Optimization of a Cost Competitive Hybrid Rocket Booster
NASA Technical Reports Server (NTRS)
Story, George
2015-01-01
Performance, reliability and cost have always been drivers in the rocket business. Hybrid rockets have been late entries into the launch business due to substantial early development work on liquid rockets and solid rockets. Slowly the technology readiness level of hybrids has been increasing due to various large scale testing and flight tests of hybrid rockets. One remaining issue is the cost of hybrids versus the existing launch propulsion systems. This paper will review the known state-of-the-art hybrid development work to date and incorporate it into a genetic algorithm to optimize the configuration based on various parameters. A cost module will be incorporated to the code based on the weights of the components. The design will be optimized on meeting the performance requirements at the lowest cost.
A Hybrid Ant Colony Algorithm for Loading Pattern Optimization
NASA Astrophysics Data System (ADS)
Hoareau, F.
2014-06-01
Electricité de France (EDF) operates 58 nuclear power plant (NPP), of the Pressurized Water Reactor (PWR) type. The loading pattern (LP) optimization of these NPP is currently done by EDF expert engineers. Within this framework, EDF R&D has developed automatic optimization tools that assist the experts. The latter can resort, for instance, to a loading pattern optimization software based on ant colony algorithm. This paper presents an analysis of the search space of a few realistic loading pattern optimization problems. This analysis leads us to introduce a hybrid algorithm based on ant colony and a local search method. We then show that this new algorithm is able to generate loading patterns of good quality.
Wenbo Zhang; Xiaoguang Zhang; Qingyue Di; Lixia Xi
2010-01-01
In this paper, we presented a new hybrid particle swarm optimization algorithm which is used to design the control unit of an adaptive polarization mode dispersion compensation. Comparing with the conventional particle swarm optimization algorithm, the new algorithm converges to the global optima much faster than the conventional particle swarm optimization algorithm. Experiments show that the new algorithm is easy
A Hybrid Swarm Algorithm for optimizing glaucoma diagnosis.
Raja, Chandrasekaran; Gangatharan, Narayanan
2015-08-01
Glaucoma is among the most common causes of permanent blindness in human. Because the initial symptoms are not evident, mass screening would assist early diagnosis in the vast population. Such mass screening requires an automated diagnosis technique. Our proposed automation consists of pre-processing, optimal wavelet transformation, feature extraction, and classification modules. The hyper analytic wavelet transformation (HWT) based statistical features are extracted from fundus images. Because HWT preserves phase information, it is appropriate for feature extraction. The features are then classified by a Support Vector Machine (SVM) with a radial basis function (RBF) kernel. The filter coefficients of the wavelet transformation process and the SVM-RB width parameter are simultaneously tailored to best-fit the diagnosis by the hybrid Particle Swarm algorithm. To overcome premature convergence, a Group Search Optimizer (GSO) random searching (ranging) and area scanning behavior (around the optima) are embedded within the Particle Swarm Optimization (PSO) framework. We also embed a novel potential-area scanning as a preventive mechanism against premature convergence, rather than diagnosis and cure. This embedding does not compromise the generality and utility of PSO. In two 10-fold cross-validated test runs, the diagnostic accuracy of the proposed hybrid PSO exceeded that of conventional PSO. Furthermore, the hybrid PSO maintained the ability to explore even at later iterations, ensuring maturity in fitness. PMID:26093787
Yannis Marinakis; Magdalene Marinaki; Nikolaos F. Matsatsinis
2007-01-01
This paper introduces a new hybrid algorithmic nature in- spired approach based on the concepts of the Honey Bees Mating Opti- mization Algorithm (HBMO) and of the Greedy Randomized Adaptive Search Procedure (GRASP), for optimally clustering N objects into K clusters. The proposed algorithm for the Clustering Analysis, the Hybrid HBMO-GRASP, is a two phase algorithm which combines a HBMO
A Hybrid Genetic Algorithm for Shape Optimization of the Truss with Discrete Variables
Guo-fu Sun; Shu-cai Li; Chun-mei Zheng; Bo Zhang; Sheng-bo Hou
2009-01-01
A hybrid genetic algorithm is proposed to deal with the shape optimization of the truss based on relative difference quotient method and improved genetic algorithm. The advantages of the genetic algorithm in global optimization and the relative difference quotient method in local searching ability are both included in the HGA method. Numerical example of a 37-bar truss was given to
A homogeneous superconducting magnet design using a hybrid optimization algorithm
NASA Astrophysics Data System (ADS)
Ni, Zhipeng; Wang, Qiuliang; Liu, Feng; Yan, Luguang
2013-12-01
This paper employs a hybrid optimization algorithm with a combination of linear programming (LP) and nonlinear programming (NLP) to design the highly homogeneous superconducting magnets for magnetic resonance imaging (MRI). The whole work is divided into two stages. The first LP stage provides a global optimal current map with several non-zero current clusters, and the mathematical model for the LP was updated by taking into account the maximum axial and radial magnetic field strength limitations. In the second NLP stage, the non-zero current clusters were discretized into practical solenoids. The superconducting conductor consumption was set as the objective function both in the LP and NLP stages to minimize the construction cost. In addition, the peak-peak homogeneity over the volume of imaging (VOI), the scope of 5 Gauss fringe field, and maximum magnetic field strength within superconducting coils were set as constraints. The detailed design process for a dedicated 3.0 T animal MRI scanner was presented. The homogeneous magnet produces a magnetic field quality of 6.0 ppm peak-peak homogeneity over a 16 cm by 18 cm elliptical VOI, and the 5 Gauss fringe field was limited within a 1.5 m by 2.0 m elliptical region.
NASA Astrophysics Data System (ADS)
Igeta, Hideki; Hasegawa, Mikio
Chaotic dynamics have been effectively applied to improve various heuristic algorithms for combinatorial optimization problems in many studies. Currently, the most used chaotic optimization scheme is to drive heuristic solution search algorithms applicable to large-scale problems by chaotic neurodynamics including the tabu effect of the tabu search. Alternatively, meta-heuristic algorithms are used for combinatorial optimization by combining a neighboring solution search algorithm, such as tabu, gradient, or other search method, with a global search algorithm, such as genetic algorithms (GA), ant colony optimization (ACO), or others. In these hybrid approaches, the ACO has effectively optimized the solution of many benchmark problems in the quadratic assignment problem library. In this paper, we propose a novel hybrid method that combines the effective chaotic search algorithm that has better performance than the tabu search and global search algorithms such as ACO and GA. Our results show that the proposed chaotic hybrid algorithm has better performance than the conventional chaotic search and conventional hybrid algorithms. In addition, we show that chaotic search algorithm combined with ACO has better performance than when combined with GA.
Zhang, Jiapu
2010-01-01
Evolutionary algorithms are parallel computing algorithms and simulated annealing algorithm is a sequential computing algorithm. This paper inserts simulated annealing into evolutionary computations and successful developed a hybrid Self-Adaptive Evolutionary Strategy $\\mu+\\lambda$ method and a hybrid Self-Adaptive Classical Evolutionary Programming method. Numerical results on more than 40 benchmark test problems of global optimization show that the hybrid methods presented in this paper are very effective. Lennard-Jones potential energy minimization is another benchmark for testing new global optimization algorithms. It is studied through the amyloid fibril constructions by this paper. To date, there is little molecular structural data available on the AGAAAAGA palindrome in the hydrophobic region (113-120) of prion proteins.This region belongs to the N-terminal unstructured region (1-123) of prion proteins, the structure of which has proved hard to determine using NMR spectroscopy or X-ray crystallography ...
Optimal Bandwidth for Distributed Multi-Pump Raman Amplifier Based on Hybrid Genetic Algorithm
NASA Astrophysics Data System (ADS)
Liu, Xue-Ming; Li, Yan-He
2004-01-01
Based on a hybrid genetic algorithm, the bandwidth for distributed multi-pump Raman amplifier (DMRA) is optimized. Optimal results show that signal bandwidth Deltalambda can be evidently broadened by means of increasing the number of pumps, Deltalambda decreases with the increase of Raman gain and with the improvement of flatness property, and the hybrid erbium-doped fibre amplifier and DMRA can availably overcome the weakness of pure DMRA.
Optimal design of DFG-based wavelength conversion based on hybrid genetic algorithm
NASA Astrophysics Data System (ADS)
Liu, Xueming; Li, Yanhe
2005-01-01
Based on such techniques as clustering, sharing, crowding and elite replacement, a hybrid genetic algorithm (GA) is proposed. This GA can effectively solve the multimodal optimization including the global and local optima in the quasi-phase-matching (QPM) difference frequency generation (DFG). The conversion efficiency and bandwidth, based on QPM-DFG, are optimized by the matrix operator and hybrid GA. Optimized examples for five-, six- and seven-segment QPM gratings are given, respectively. The optimal results show that adding the segment number of QPM can obviously broaden the conversion bandwidth.
Yu, Xiaobing; Cao, Jie; Shan, Haiyan; Zhu, Li; Guo, Jun
2014-01-01
Particle swarm optimization (PSO) and differential evolution (DE) are both efficient and powerful population-based stochastic search techniques for solving optimization problems, which have been widely applied in many scientific and engineering fields. Unfortunately, both of them can easily fly into local optima and lack the ability of jumping out of local optima. A novel adaptive hybrid algorithm based on PSO and DE (HPSO-DE) is formulated by developing a balanced parameter between PSO and DE. Adaptive mutation is carried out on current population when the population clusters around local optima. The HPSO-DE enjoys the advantages of PSO and DE and maintains diversity of the population. Compared with PSO, DE, and their variants, the performance of HPSO-DE is competitive. The balanced parameter sensitivity is discussed in detail. PMID:24688370
Yu, Xiaobing; Cao, Jie; Shan, Haiyan; Zhu, Li; Guo, Jun
2014-01-01
Particle swarm optimization (PSO) and differential evolution (DE) are both efficient and powerful population-based stochastic search techniques for solving optimization problems, which have been widely applied in many scientific and engineering fields. Unfortunately, both of them can easily fly into local optima and lack the ability of jumping out of local optima. A novel adaptive hybrid algorithm based on PSO and DE (HPSO-DE) is formulated by developing a balanced parameter between PSO and DE. Adaptive mutation is carried out on current population when the population clusters around local optima. The HPSO-DE enjoys the advantages of PSO and DE and maintains diversity of the population. Compared with PSO, DE, and their variants, the performance of HPSO-DE is competitive. The balanced parameter sensitivity is discussed in detail. PMID:24688370
M. Adam Khan; A. Senthil Kumar; A. Poomari
In this paper, two different evolutionary algorithm-based neural network models were developed to optimise the unit production\\u000a cost. The hybrid neural network models are, namely, genetic algorithm-based neural network (GA-NN) model and particle swarm\\u000a optimization-based neural network (PSO-NN) model. These hybrid neural network models were used to find the optimal cutting\\u000a conditions of Ti[C,N] mixed alumina-based ceramic cutting tool (CC650)
Improved Fractal Space Filling Curves Hybrid Optimization Algorithm for Vehicle Routing Problem
Yue, Yi-xiang; Zhang, Tong; Yue, Qun-xing
2015-01-01
Vehicle Routing Problem (VRP) is one of the key issues in optimization of modern logistics system. In this paper, a modified VRP model with hard time window is established and a Hybrid Optimization Algorithm (HOA) based on Fractal Space Filling Curves (SFC) method and Genetic Algorithm (GA) is introduced. By incorporating the proposed algorithm, SFC method can find an initial and feasible solution very fast; GA is used to improve the initial solution. Thereafter, experimental software was developed and a large number of experimental computations from Solomon's benchmark have been studied. The experimental results demonstrate the feasibility and effectiveness of the HOA. PMID:26167171
Improved Fractal Space Filling Curves Hybrid Optimization Algorithm for Vehicle Routing Problem.
Yue, Yi-xiang; Zhang, Tong; Yue, Qun-xing
2015-01-01
Vehicle Routing Problem (VRP) is one of the key issues in optimization of modern logistics system. In this paper, a modified VRP model with hard time window is established and a Hybrid Optimization Algorithm (HOA) based on Fractal Space Filling Curves (SFC) method and Genetic Algorithm (GA) is introduced. By incorporating the proposed algorithm, SFC method can find an initial and feasible solution very fast; GA is used to improve the initial solution. Thereafter, experimental software was developed and a large number of experimental computations from Solomon's benchmark have been studied. The experimental results demonstrate the feasibility and effectiveness of the HOA. PMID:26167171
A hybrid Honey Bees Mating Optimization algorithm for the Probabilistic Traveling Salesman Problem
Yannis Marinakis; Magdalene Marinaki
2009-01-01
The probabilistic traveling salesman problem is a variation of the classic traveling salesman problem and one of the most significant stochastic routing problems. In this paper, a new hybrid algorithmic nature inspired approach based on honey bees mating optimization (HBMO), greedy randomized adaptive search procedure (GRASP) and expanding neighborhood search strategy (ENS) is proposed for the solution of the probabilistic
NASA Astrophysics Data System (ADS)
Voglis, C.; Parsopoulos, K. E.; Papageorgiou, D. G.; Lagaris, I. E.; Vrahatis, M. N.
2012-05-01
We present MEMPSODE, a global optimization software tool that integrates two prominent population-based stochastic algorithms, namely Particle Swarm Optimization and Differential Evolution, with well established efficient local search procedures made available via the Merlin optimization environment. The resulting hybrid algorithms, also referred to as Memetic Algorithms, combine the space exploration advantage of their global part with the efficiency asset of the local search, and as expected they have displayed a highly efficient behavior in solving diverse optimization problems. The proposed software is carefully parametrized so as to offer complete control to fully exploit the algorithmic virtues. It is accompanied by comprehensive examples and a large set of widely used test functions, including tough atomic cluster and protein conformation problems.
Hybrid Estimation of Distribution Algorithm for Global Optimization
Zhang, Qingfu
of Distribution algorithm with Local search (EDA/L). Like other evolutionary algorithms, EDA/L maintains of main components of EDA/L. The experimental results demonstrate that EDA/L is better than four other of distribution algorithms (EDAs) [4][5][9][14][20] are a new class of evolutionary algorithms. Like other
An Effective Hybrid Firefly Algorithm with Harmony Search for Global Numerical Optimization
Guo, Lihong; Wang, Gai-Ge; Wang, Heqi; Wang, Dinan
2013-01-01
A hybrid metaheuristic approach by hybridizing harmony search (HS) and firefly algorithm (FA), namely, HS/FA, is proposed to solve function optimization. In HS/FA, the exploration of HS and the exploitation of FA are fully exerted, so HS/FA has a faster convergence speed than HS and FA. Also, top fireflies scheme is introduced to reduce running time, and HS is utilized to mutate between fireflies when updating fireflies. The HS/FA method is verified by various benchmarks. From the experiments, the implementation of HS/FA is better than the standard FA and other eight optimization methods. PMID:24348137
An effective hybrid firefly algorithm with harmony search for global numerical optimization.
Guo, Lihong; Wang, Gai-Ge; Wang, Heqi; Wang, Dinan
2013-01-01
A hybrid metaheuristic approach by hybridizing harmony search (HS) and firefly algorithm (FA), namely, HS/FA, is proposed to solve function optimization. In HS/FA, the exploration of HS and the exploitation of FA are fully exerted, so HS/FA has a faster convergence speed than HS and FA. Also, top fireflies scheme is introduced to reduce running time, and HS is utilized to mutate between fireflies when updating fireflies. The HS/FA method is verified by various benchmarks. From the experiments, the implementation of HS/FA is better than the standard FA and other eight optimization methods. PMID:24348137
Hybrid Random/Deterministic Parallel Algorithms for Convex and Nonconvex Big Data Optimization
NASA Astrophysics Data System (ADS)
Daneshmand, Amir; Facchinei, Francisco; Kungurtsev, Vyacheslav; Scutari, Gesualdo
2015-08-01
We propose a decomposition framework for the parallel optimization of the sum of a differentiable {(possibly nonconvex)} function and a nonsmooth (possibly nonseparable), convex one. The latter term is usually employed to enforce structure in the solution, typically sparsity. The main contribution of this work is a novel \\emph{parallel, hybrid random/deterministic} decomposition scheme wherein, at each iteration, a subset of (block) variables is updated at the same time by minimizing local convex approximations of the original nonconvex function. To tackle with huge-scale problems, the (block) variables to be updated are chosen according to a \\emph{mixed random and deterministic} procedure, which captures the advantages of both pure deterministic and random update-based schemes. Almost sure convergence of the proposed scheme is established. Numerical results show that on huge-scale problems the proposed hybrid random/deterministic algorithm outperforms both random and deterministic schemes.
Lin, Kuan-Cheng; Hsieh, Yi-Hsiu
2015-10-01
The classification and analysis of data is an important issue in today's research. Selecting a suitable set of features makes it possible to classify an enormous quantity of data quickly and efficiently. Feature selection is generally viewed as a problem of feature subset selection, such as combination optimization problems. Evolutionary algorithms using random search methods have proven highly effective in obtaining solutions to problems of optimization in a diversity of applications. In this study, we developed a hybrid evolutionary algorithm based on endocrine-based particle swarm optimization (EPSO) and artificial bee colony (ABC) algorithms in conjunction with a support vector machine (SVM) for the selection of optimal feature subsets for the classification of datasets. The results of experiments using specific UCI medical datasets demonstrate that the accuracy of the proposed hybrid evolutionary algorithm is superior to that of basic PSO, EPSO and ABC algorithms, with regard to classification accuracy using subsets with a reduced number of features. PMID:26289628
NASA Astrophysics Data System (ADS)
Kumar, Vijay M.; Murthy, ANN; Chandrashekara, K.
2012-05-01
The production planning problem of flexible manufacturing system (FMS) concerns with decisions that have to be made before an FMS begins to produce parts according to a given production plan during an upcoming planning horizon. The main aspect of production planning deals with machine loading problem in which selection of a subset of jobs to be manufactured and assignment of their operations to the relevant machines are made. Such problems are not only combinatorial optimization problems, but also happen to be non-deterministic polynomial-time-hard, making it difficult to obtain satisfactory solutions using traditional optimization techniques. In this paper, an attempt has been made to address the machine loading problem with objectives of minimization of system unbalance and maximization of throughput simultaneously while satisfying the system constraints related to available machining time and tool slot designing and using a meta-hybrid heuristic technique based on genetic algorithm and particle swarm optimization. The results reported in this paper demonstrate the model efficiency and examine the performance of the system with respect to measures such as throughput and system utilization.
Peters, Ashton; Chase, J Geoffrey; Van Houten, Elijah E W
2008-11-01
Results from the application of three nonlinear stiffness reconstruction algorithms to two simple cylindrical geometries are presented in this paper. Finite-element simulated harmonic motion data with added noise were initially used to represent a measured surface displacement dataset for each geometry. This motion was used as input to gradient-descent, combinatorial optimization, and hybrid reconstruction algorithms that aimed to reconstruct two shape-based parameters describing the internal stiffness of the geometry. Both the combinatorial optimization and hybrid algorithms showed significant advantages in reconstructed parameter accuracy when compared with the traditional gradient-descent approach, with success metrics improving by 13-28%. Results from the hybrid algorithm applied to silicone phantom displacements demonstrated for the first time the ability of this type of algorithm to reconstruct internal stiffness using only experimentally measured surface motion data. Improvements in the sophistication of the hybrid approach should lead to improved accuracy in reconstructed solutions, as well as enabling reconstructions where the geometry is less straightforward. PMID:18990627
Model-based Layer Estimation using a Hybrid Genetic/Gradient Search Optimization Algorithm
Chambers, D; Lehman, S; Dowla, F
2007-05-17
A particle swarm optimization (PSO) algorithm is combined with a gradient search method in a model-based approach for extracting interface positions in a one-dimensional multilayer structure from acoustic or radar reflections. The basic approach is to predict the reflection measurement using a simulation of one-dimensional wave propagation in a multi-layer, evaluate the error between prediction and measurement, and then update the simulation parameters to minimize the error. Gradient search methods alone fail due to the number of local minima in the error surface close to the desired global minimum. The PSO approach avoids this problem by randomly sampling the region of the error surface around the global minimum, but at the cost of a large number of evaluations of the simulator. The hybrid approach uses the PSO at the beginning to locate the general area around the global minimum then switches to the gradient search method to zero in on it. Examples of the algorithm applied to the detection of interior walls of a building from reflected ultra-wideband radar signals are shown. Other possible applications are optical inspection of coatings and ultrasonic measurement of multilayer structures.
A Novel Hybrid Algorithm for the Topology Optimization of Truss Structure
Wang Renhua; Zhao Xianzhong; Xie Buying
2009-01-01
Structural Topology and Shape Annealing (STSA), as a heuristic mathematic algorithm, achieves the real topology optimization of truss structures but little considering the mechanical characters of structure. Fully stressed design (FSD) criteria, as a mechanical method, is good at sizing optimization of the fixed configuration structures. The paper aims to apply the mechanical algorithm to guide the heuristic search process
NASA Astrophysics Data System (ADS)
Tang, Qiuhua; Li, Zixiang; Zhang, Liping; Floudas, C. A.; Cao, Xiaojun
2015-09-01
Due to the NP-hardness of the two-sided assembly line balancing (TALB) problem, multiple constraints existing in real applications are less studied, especially when one task is involved with several constraints. In this paper, an effective hybrid algorithm is proposed to address the TALB problem with multiple constraints (TALB-MC). Considering the discrete attribute of TALB-MC and the continuous attribute of the standard teaching-learning-based optimization (TLBO) algorithm, the random-keys method is hired in task permutation representation, for the purpose of bridging the gap between them. Subsequently, a special mechanism for handling multiple constraints is developed. In the mechanism, the directions constraint of each task is ensured by the direction check and adjustment. The zoning constraints and the synchronism constraints are satisfied by teasing out the hidden correlations among constraints. The positional constraint is allowed to be violated to some extent in decoding and punished in cost function. Finally, with the TLBO seeking for the global optimum, the variable neighborhood search (VNS) is further hybridized to extend the local search space. The experimental results show that the proposed hybrid algorithm outperforms the late acceptance hill-climbing algorithm (LAHC) for TALB-MC in most cases, especially for large-size problems with multiple constraints, and demonstrates well balance between the exploration and the exploitation. This research proposes an effective and efficient algorithm for solving TALB-MC problem by hybridizing the TLBO and VNS.
A Hybrid Algorithm for Global Optimization Miguel Argaez, Leticia Velazquez, Carlos Quintero
Kearfott, R. Baker
Quintero Department of Mathematical Sciences and Computa- tional Science Program, The University of Texas at El Paso, El Paso, TX 79968, USA leti@utep.edu Hector Klie, and Mary Wheeler Center for Subsurface Modeling, The University of Texas at Austin, Austin, TX 78712, USA. Abstract We propose a hybrid algorithm
Hybrid-optimization algorithm for the management of a conjunctive-use project and well field design
Chiu, Yung-Chia; Nishikawa, Tracy; Martin, Peter
2012-01-01
Hi-Desert Water District (HDWD), the primary water-management agency in the Warren Groundwater Basin, California, plans to construct a waste water treatment plant to reduce future septic-tank effluent from reaching the groundwater system. The treated waste water will be reclaimed by recharging the groundwater basin via recharge ponds as part of a larger conjunctive-use strategy. HDWD wishes to identify the least-cost conjunctiveuse strategies for managing imported surface water, reclaimed water, and local groundwater. As formulated, the mixed-integer nonlinear programming (MINLP) groundwater-management problem seeks to minimize water delivery costs subject to constraints including potential locations of the new pumping wells, California State regulations, groundwater-level constraints, water-supply demand, available imported water, and pump/recharge capacities. In this study, a hybrid-optimization algorithm, which couples a genetic algorithm and successive-linear programming, is developed to solve the MINLP problem. The algorithm was tested by comparing results to the enumerative solution for a simplified version of the HDWD groundwater-management problem. The results indicate that the hybrid-optimization algorithm can identify the global optimum. The hybrid-optimization algorithm is then applied to solve a complex groundwater-management problem. Sensitivity analyses were also performed to assess the impact of varying the new recharge pond orientation, varying the mixing ratio of reclaimed water and pumped water, and varying the amount of imported water available. The developed conjunctive management model can provide HDWD water managers with information that will improve their ability to manage their surface water, reclaimed water, and groundwater resources.
Hybrid-optimization algorithm for the management of a conjunctive-use project and well field design
Chiu, Y.-C.; Nishikawa, T.; Martin, P.
2012-01-01
Hi-Desert Water District (HDWD), the primary water-management agency in the Warren Groundwater Basin, California, plans to construct a waste water treatment plant to reduce future septic-tank effluent from reaching the groundwater system. The treated waste water will be reclaimed by recharging the groundwater basin via recharge ponds as part of a larger conjunctive-use strategy. HDWD wishes to identify the least-cost conjunctive-use strategies for managing imported surface water, reclaimed water, and local groundwater. As formulated, the mixed-integer nonlinear programming (MINLP) groundwater-management problem seeks to minimize water-delivery costs subject to constraints including potential locations of the new pumping wells, California State regulations, groundwater-level constraints, water-supply demand, available imported water, and pump/recharge capacities. In this study, a hybrid-optimization algorithm, which couples a genetic algorithm and successive-linear programming, is developed to solve the MINLP problem. The algorithm was tested by comparing results to the enumerative solution for a simplified version of the HDWD groundwater-management problem. The results indicate that the hybrid-optimization algorithm can identify the global optimum. The hybrid-optimization algorithm is then applied to solve a complex groundwater-management problem. Sensitivity analyses were also performed to assess the impact of varying the new recharge pond orientation, varying the mixing ratio of reclaimed water and pumped water, and varying the amount of imported water available. The developed conjunctive management model can provide HDWD water managers with information that will improve their ability to manage their surface water, reclaimed water, and groundwater resources. ?? 2011, National Ground Water Association.
Hybrid-optimization algorithm for the management of a conjunctive-use project and well field design.
Chiu, Yung-Chia; Nishikawa, Tracy; Martin, Peter
2012-01-01
Hi-Desert Water District (HDWD), the primary water-management agency in the Warren Groundwater Basin, California, plans to construct a waste water treatment plant to reduce future septic-tank effluent from reaching the groundwater system. The treated waste water will be reclaimed by recharging the groundwater basin via recharge ponds as part of a larger conjunctive-use strategy. HDWD wishes to identify the least-cost conjunctive-use strategies for managing imported surface water, reclaimed water, and local groundwater. As formulated, the mixed-integer nonlinear programming (MINLP) groundwater-management problem seeks to minimize water-delivery costs subject to constraints including potential locations of the new pumping wells, California State regulations, groundwater-level constraints, water-supply demand, available imported water, and pump/recharge capacities. In this study, a hybrid-optimization algorithm, which couples a genetic algorithm and successive-linear programming, is developed to solve the MINLP problem. The algorithm was tested by comparing results to the enumerative solution for a simplified version of the HDWD groundwater-management problem. The results indicate that the hybrid-optimization algorithm can identify the global optimum. The hybrid-optimization algorithm is then applied to solve a complex groundwater-management problem. Sensitivity analyses were also performed to assess the impact of varying the new recharge pond orientation, varying the mixing ratio of reclaimed water and pumped water, and varying the amount of imported water available. The developed conjunctive management model can provide HDWD water managers with information that will improve their ability to manage their surface water, reclaimed water, and groundwater resources. PMID:21635245
Pokharel, Shyam; Rana, Suresh; Blikenstaff, Joseph; Sadeghi, Amir; Prestidge, Bradley
2013-01-01
The purpose of this study is to investigate the effectiveness of the HIPO planning and optimization algorithm for real-time prostate HDR brachytherapy. This study consists of 20 patients who underwent ultrasound-based real-time HDR brachytherapy of the prostate using the treatment planning system called Oncentra Prostate (SWIFT version 3.0). The treatment plans for all patients were optimized using inverse dose-volume histogram-based optimization followed by graphical optimization (GRO) in real time. The GRO is manual manipulation of isodose lines slice by slice. The quality of the plan heavily depends on planner expertise and experience. The data for all patients were retrieved later, and treatment plans were created and optimized using HIPO algorithm with the same set of dose constraints, number of catheters, and set of contours as in the real-time optimization algorithm. The HIPO algorithm is a hybrid because it combines both stochastic and deterministic algorithms. The stochastic algorithm, called simulated annealing, searches the optimal catheter distributions for a given set of dose objectives. The deterministic algorithm, called dose-volume histogram-based optimization (DVHO), optimizes three-dimensional dose distribution quickly by moving straight downhill once it is in the advantageous region of the search space given by the stochastic algorithm. The PTV receiving 100% of the prescription dose (V100) was 97.56% and 95.38% with GRO and HIPO, respectively. The mean dose (D(mean)) and minimum dose to 10% volume (D10) for the urethra, rectum, and bladder were all statistically lower with HIPO compared to GRO using the student pair t-test at 5% significance level. HIPO can provide treatment plans with comparable target coverage to that of GRO with a reduction in dose to the critical structures. PMID:23835384
Moteghaed, Niloofar Yousefi; Maghooli, Keivan; Pirhadi, Shiva; Garshasbi, Masoud
2015-01-01
The improvement of high-through-put gene profiling based microarrays technology has provided monitoring the expression value of thousands of genes simultaneously. Detailed examination of changes in expression levels of genes can help physicians to have efficient diagnosing, classification of tumors and cancer's types as well as effective treatments. Finding genes that can classify the group of cancers correctly based on hybrid optimization algorithms is the main purpose of this paper. In this paper, a hybrid particle swarm optimization and genetic algorithm method are used for gene selection and also artificial neural network (ANN) is adopted as the classifier. In this work, we have improved the ability of the algorithm for the classification problem by finding small group of biomarkers and also best parameters of the classifier. The proposed approach is tested on three benchmark gene expression data sets: Blood (acute myeloid leukemia, acute lymphoblastic leukemia), colon and breast datasets. We used 10-fold cross-validation to achieve accuracy and also decision tree algorithm to find the relation between the biomarkers for biological point of view. To test the ability of the trained ANN models to categorize the cancers, we analyzed additional blinded samples that were not previously used for the training procedure. Experimental results show that the proposed method can reduce the dimension of the data set and confirm the most informative gene subset and improve classification accuracy with best parameters based on datasets. PMID:26120567
A hybrid Particle Swarm Optimization - Simplex algorithm (PSOS) for structural damage identification
O. Begambre; José Elias Laier
2009-01-01
This study proposes a new PSOS-model based damage identification procedure using frequency domain data. The formulation of the objective function for the minimization problem is based on the Frequency Response Functions (FRFs) of the system. A novel strategy for the control of the Particle Swarm Optimization (PSO) parameters based on the Nelder–Mead algorithm (Simplex method) is presented; consequently, the convergence
Jing-ru Zhang; Jun Zhang; Tat-ming Lok; Michael R. Lyu
2007-01-01
The particle swarm optimization algorithm was showed to converge rapidly during the initial stages of a global search, but around global optimum, the search process will become very slow. On the contrary, the gradient descending method can achieve faster convergent speed around global optimum, and at the same time, the convergent accuracy can be higher. So in this paper, a
Lu, Shi Jing; Salleh, Abdul Hakim Mohamed; Mohamad, Mohd Saberi; Deris, Safaai; Omatu, Sigeru; Yoshioka, Michifumi
2014-09-28
Reconstructions of genome-scale metabolic networks from different organisms have become popular in recent years. Metabolic engineering can simulate the reconstruction process to obtain desirable phenotypes. In previous studies, optimization algorithms have been implemented to identify the near-optimal sets of knockout genes for improving metabolite production. However, previous works contained premature convergence and the stop criteria were not clear for each case. Therefore, this study proposes an algorithm that is a hybrid of the ant colony optimization algorithm and flux balance analysis (ACOFBA) to predict near optimal sets of gene knockouts in an effort to maximize growth rates and the production of certain metabolites. Here, we present a case study that uses Baker's yeast, also known as Saccharomyces cerevisiae, as the model organism and target the rate of vanillin production for optimization. The results of this study are the growth rate of the model organism after gene deletion and a list of knockout genes. The ACOFBA algorithm was found to improve the yield of vanillin in terms of growth rate and production compared with the previous algorithms. PMID:25462325
NASA Astrophysics Data System (ADS)
Feng, Kunpeng; Zhou, Tong; Cui, Jiwen; Tan, Jiubin
2014-11-01
This paper presents a novel example-based super-resolution (SR) algorithm with improved k-means cluster. In this algorithm, genetic k-means (GKM) with hybrid particle swarm optimization (HPSO) is employed to improve the reconstruction of high-resolution (HR) images, and a pre-processing of classification in frequency is used to accelerate the procedure. Self-redundancy across different scales of a natural image is also utilized to build attached training set to expand example-based information. Meanwhile, a reconstruction algorithm based on hybrid supervise locally linear embedding (HSLLE) is proposed which uses training sets, high-resolution images and self-redundancy across different scales of a natural image. Experimental results show that patches are classified rapidly in training set processing session and the runtime of reconstruction is half of traditional algorithm at least in super-resolution session. And clustering and attached training set lead to a better recovery of low-resolution (LR) image.
NASA Astrophysics Data System (ADS)
Gharehbaghi, Sadjad; Khatibinia, Mohsen
2015-03-01
A reliable seismic-resistant design of structures is achieved in accordance with the seismic design codes by designing structures under seven or more pairs of earthquake records. Based on the recommendations of seismic design codes, the average time-history responses (ATHR) of structure is required. This paper focuses on the optimal seismic design of reinforced concrete (RC) structures against ten earthquake records using a hybrid of particle swarm optimization algorithm and an intelligent regression model (IRM). In order to reduce the computational time of optimization procedure due to the computational efforts of time-history analyses, IRM is proposed to accurately predict ATHR of structures. The proposed IRM consists of the combination of the subtractive algorithm (SA), K-means clustering approach and wavelet weighted least squares support vector machine (WWLS-SVM). To predict ATHR of structures, first, the input-output samples of structures are classified by SA and K-means clustering approach. Then, WWLS-SVM is trained with few samples and high accuracy for each cluster. 9- and 18-storey RC frames are designed optimally to illustrate the effectiveness and practicality of the proposed IRM. The numerical results demonstrate the efficiency and computational advantages of IRM for optimal design of structures subjected to time-history earthquake loads.
Broadband and broad-angle low-scattering metasurface based on hybrid optimization algorithm.
Wang, Ke; Zhao, Jie; Cheng, Qiang; Dong, Di Sha; Cui, Tie Jun
2014-01-01
A broadband and broad-angle low-scattering metasurface is designed, fabricated, and characterized. Based on the optimization algorithm and far-field scattering pattern analysis, we propose a rapid and efficient method to design metasurfaces, which avoids the large amount of time-consuming electromagnetic simulations. Full-wave simulation and measurement results show that the proposed metasurface is insensitive to the polarization of incident waves, and presents good scattering-reduction properties for oblique incident waves. PMID:25089367
Broadband and Broad-Angle Low-Scattering Metasurface Based on Hybrid Optimization Algorithm
Wang, Ke; Zhao, Jie; Cheng, Qiang; Dong, Di Sha; Cui, Tie Jun
2014-01-01
A broadband and broad-angle low-scattering metasurface is designed, fabricated, and characterized. Based on the optimization algorithm and far-field scattering pattern analysis, we propose a rapid and efficient method to design metasurfaces, which avoids the large amount of time-consuming electromagnetic simulations. Full-wave simulation and measurement results show that the proposed metasurface is insensitive to the polarization of incident waves, and presents good scattering-reduction properties for oblique incident waves. PMID:25089367
PSOVina: The hybrid particle swarm optimization algorithm for protein-ligand docking.
Ng, Marcus C K; Fong, Simon; Siu, Shirley W I
2015-06-01
Protein-ligand docking is an essential step in modern drug discovery process. The challenge here is to accurately predict and efficiently optimize the position and orientation of ligands in the binding pocket of a target protein. In this paper, we present a new method called PSOVina which combined the particle swarm optimization (PSO) algorithm with the efficient Broyden-Fletcher-Goldfarb-Shannon (BFGS) local search method adopted in AutoDock Vina to tackle the conformational search problem in docking. Using a diverse data set of 201 protein-ligand complexes from the PDBbind database and a full set of ligands and decoys for four representative targets from the directory of useful decoys (DUD) virtual screening data set, we assessed the docking performance of PSOVina in comparison to the original Vina program. Our results showed that PSOVina achieves a remarkable execution time reduction of 51-60% without compromising the prediction accuracies in the docking and virtual screening experiments. This improvement in time efficiency makes PSOVina a better choice of a docking tool in large-scale protein-ligand docking applications. Our work lays the foundation for the future development of swarm-based algorithms in molecular docking programs. PSOVina is freely available to non-commercial users at http://cbbio.cis.umac.mo . PMID:25800162
NASA Astrophysics Data System (ADS)
Mahmood, Zakaria N.; Mahmuddin, Massudi; Mahmood, Mohammed Nooraldeen
Encoding proteins of amino acid sequence to predict classified into their respective families and subfamilies is important research area. However for a given protein, knowing the exact action whether hormonal, enzymatic, transmembranal or nuclear receptors does not depend solely on amino acid sequence but on the way the amino acid thread folds as well. This study provides a prototype system that able to predict a protein tertiary structure. Several methods are used to develop and evaluate the system to produce better accuracy in protein 3D structure prediction. The Bees Optimization algorithm which inspired from the honey bees food foraging method, is used in the searching phase. In this study, the experiment is conducted on short sequence proteins that have been used by the previous researches using well-known tools. The proposed approach shows a promising result.
HOPSPACK: Hybrid Optimization Parallel Search Package.
Gray, Genetha A.; Kolda, Tamara G.; Griffin, Joshua; Taddy, Matt; Martinez-Canales, Monica
2008-12-01
In this paper, we describe the technical details of HOPSPACK (Hybrid Optimization Parallel SearchPackage), a new software platform which facilitates combining multiple optimization routines into asingle, tightly-coupled, hybrid algorithm that supports parallel function evaluations. The frameworkis designed such that existing optimization source code can be easily incorporated with minimalcode modification. By maintaining the integrity of each individual solver, the strengths and codesophistication of the original optimization package are retained and exploited.4
Cheng Qiming; Cheng Yinman; Guo Ruiqing; Zheng Yong
2009-01-01
As to the multi-variable, close coupling, nonlinear and time-varying characteristics of the ball mill pulverizing system, the forward NN-PID controller based on chaos PSO-BP hybrid optimization algorithms for decoupling control system of ball mill was proposed. In this controller, the control parameters of PID controller are adaptively adjusted by forward NN, the weights of NN are optimized by the mixed
A hybrid discrete Artificial Bee Colony - GRASP algorithm for clustering
Y. Marinakis; M. Marinaki; N. Matsatsinis
2009-01-01
This paper presents a new hybrid algorithm, which is based on the concepts of the artificial bee colony (ABC) and greedy randomized adaptive search procedure (GRASP), for optimally clustering N objects into K clusters. The proposed algorithm is a two phase algorithm which combines an artificial bee colony optimization algorithm for the solution of the feature selection problem and a
ccsd-00004191,version1-8Feb2005 Algorithms for Hybrid Optimal Control
Dumas, Jean-Guillaume
.Rondepierre@imag.fr www-lmc.imag.fr/lmc-mosaic/Aude.Rondepierre Abstract. We consider a non linear ordinary differential nonlinear. Since the years 1950-1970, the theory of optimal control has been extensively developed and has amount of theory have been well studied, but resolutions are mainly numerical. In this paper, we consider
Hybrid pso algorithm for estimation modulus of elasticity of wood
Mingbao Li; Jiawei Zhang
2009-01-01
Particle swarm optimization algorithm based neural network construction has been presented to calibrate the complex nonlinear relationship between modulus of elasticity (MOE) and wood physical property parameters. Consider that the traditional BP algorithm has shortcomings of converging slowly and easily trapping a local minimum value, a hybrid algorithm using particle swarm optimization (PSO) and back propagation (BP) is adopted to
A hybrid PSO-BP algorithm and its application
Jie Hu; Xiangjin Zeng
2010-01-01
An approach that neural network optimized with PSO algorithm is proposed in the paper. Unlike conventional training method with gradient descent method only, this paper introduces a hybrid training algorithm by combining the PSO and BP algorithm. The PSO is used to optimize the initial parameters of the BP neural network, including the weights and biases. It can effectively better
NASA Astrophysics Data System (ADS)
Ma, Denglong; Wang, Simin; Zhang, Zaoxiao
2014-09-01
In order to identify the source term of gas emission in atmosphere, an improved hybrid algorithm combined with the minimum relative entropy (MRE) and particle swarm optimization (PSO) method was presented. Not only are the estimated source parameters obtained, but also the confidence intervals at some probability levels. If only the source strength was required to be determined, the problem can be viewed as a linear inverse problem directly, which can be solved by original MRE method successfully. When both source strength and location are unknown, the common gas dispersion model should be transformed to be a linear system. Although the transformed linear model has some differences from that in original MRE method, satisfied estimation results were still obtained by adding iteratively adaptive adjustment parameters in the MRE-PSO method. The dependence of the MRE-PSO method on prior information such as lower and upper bound, prior expected values and noises were also discussed. The results showed that the confidence intervals and estimated parameters are influenced little by the prior bounds and expected values, but the errors affect the estimation results greatly. The simulation and experiment verification results showed that the MRE-PSO method is able to identify the source parameters with satisfied results. Finally, the error model was probed and then it was added in the MRE-PSO method. The addition of error model improves the performance of the identification method. Therefore, the MRE-PSO method with adjustment parameters proposed in this paper is a potential good method to resolve inverse problem in atmosphere environment.
Aerodynamic Shape Optimization Using Hybridized Differential Evolution
NASA Technical Reports Server (NTRS)
Madavan, Nateri K.
2003-01-01
An aerodynamic shape optimization method that uses an evolutionary algorithm known at Differential Evolution (DE) in conjunction with various hybridization strategies is described. DE is a simple and robust evolutionary strategy that has been proven effective in determining the global optimum for several difficult optimization problems. Various hybridization strategies for DE are explored, including the use of neural networks as well as traditional local search methods. A Navier-Stokes solver is used to evaluate the various intermediate designs and provide inputs to the hybrid DE optimizer. The method is implemented on distributed parallel computers so that new designs can be obtained within reasonable turnaround times. Results are presented for the inverse design of a turbine airfoil from a modern jet engine. (The final paper will include at least one other aerodynamic design application). The capability of the method to search large design spaces and obtain the optimal airfoils in an automatic fashion is demonstrated.
A Hybrid Differential Invasive Weed Algorithm for Congestion Management
NASA Astrophysics Data System (ADS)
Basak, Aniruddha; Pal, Siddharth; Pandi, V. Ravikumar; Panigrahi, B. K.; Das, Swagatam
This work is dedicated to solve the problem of congestion management in restructured power systems. Nowadays we have open access market which pushes the power system operation to their limits for maximum economic benefits but at the same time making the system more susceptible to congestion. In this regard congestion management is absolutely vital. In this paper we try to remove congestion by generation rescheduling where the cost involved in the rescheduling process is minimized. The proposed algorithm is a hybrid of Invasive Weed Optimization (IWO) and Differential Evolution (DE). The resultant hybrid algorithm was applied on standard IEEE 30 bus system and observed to beat existing algorithms like Simple Bacterial foraging (SBF), Genetic Algorithm (GA), Invasive Weed Optimization (IWO), Differential Evolution (DE) and hybrid algorithms like Hybrid Bacterial Foraging and Differential Evolution (HBFDE) and Adaptive Bacterial Foraging with Nelder Mead (ABFNM).
Hybrid Algorithms for Fuzzy Reverse Supply Chain Network Design
Che, Z. H.; Chiang, Tzu-An; Kuo, Y. C.
2014-01-01
In consideration of capacity constraints, fuzzy defect ratio, and fuzzy transport loss ratio, this paper attempted to establish an optimized decision model for production planning and distribution of a multiphase, multiproduct reverse supply chain, which addresses defects returned to original manufacturers, and in addition, develops hybrid algorithms such as Particle Swarm Optimization-Genetic Algorithm (PSO-GA), Genetic Algorithm-Simulated Annealing (GA-SA), and Particle Swarm Optimization-Simulated Annealing (PSO-SA) for solving the optimized model. During a case study of a multi-phase, multi-product reverse supply chain network, this paper explained the suitability of the optimized decision model and the applicability of the algorithms. Finally, the hybrid algorithms showed excellent solving capability when compared with original GA and PSO methods. PMID:24892057
Hybrid algorithms for fuzzy reverse supply chain network design.
Che, Z H; Chiang, Tzu-An; Kuo, Y C; Cui, Zhihua
2014-01-01
In consideration of capacity constraints, fuzzy defect ratio, and fuzzy transport loss ratio, this paper attempted to establish an optimized decision model for production planning and distribution of a multiphase, multiproduct reverse supply chain, which addresses defects returned to original manufacturers, and in addition, develops hybrid algorithms such as Particle Swarm Optimization-Genetic Algorithm (PSO-GA), Genetic Algorithm-Simulated Annealing (GA-SA), and Particle Swarm Optimization-Simulated Annealing (PSO-SA) for solving the optimized model. During a case study of a multi-phase, multi-product reverse supply chain network, this paper explained the suitability of the optimized decision model and the applicability of the algorithms. Finally, the hybrid algorithms showed excellent solving capability when compared with original GA and PSO methods. PMID:24892057
An Algorithmic Framework for Multiobjective Optimization
Ganesan, T.; Elamvazuthi, I.; Shaari, Ku Zilati Ku; Vasant, P.
2013-01-01
Multiobjective (MO) optimization is an emerging field which is increasingly being encountered in many fields globally. Various metaheuristic techniques such as differential evolution (DE), genetic algorithm (GA), gravitational search algorithm (GSA), and particle swarm optimization (PSO) have been used in conjunction with scalarization techniques such as weighted sum approach and the normal-boundary intersection (NBI) method to solve MO problems. Nevertheless, many challenges still arise especially when dealing with problems with multiple objectives (especially in cases more than two). In addition, problems with extensive computational overhead emerge when dealing with hybrid algorithms. This paper discusses these issues by proposing an alternative framework that utilizes algorithmic concepts related to the problem structure for generating efficient and effective algorithms. This paper proposes a framework to generate new high-performance algorithms with minimal computational overhead for MO optimization. PMID:24470795
An algorithmic framework for multiobjective optimization.
Ganesan, T; Elamvazuthi, I; Shaari, Ku Zilati Ku; Vasant, P
2013-01-01
Multiobjective (MO) optimization is an emerging field which is increasingly being encountered in many fields globally. Various metaheuristic techniques such as differential evolution (DE), genetic algorithm (GA), gravitational search algorithm (GSA), and particle swarm optimization (PSO) have been used in conjunction with scalarization techniques such as weighted sum approach and the normal-boundary intersection (NBI) method to solve MO problems. Nevertheless, many challenges still arise especially when dealing with problems with multiple objectives (especially in cases more than two). In addition, problems with extensive computational overhead emerge when dealing with hybrid algorithms. This paper discusses these issues by proposing an alternative framework that utilizes algorithmic concepts related to the problem structure for generating efficient and effective algorithms. This paper proposes a framework to generate new high-performance algorithms with minimal computational overhead for MO optimization. PMID:24470795
X. H. Shi; L. M. Wan; H. P. Lee; X. W. Yang; L. M. Wang; Y. C. Liang
2003-01-01
This paper presents an improved genetic algorithm with variable population-size (VPGA) inspired by the natural features of the variable size of the population. Based on the VPGA and the particle swarm optimization (PSO) algorithms, this paper also proposes a novel hybrid approach called PSO-GA based hybrid evolutionary algorithm (PGBHEA). Simulations show that both VPGA and PGBHEA are effective for the
Choon, Yee Wen; Mohamad, Mohd Saberi; Deris, Safaai; Illias, Rosli Md; Chong, Chuii Khim; Chai, Lian En
2014-03-01
Microbial strain optimization focuses on improving technological properties of the strain of microorganisms. However, the complexities of the metabolic networks, which lead to data ambiguity, often cause genetic modification on the desirable phenotypes difficult to predict. Furthermore, vast number of reactions in cellular metabolism lead to the combinatorial problem in obtaining optimal gene deletion strategy. Consequently, the computation time increases exponentially with the increase in the size of the problem. Hence, we propose an extension of a hybrid of Bees Algorithm and Flux Balance Analysis (BAFBA) by integrating OptKnock into BAFBA to validate the result. This paper presents a number of computational experiments to test on the performance and capability of BAFBA. Escherichia coli, Bacillus subtilis and Clostridium thermocellum are the model organisms in this paper. Also included is the identification of potential reactions to improve the production of succinic acid, lactic acid and ethanol, plus the discussion on the changes in the flux distribution of the predicted mutants. BAFBA shows potential in suggesting the non-intuitive gene knockout strategies and a low variability among the several runs. The results show that BAFBA is suitable, reliable and applicable in predicting optimal gene knockout strategy. PMID:23892659
Hybrid Ant Algorithm and Applications for Vehicle Routing Problem
NASA Astrophysics Data System (ADS)
Xiao, Zhang; Jiang-qing, Wang
Ant colony optimization (ACO) is a metaheuristic method that inspired by the behavior of real ant colonies. ACO has been successfully applied to several combinatorial optimization problems, but it has some short-comings like its slow computing speed and local-convergence. For solving Vehicle Routing Problem, we proposed Hybrid Ant Algorithm (HAA) in order to improve both the performance of the algorithm and the quality of solutions. The proposed algorithm took the advantages of Nearest Neighbor (NN) heuristic and ACO for solving VRP, it also expanded the scope of solution space and improves the global ability of the algorithm through importing mutation operation, combining 2-opt heuristics and adjusting the configuration of parameters dynamically. Computational results indicate that the hybrid ant algorithm can get optimal resolution of VRP effectively.
Hybrid Genetic Algorithms for Feature Selection
Il-Seok Oh; Jin-Seon Lee; Byung-Ro Moon
2004-01-01
This paper proposes a novel hybrid genetic algorithm for feature selection. Local search operations are devised and embedded in hybrid GAs to fine-tune the search. The operations are parameterized in terms of their fine-tuning power, and their effectiveness and timing requirements are analyzed and compared. The hybridization technique produces two desirable effects: a significant improvement in the final performance and
DongSeop Lee; Luis Felipe Gonzalez; Jacques Périaux; Karkenahalli Srinivas
2011-01-01
A number of game strategies have been developed in past decades and used in the fields of economics, engi- neering, computer science, and biology due to their efficiency in solving design optimization problems. In addition, research in multiobjective and multidisciplinary design optimization has focused on developing a robust and efficient optimization method so it can produce a set of high
UNCORRECTEDPROOF Algorithms for Optimizing Rheology
Yang, Youqing "Richard"
UNCORRECTEDPROOF Algorithms for Optimizing Rheology and Loading Forces in Finite Element Models forces (loading) and lithospheric properties (rheology and structure). Unlike engineering problems to seek optimal loading conditions and rheological parameters in models of lithospheric deformation
Hybrid optimization methods for Full Waveform Inversion
NASA Astrophysics Data System (ADS)
Datta, D.; Sen, M. K.
2014-12-01
FWI is slowly becoming the mainstream method to estimate velocity models of the subsurface from seismic data. Typically it makes use of a gradient descent approach in which a model update is computed by back propagating the residual seismograms and cross correlating with the forward propagating wavefields at each grid point in the subsurface model. FWI is a local optimization technique, which requires the starting model to be very close to the true model. Because the objective function is multimodal with many local minima, the requirement of good starting model becomes essential. A starting model is generated using travel time tomography. We propose two hybrid FWI algorithms one of which generates a very good starting model for a conventional FWI and the other, which works with a population of models uses gradient information from multiple starting locations in guiding the search. The first approach uses a sparse parameterization of model space using non-oscillatory splines, whose coeffiencts are estimated using an optimization algorithm like very fast simulated annealing (VFSA) by minimizing the misfit between the observed and synthetic data. The estimated velocity model is then used as a starting model for gradient-based FWI. This is done in the shot domain by converting the end-on marine geometry to a split spread geometry using the principle of reciprocity. The second approach is to uses an alternate global optimization algorithm called particle swarm optimization (PSO) where PSO update rules are applied. However, we employ a new gradient guided PSO that exploits the gradient information as well. This approach avoids the local minima and converges faster than a conventional PSO. We demonstrate our methods with application to 2D marine data sets from offshore India. Each line comprises over 1000 shots; our hybrid methods produce geologically meaningful velocity models fairly rapidly on a GPU cluster. We show that starting with the hybrid model gives a much better velocity model than starting with a simple smooth model.
PMSM Driver Based on Hybrid Particle Swarm Optimization and CMAC
NASA Astrophysics Data System (ADS)
Tu, Ji; Cao, Shaozhong
A novel hybrid particle swarm optimization (PSO) and cerebellar model articulation controller (CMAC) is introduced to the permanent magnet synchronous motor (PMSM) driver. PSO can simulate the random learning among the individuals of population and CMAC can simulate the self-learning of an individual. To validate the ability and superiority of the novel algorithm, experiments and comparisons have been done in MATLAB/SIMULINK. Analysis among PSO, hybrid PSO-CMAC and CMAC feed-forward control is also given. The results prove that the electric torque ripple and torque disturbance of the PMSM driver can be reduced by using the hybrid PSO-CMAC algorithm.
Li, Jun-qing; Pan, Quan-ke; Mao, Kun
2014-01-01
A hybrid algorithm which combines particle swarm optimization (PSO) and iterated local search (ILS) is proposed for solving the hybrid flowshop scheduling (HFS) problem with preventive maintenance (PM) activities. In the proposed algorithm, different crossover operators and mutation operators are investigated. In addition, an efficient multiple insert mutation operator is developed for enhancing the searching ability of the algorithm. Furthermore, an ILS-based local search procedure is embedded in the algorithm to improve the exploitation ability of the proposed algorithm. The detailed experimental parameter for the canonical PSO is tuning. The proposed algorithm is tested on the variation of 77 Carlier and Néron's benchmark problems. Detailed comparisons with the present efficient algorithms, including hGA, ILS, PSO, and IG, verify the efficiency and effectiveness of the proposed algorithm. PMID:24883414
Algorithms for bilevel optimization
NASA Technical Reports Server (NTRS)
Alexandrov, Natalia; Dennis, J. E., Jr.
1994-01-01
General multilevel nonlinear optimization problems arise in design of complex systems and can be used as a means of regularization for multi-criteria optimization problems. Here, for clarity in displaying our ideas, we restrict ourselves to general bi-level optimization problems, and we present two solution approaches. Both approaches use a trust-region globalization strategy, and they can be easily extended to handle the general multilevel problem. We make no convexity assumptions, but we do assume that the problem has a nondegenerate feasible set. We consider necessary optimality conditions for the bi-level problem formulations and discuss results that can be extended to obtain multilevel optimization formulations with constraints at each level.
Bayesian network structure learning using chaos hybrid genetic algorithm
NASA Astrophysics Data System (ADS)
Shen, Jiajie; Lin, Feng; Sun, Wei; Chang, KC
2012-06-01
A new Bayesian network (BN) learning method using a hybrid algorithm and chaos theory is proposed. The principles of mutation and crossover in genetic algorithm and the cloud-based adaptive inertia weight were incorporated into the proposed simple particle swarm optimization (sPSO) algorithm to achieve better diversity, and improve the convergence speed. By means of ergodicity and randomicity of chaos algorithm, the initial network structure population is generated by using chaotic mapping with uniform search under structure constraints. When the algorithm converges to a local minimal, a chaotic searching is started to skip the local minima and to identify a potentially better network structure. The experiment results show that this algorithm can be effectively used for BN structure learning.
5. Greedy and other efficient optimization algorithms
Keil, David M.
5. Greedy and other efficient optimization algorithms David Keil Analysis of Algorithms 7/14 1David. Greedy algorithms 8/14 #12;5. Greedy and other efficient optimization algorithms David Keil Analysis outcomes David Keil Analysis of Algorithms 5. Greedy algorithms 8/14 #12;5. Greedy and other efficient
D-H Kim; J-M Kim; S-H Hwang; H-S Kim
2007-01-01
Vehicle stability control logic for a four-wheel-drive hybrid electric vehicle is proposed using the regenerative braking of the rear motor and an electrohydraulic brake (EHB). To obtain the optimal brake torque distribution between the regenerative braking and the EHB torque, a genetic algorithm is used. The genetic algorithm calculates the optimal regenerative braking torque and the optimal EHB torque for
Madhubanti Maitra; Amitava Chatterjee
2008-01-01
A novel optimal multilevel thresholding algorithm for histogram-based image segmentation is presented in this paper. The proposed algorithm presents an improved variant of PSO, a relatively recently introduced stochastic optimization strategy. This hybrid approach employs both cooperative learning and comprehensive learning along with some additional modifications. Cooperative learning is employed to overcome the “curse of dimensionality” by decomposing a high-dimensional
Time series prediction with recurrent neural networks using a hybrid PSO-EA algorithm
Xindi Cai; Nian Zhang; Ganesh K. Venayagamoorthy; Donald C. Wunsch I
2004-01-01
To predict the 100 missing values from the time series consisting of 5000 data given for the IJCNN 2004 time series prediction competition, we applied an architecture which automates the design of recurrent neural networks using a new evolutionary learning algorithm. This new evolutionary learning algorithm is based on a hybrid of particle swarm optimization (PSO) and evolutionary algorithm (EA).
Size optimization of space trusses using Big Bang–Big Crunch algorithm
A. Kaveh; S. Talatahari
2009-01-01
A Hybrid Big Bang–Big Crunch (HBB–BC) optimization algorithm is employed for optimal design of truss structures. HBB–BC is compared to Big Bang–Big Crunch (BB–BC) method and other optimization methods including Genetic Algorithm, Ant Colony Optimization, Particle Swarm Optimization and Harmony Search. Numerical results demonstrate the efficiency and robustness of the HBB–BC method compared to other heuristic algorithms.
Multilevel algorithms for nonlinear optimization
NASA Technical Reports Server (NTRS)
Alexandrov, Natalia; Dennis, J. E., Jr.
1994-01-01
Multidisciplinary design optimization (MDO) gives rise to nonlinear optimization problems characterized by a large number of constraints that naturally occur in blocks. We propose a class of multilevel optimization methods motivated by the structure and number of constraints and by the expense of the derivative computations for MDO. The algorithms are an extension to the nonlinear programming problem of the successful class of local Brown-Brent algorithms for nonlinear equations. Our extensions allow the user to partition constraints into arbitrary blocks to fit the application, and they separately process each block and the objective function, restricted to certain subspaces. The methods use trust regions as a globalization strategy, and they have been shown to be globally convergent under reasonable assumptions. The multilevel algorithms can be applied to all classes of MDO formulations. Multilevel algorithms for solving nonlinear systems of equations are a special case of the multilevel optimization methods. In this case, they can be viewed as a trust-region globalization of the Brown-Brent class.
Discrete and Hybrid Stochastic State Estimation Algorithms
Murray, Richard M.
Discrete and Hybrid Stochastic State Estimation Algorithms for Networked Control Systems S. Di of Technology murray@caltech.edu Abstract. Networked control systems enable for flexible systems oper- ation, which are modelled as stochastic events that depend on the cur- rent state of the network. To design
Discrete and Hybrid Stochastic State Estimation Algorithms
Johansson, Karl Henrik
Discrete and Hybrid Stochastic State Estimation Algorithms for Networked Control Systems S. Di Institute of Technology murray@caltech.edu Abstract. Networked control systems enable for flexible systems of packet drops, which are modelled as stochastic events that depend on the cur- rent state of the network
Hybrid genetic algorithms for polypeptide energy minimization
Laurence D. Merkle; Robert L. Gaulke; Gary B. Lamont; George H. Gates Jr.; Ruth Pachter
1996-01-01
Efforts to predict polypeptide structures nearly always assume that the native conformation corresponds to the global minimum free energy state of the system. Given this assumption, a necessary step in solving the problem is the development of efficient global energy minimization techniques. We describe a hybrid genetic algorithm which incorporates efficient gradient-based minimization directly in the fitness evaluation, which is
Firefly Algorithm, Lévy Flights and Global Optimization
NASA Astrophysics Data System (ADS)
Yang, Xin-She
Nature-inspired algorithms such as Particle Swarm Optimization and Firefly Algorithm are among the most powerful algorithms for optimization. In this paper, we intend to formulate a new metaheuristic algorithm by combining Lévy flights with the search strategy via the Firefly Algorithm. Numerical studies and results suggest that the proposed Lévy-flight firefly algorithm is superior to existing metaheuristic algorithms. Finally implications for further research and wider applications will be discussed.
BP neural network optimized with PSO algorithm and its application in forecasting
Wen Guo; Yizheng Qiao; Haiyan Hou
2006-01-01
An approach that neural network optimized with PSO algorithm is proposed in the paper. Unlike conventional training method with gradient descent method only, this paper introduces a hybrid training algorithm by combining the PSO and BP algorithm. The PSO is used to optimize the initial parameters of the BP neural network, including the weights and biases. It can effectively better
Optimal Control of Hybrid Systems in Air Traffic Applications
NASA Astrophysics Data System (ADS)
Kamgarpour, Maryam
Growing concerns over the scalability of air traffic operations, air transportation fuel emissions and prices, as well as the advent of communication and sensing technologies motivate improvements to the air traffic management system. To address such improvements, in this thesis a hybrid dynamical model as an abstraction of the air traffic system is considered. Wind and hazardous weather impacts are included using a stochastic model. This thesis focuses on the design of algorithms for verification and control of hybrid and stochastic dynamical systems and the application of these algorithms to air traffic management problems. In the deterministic setting, a numerically efficient algorithm for optimal control of hybrid systems is proposed based on extensions of classical optimal control techniques. This algorithm is applied to optimize the trajectory of an Airbus 320 aircraft in the presence of wind and storms. In the stochastic setting, the verification problem of reaching a target set while avoiding obstacles (reach-avoid) is formulated as a two-player game to account for external agents' influence on system dynamics. The solution approach is applied to air traffic conflict prediction in the presence of stochastic wind. Due to the uncertainty in forecasts of the hazardous weather, and hence the unsafe regions of airspace for aircraft flight, the reach-avoid framework is extended to account for stochastic target and safe sets. This methodology is used to maximize the probability of the safety of aircraft paths through hazardous weather. Finally, the problem of modeling and optimization of arrival air traffic and runway configuration in dense airspace subject to stochastic weather data is addressed. This problem is formulated as a hybrid optimal control problem and is solved with a hierarchical approach that decouples safety and performance. As illustrated with this problem, the large scale of air traffic operations motivates future work on the efficient implementation of the proposed algorithms.
M. Senthil Arumugam; M. V. C. Rao
2008-01-01
This paper deals with the concept of including the popular genetic algorithm operator, cross-over and root mean square (RMS) variants into particle swarm optimization (PSO) algorithm to make the convergence faster. Two different PSO algorithms are considered in this paper: the first one is the conventional PSO (cPSO) and the second is the global-local best values based PSO (GLbest-PSO). The
The theory of variational hybrid quantum-classical algorithms
McClean, Jarrod R; Babbush, Ryan; Aspuru-Guzik, Alán
2015-01-01
Many quantum algorithms have daunting resource requirements when compared to what is available today. To address this discrepancy, a quantum-classical hybrid optimization scheme known as "the quantum variational eigensolver" was developed with the philosophy that even minimal quantum resources could be made useful when used in conjunction with classical routines. In this work we extend the general theory of this algorithm and suggest algorithmic improvements for practical implementations. Specifically, we develop a variational adiabatic ansatz and explore unitary coupled cluster where we establish a connection from second order unitary coupled cluster to universal gate sets through relaxation of exponential splitting. We introduce the concept of quantum variational error suppression that allows some errors to be suppressed naturally in this algorithm on a pre-threshold quantum device. Additionally, we analyze truncation and correlated sampling in Hamiltonian averaging as ways to reduce the cost of this proced...
Ying Chen; Qiguang Zhu; Zhiquan Li
2007-01-01
An artificial neural network (ANN) based on hybrid algorithm combining particle swarm optimization (PSO) algorithm with back-propagation\\u000a (BP) algorithm has been introduced to compensate the polarization mode dispersion (PMD) in the ultra-high speed optical communication\\u000a system. The hybrid algorithm, also referred to as PSO-BP algorithm, has been adopted to train the weights of ANN, and it can\\u000a make use of
A New Optimized GA-RBF Neural Network Algorithm
Zhao, Dean; Su, Chunyang; Hu, Chanli; Zhao, Yuyan
2014-01-01
When confronting the complex problems, radial basis function (RBF) neural network has the advantages of adaptive and self-learning ability, but it is difficult to determine the number of hidden layer neurons, and the weights learning ability from hidden layer to the output layer is low; these deficiencies easily lead to decreasing learning ability and recognition precision. Aiming at this problem, we propose a new optimized RBF neural network algorithm based on genetic algorithm (GA-RBF algorithm), which uses genetic algorithm to optimize the weights and structure of RBF neural network; it chooses new ways of hybrid encoding and optimizing simultaneously. Using the binary encoding encodes the number of the hidden layer's neurons and using real encoding encodes the connection weights. Hidden layer neurons number and connection weights are optimized simultaneously in the new algorithm. However, the connection weights optimization is not complete; we need to use least mean square (LMS) algorithm for further leaning, and finally get a new algorithm model. Using two UCI standard data sets to test the new algorithm, the results show that the new algorithm improves the operating efficiency in dealing with complex problems and also improves the recognition precision, which proves that the new algorithm is valid. PMID:25371666
End of semester project Global Optimization algorithms
Dreyfuss, Pierre
End of semester project Global Optimization algorithms Ecole Polytechnique de l'UniversitÃ© de Nice.......................................................................................................................................3 II. Simulated annealing algorithm (SA.........................................................................................................................................7 2.Principle,algorithm and choice of parameters
Available Transfer Capability Determination Using Hybrid Evolutionary Algorithm
NASA Astrophysics Data System (ADS)
Jirapong, Peeraool; Ongsakul, Weerakorn
2008-10-01
This paper proposes a new hybrid evolutionary algorithm (HEA) based on evolutionary programming (EP), tabu search (TS), and simulated annealing (SA) to determine the available transfer capability (ATC) of power transactions between different control areas in deregulated power systems. The optimal power flow (OPF)-based ATC determination is used to evaluate the feasible maximum ATC value within real and reactive power generation limits, line thermal limits, voltage limits, and voltage and angle stability limits. The HEA approach simultaneously searches for real power generations except slack bus in a source area, real power loads in a sink area, and generation bus voltages to solve the OPF-based ATC problem. Test results on the modified IEEE 24-bus reliability test system (RTS) indicate that ATC determination by the HEA could enhance ATC far more than those from EP, TS, hybrid TS/SA, and improved EP (IEP) algorithms, leading to an efficient utilization of the existing transmission system.
Time series prediction with recurrent neural networks trained by a hybrid PSO-EA algorithm
Xindi Cai; Nian Zhang; Ganesh K. Venayagamoorthy; Donald C. Wunsch II
2007-01-01
Abstract To predict the 100 missing values from a time series of 5000 data points, given for the IJCNN 2004 time series prediction competition, recurrent neural networks,(RNNs) are trained with a new,learning algorithm. This training algorithm,is based on a hybrid of particle swarm,optimization,(PSO) and,evolutionary,algorithm,(EA). By combining,the searching,abilities of these two,global optimization methods, the evolution of individuals is no longer restricted
Optimal Power Train Design of a Hybrid Refuse Collector Vehicle
Noé, Reinhold
Optimal Power Train Design of a Hybrid Refuse Collector Vehicle Tobias Knoke, Joachim Böcker components compared to a heuristically designed vehicle. Keywords: Hybrid Electric Vehicle (HEV data are given. III. HYBRID REFUSE COLLECTOR VEHICLE Design of the hybrid collector vehicle
A HYBRID ALGORITHM FOR SPECTRAL ANALYSIS J. FEDERICO RAMREZ and OLAC FUENTES
Fuentes, Olac
A HYBRID ALGORITHM FOR SPECTRAL ANALYSIS J. FEDERICO RAMÍREZ and OLAC FUENTES Instituto Nacional de;2 J. F. RAM´IREZ AND O. FUENTES Figure 2. The hybrid system. Figure 3. Representation of the object successfully to optimization problems in poorly-modelled domains and in the pres- ence of noisy data (Fuentes
Hybrid intelligent algorithms for industrial production planning
NASA Astrophysics Data System (ADS)
Vasant, P.
2012-11-01
In this paper, the main significant contributions of a new non-linear membership function using fuzzy approach to capture and describe vagueness in the technological coefficients of constraints in the industrial production planning problems has been investigated thoroughly. This non-linear membership function is flexible and convenience to the decision makers in their decision making process. Secondly, a nonlinear objective function in the form of cubic function for fuzzy optimization problems is successfully solved by 15 hybrid and non-hybrid optimization techniques from the area of soft computing and classical approaches. An intelligent performance analysis table is tabulated to the convenience of decision makers and implementers to select the niche optimization techniques to apply in real word problem solving approach particularly related to industrial engineering problems.
A Hybrid Ant Colony Optimization and Its Application to Vehicle Routing Problem with Time Windows
Xiangpei Hu; Qiulei Ding; Yunzeng Wang
\\u000a The Ant Colony Optimization (ACO) is a recent meta-heuristic algorithm for solving hard combinatorial optimization problems.\\u000a The algorithm, however, has the weaknesses of premature convergence and low search speed, which greatly hinder its application.\\u000a In order to improve the performance of the algorithm, a hybrid ant colony optimization (HACO) is presented by adjusting pheromone\\u000a approach, introducing a disaster operator, and
Progress in design optimization using evolutionary algorithms for aerodynamic problems
NASA Astrophysics Data System (ADS)
Lian, Yongsheng; Oyama, Akira; Liou, Meng-Sing
2010-07-01
Evolutionary algorithms (EAs) are useful tools in design optimization. Due to their simplicity, ease of use, and suitability for multi-objective design optimization problems, EAs have been applied to design optimization problems from various areas. In this paper we review the recent progress in design optimization using evolutionary algorithms to solve real-world aerodynamic problems. Examples are given in the design of turbo pump, compressor, and micro-air vehicles. The paper covers the following topics that are deemed important to solve a large optimization problem from a practical viewpoint: (1) hybridized approaches to speed up the convergence rate of EAs; (2) the use of surrogate model to reduce the computational cost stemmed from EAs; (3) reliability based design optimization using EAs; and (4) data mining of Pareto-optimal solutions.
Genetic Algorithm for Optimization: Preprocessor and Algorithm
NASA Technical Reports Server (NTRS)
Sen, S. K.; Shaykhian, Gholam A.
2006-01-01
Genetic algorithm (GA) inspired by Darwin's theory of evolution and employed to solve optimization problems - unconstrained or constrained - uses an evolutionary process. A GA has several parameters such the population size, search space, crossover and mutation probabilities, and fitness criterion. These parameters are not universally known/determined a priori for all problems. Depending on the problem at hand, these parameters need to be decided such that the resulting GA performs the best. We present here a preprocessor that achieves just that, i.e., it determines, for a specified problem, the foregoing parameters so that the consequent GA is a best for the problem. We stress also the need for such a preprocessor both for quality (error) and for cost (complexity) to produce the solution. The preprocessor includes, as its first step, making use of all the information such as that of nature/character of the function/system, search space, physical/laboratory experimentation (if already done/available), and the physical environment. It also includes the information that can be generated through any means - deterministic/nondeterministic/graphics. Instead of attempting a solution of the problem straightway through a GA without having/using the information/knowledge of the character of the system, we would do consciously a much better job of producing a solution by using the information generated/created in the very first step of the preprocessor. We, therefore, unstintingly advocate the use of a preprocessor to solve a real-world optimization problem including NP-complete ones before using the statistically most appropriate GA. We also include such a GA for unconstrained function optimization problems.
Two-stage hybrid optimization of fiber Bragg gratings for design of linear phase filters
NASA Astrophysics Data System (ADS)
Zheng, Rui Tao; Ngo, Nam Quoc; Binh, Le Nguyen; Tjin, Swee Chuan
2004-12-01
We present a new hybrid optimization method for the synthesis of fiber Bragg gratings (FBGs) with complex characteristics. The hybrid optimization method is a two-tier search that employs a global optimization algorithm [i.e., the tabu search (TS) algorithm] and a local optimization method (i.e., the quasi-Netwon method). First the TS global optimization algorithm is used to find a ``promising'' FBG structure that has a spectral response as close as possible to the targeted spectral response. Then the quasi-Newton local optimization method is applied to further optimize the FBG structure obtained from the TS algorithm to arrive at a targeted spectral response. A dynamic mechanism for weighting of different requirements of the spectral response is employed to enhance the optimization efficiency. To demonstrate the effectiveness of the method, the synthesis of three linear-phase optical filters based on FBGs with different grating lengths is described.
Coupled Low-thrust Trajectory and System Optimization via Multi-Objective Hybrid Optimal Control
NASA Technical Reports Server (NTRS)
Vavrina, Matthew A.; Englander, Jacob Aldo; Ghosh, Alexander R.
2015-01-01
The optimization of low-thrust trajectories is tightly coupled with the spacecraft hardware. Trading trajectory characteristics with system parameters ton identify viable solutions and determine mission sensitivities across discrete hardware configurations is labor intensive. Local independent optimization runs can sample the design space, but a global exploration that resolves the relationships between the system variables across multiple objectives enables a full mapping of the optimal solution space. A multi-objective, hybrid optimal control algorithm is formulated using a multi-objective genetic algorithm as an outer loop systems optimizer around a global trajectory optimizer. The coupled problem is solved simultaneously to generate Pareto-optimal solutions in a single execution. The automated approach is demonstrated on two boulder return missions.
Path planning using a hybrid evolutionary algorithm based on tree structure encoding.
Ju, Ming-Yi; Wang, Siao-En; Guo, Jian-Horn
2014-01-01
A hybrid evolutionary algorithm using scalable encoding method for path planning is proposed in this paper. The scalable representation is based on binary tree structure encoding. To solve the problem of hybrid genetic algorithm and particle swarm optimization, the "dummy node" is added into the binary trees to deal with the different lengths of representations. The experimental results show that the proposed hybrid method demonstrates using fewer turning points than traditional evolutionary algorithms to generate shorter collision-free paths for mobile robot navigation. PMID:24971389
Enhanced hybrid search algorithm for protein structure prediction using the 3D-HP lattice model.
Zhou, Changjun; Hou, Caixia; Zhang, Qiang; Wei, Xiaopeng
2013-09-01
The problem of protein structure prediction in the hydrophobic-polar (HP) lattice model is the prediction of protein tertiary structure. This problem is usually referred to as the protein folding problem. This paper presents a method for the application of an enhanced hybrid search algorithm to the problem of protein folding prediction, using the three dimensional (3D) HP lattice model. The enhanced hybrid search algorithm is a combination of the particle swarm optimizer (PSO) and tabu search (TS) algorithms. Since the PSO algorithm entraps local minimum in later evolution extremely easily, we combined PSO with the TS algorithm, which has properties of global optimization. Since the technologies of crossover and mutation are applied many times to PSO and TS algorithms, so enhanced hybrid search algorithm is called the MCMPSO-TS (multiple crossover and mutation PSO-TS) algorithm. Experimental results show that the MCMPSO-TS algorithm can find the best solutions so far for the listed benchmarks, which will help comparison with any future paper approach. Moreover, real protein sequences and Fibonacci sequences are verified in the 3D HP lattice model for the first time. Compared with the previous evolutionary algorithms, the new hybrid search algorithm is novel, and can be used effectively to predict 3D protein folding structure. With continuous development and changes in amino acids sequences, the new algorithm will also make a contribution to the study of new protein sequences. PMID:23824509
Efficient hybrid distributed genetic algorithms for wind turbine positioning in large wind farms
Hou-Sheng Huang
2009-01-01
An efficient hybrid distributed genetic algorithm is proposed to determine the proper number and locations of wind turbines in large wind farms. The objective of this optimal process is to find a solution that maximizes the annual profit obtained from a wind farm. It is well-known that genetic algorithms are good for global searches, but are weak for local searches.
A Hybrid Parallel Preconditioning Algorithm For CFD
NASA Technical Reports Server (NTRS)
Barth,Timothy J.; Tang, Wei-Pai; Kwak, Dochan (Technical Monitor)
1995-01-01
A new hybrid preconditioning algorithm will be presented which combines the favorable attributes of incomplete lower-upper (ILU) factorization with the favorable attributes of the approximate inverse method recently advocated by numerous researchers. The quality of the preconditioner is adjustable and can be increased at the cost of additional computation while at the same time the storage required is roughly constant and approximately equal to the storage required for the original matrix. In addition, the preconditioning algorithm suggests an efficient and natural parallel implementation with reduced communication. Sample calculations will be presented for the numerical solution of multi-dimensional advection-diffusion equations. The matrix solver has also been embedded into a Newton algorithm for solving the nonlinear Euler and Navier-Stokes equations governing compressible flow. The full paper will show numerous examples in CFD to demonstrate the efficiency and robustness of the method.
Three-dimension path planning for UCAV using hybrid meta-heuristic ACO-DE algorithm
Haibin Duan; Yaxiang Yu; Xiangyin Zhang; Shan Shao
2010-01-01
Three-dimension path planning of uninhabited combat air vehicle (UCAV) is a complicated optimal problem, which mainly focuses on optimizing the flight route considering the different types of constrains under complicated combating environments. A new hybrid meta-heuristic ant colony optimization (ACO) and differential evolution (DE) algorithm is proposed to solve the UCAV three-dimension path planning problem. DE is applied to optimize
Optimization of Wind Turbine Power Coefficient Parameters using Hybrid Technique
NASA Astrophysics Data System (ADS)
Rajakumar, S.; Ravindran, D.
2012-06-01
Wind turbine is a device that is used for converting kinetic energy from the wind into mechanical energy. The efficiency of wind turbine mainly depends on power coefficient of wind turbine. Maximization of power coefficient is one of the important factors for increasing efficiency in wind turbine. The maximized power coefficient enables high power production at low costs. The power coefficient is maximized by selecting suitable the values of design parameters. In this work a hybrid technique is proposed to optimize the power coefficient parameters of wind turbine blades. The proposed technique is a combination of genetic algorithm and artificial neural network (ANN). Genetic Algorithm is one of the evolutionary programs and it is used to optimize the parameters of power coefficient. The proposed genetic algorithm performs optimization in two phases. Initially, power coefficient parameters are determined for the respective angle of attack and optimized by using genetic algorithm phase I. ANN is used to generate the training data of design parameters of wind turbine. From the training data set, the best power coefficient parameters are optimized by executing phase II of the genetic algorithm. The proposed method is evaluated and its performances are identified.
DERIVATIVE-FREE OPTIMIZATION Algorithms, software and
Grossmann, Ignacio E.
1 DERIVATIVE-FREE OPTIMIZATION Algorithms, software and applications Nick Sahinidis National Energy) With: global optimality, provided search is "dense" #12;12 DERIVATIVE-FREE OPTIMIZATION SOFTWARE LOCAL@cmu.edu Acknowledgments: Luis Miguel Rios NIH and DOE/NETL #12;2 DERIVATIVE-FREE OPTIMIZATION · Optimization of a function
An efficient algorithm for function optimization: modified stem cells algorithm
NASA Astrophysics Data System (ADS)
Taherdangkoo, Mohammad; Paziresh, Mahsa; Yazdi, Mehran; Bagheri, Mohammad Hadi
2013-03-01
In this paper, we propose an optimization algorithm based on the intelligent behavior of stem cell swarms in reproduction and self-organization. Optimization algorithms, such as the Genetic Algorithm (GA), Particle Swarm Optimization (PSO) algorithm, Ant Colony Optimization (ACO) algorithm and Artificial Bee Colony (ABC) algorithm, can give solutions to linear and non-linear problems near to the optimum for many applications; however, in some case, they can suffer from becoming trapped in local optima. The Stem Cells Algorithm (SCA) is an optimization algorithm inspired by the natural behavior of stem cells in evolving themselves into new and improved cells. The SCA avoids the local optima problem successfully. In this paper, we have made small changes in the implementation of this algorithm to obtain improved performance over previous versions. Using a series of benchmark functions, we assess the performance of the proposed algorithm and compare it with that of the other aforementioned optimization algorithms. The obtained results prove the superiority of the Modified Stem Cells Algorithm (MSCA).
Hybrid Evolutionary-Heuristic Algorithm for Capacitor Banks Allocation
NASA Astrophysics Data System (ADS)
Baruk?i?, Marinko; Nikolovski, Srete; Jovi?, Franjo
2010-11-01
The issue of optimal allocation of capacitor banks concerning power losses minimization in distribution networks are considered in this paper. This optimization problem has been recently tackled by application of contemporary soft computing methods such as: genetic algorithms, neural networks, fuzzy logic, simulated annealing, ant colony methods, and hybrid methods. An evolutionaryheuristic method has been proposed for optimal capacitor allocation in radial distribution networks. An evolutionary method based on genetic algorithm is developed. The proposed method has a reduced number of parameters compared to the usual genetic algorithm. A heuristic stage is used for improving the optimal solution given by the evolutionary stage. A new cost-voltage node index is used in the heuristic stage in order to improve the quality of solution. The efficiency of the proposed two-stage method has been tested on different test networks. The quality of solution has been verified by comparison tests with other methods on the same test networks. The proposed method has given significantly better solutions for time dependent load in the 69-bus network than found in references.
NASA Astrophysics Data System (ADS)
Verma, Harish Kumar; Jain, Cheshta
2015-07-01
In this article, a hybrid algorithm of particle swarm optimization (PSO) with statistical parameter (HSPSO) is proposed. Basic PSO for shifted multimodal problems have low searching precision due to falling into a number of local minima. The proposed approach uses statistical characteristics to update the velocity of the particle to avoid local minima and help particles to search global optimum with improved convergence. The performance of the newly developed algorithm is verified using various standard multimodal, multivariable, shifted hybrid composition benchmark problems. Further, the comparative analysis of HSPSO with variants of PSO is tested to control frequency of hybrid renewable energy system which comprises solar system, wind system, diesel generator, aqua electrolyzer and ultra capacitor. A significant improvement in convergence characteristic of HSPSO algorithm over other variants of PSO is observed in solving benchmark optimization and renewable hybrid system problems.
NASA Astrophysics Data System (ADS)
Sheng, Zheng; Wang, Jun; Zhou, Shudao; Zhou, Bihua
2014-03-01
This paper introduces a novel hybrid optimization algorithm to establish the parameters of chaotic systems. In order to deal with the weaknesses of the traditional cuckoo search algorithm, the proposed adaptive cuckoo search with simulated annealing algorithm is presented, which incorporates the adaptive parameters adjusting operation and the simulated annealing operation in the cuckoo search algorithm. Normally, the parameters of the cuckoo search algorithm are kept constant that may result in decreasing the efficiency of the algorithm. For the purpose of balancing and enhancing the accuracy and convergence rate of the cuckoo search algorithm, the adaptive operation is presented to tune the parameters properly. Besides, the local search capability of cuckoo search algorithm is relatively weak that may decrease the quality of optimization. So the simulated annealing operation is merged into the cuckoo search algorithm to enhance the local search ability and improve the accuracy and reliability of the results. The functionality of the proposed hybrid algorithm is investigated through the Lorenz chaotic system under the noiseless and noise condition, respectively. The numerical results demonstrate that the method can estimate parameters efficiently and accurately in the noiseless and noise condition. Finally, the results are compared with the traditional cuckoo search algorithm, genetic algorithm, and particle swarm optimization algorithm. Simulation results demonstrate the effectiveness and superior performance of the proposed algorithm.
Sheng, Zheng; Wang, Jun; Zhou, Bihua; Zhou, Shudao; Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science and Technology, Nanjing 210044
2014-03-15
This paper introduces a novel hybrid optimization algorithm to establish the parameters of chaotic systems. In order to deal with the weaknesses of the traditional cuckoo search algorithm, the proposed adaptive cuckoo search with simulated annealing algorithm is presented, which incorporates the adaptive parameters adjusting operation and the simulated annealing operation in the cuckoo search algorithm. Normally, the parameters of the cuckoo search algorithm are kept constant that may result in decreasing the efficiency of the algorithm. For the purpose of balancing and enhancing the accuracy and convergence rate of the cuckoo search algorithm, the adaptive operation is presented to tune the parameters properly. Besides, the local search capability of cuckoo search algorithm is relatively weak that may decrease the quality of optimization. So the simulated annealing operation is merged into the cuckoo search algorithm to enhance the local search ability and improve the accuracy and reliability of the results. The functionality of the proposed hybrid algorithm is investigated through the Lorenz chaotic system under the noiseless and noise condition, respectively. The numerical results demonstrate that the method can estimate parameters efficiently and accurately in the noiseless and noise condition. Finally, the results are compared with the traditional cuckoo search algorithm, genetic algorithm, and particle swarm optimization algorithm. Simulation results demonstrate the effectiveness and superior performance of the proposed algorithm.
Sheng, Zheng; Wang, Jun; Zhou, Shudao; Zhou, Bihua
2014-03-01
This paper introduces a novel hybrid optimization algorithm to establish the parameters of chaotic systems. In order to deal with the weaknesses of the traditional cuckoo search algorithm, the proposed adaptive cuckoo search with simulated annealing algorithm is presented, which incorporates the adaptive parameters adjusting operation and the simulated annealing operation in the cuckoo search algorithm. Normally, the parameters of the cuckoo search algorithm are kept constant that may result in decreasing the efficiency of the algorithm. For the purpose of balancing and enhancing the accuracy and convergence rate of the cuckoo search algorithm, the adaptive operation is presented to tune the parameters properly. Besides, the local search capability of cuckoo search algorithm is relatively weak that may decrease the quality of optimization. So the simulated annealing operation is merged into the cuckoo search algorithm to enhance the local search ability and improve the accuracy and reliability of the results. The functionality of the proposed hybrid algorithm is investigated through the Lorenz chaotic system under the noiseless and noise condition, respectively. The numerical results demonstrate that the method can estimate parameters efficiently and accurately in the noiseless and noise condition. Finally, the results are compared with the traditional cuckoo search algorithm, genetic algorithm, and particle swarm optimization algorithm. Simulation results demonstrate the effectiveness and superior performance of the proposed algorithm. PMID:24697395
A HYBRID D3 -SIGMA DELTA STAP ALGORITHM IN NON-
Adve, Raviraj
A HYBRID D3 -SIGMA DELTA STAP ALGORITHM IN NON- HOMOGENEOUS CLUTTER Eunjung Yang*, Joowhan Chun-gu, Yongin-city, Gyeonggi-do 446-885, Korea twinbear@hanafos.com Keywords: D3 algorithm, Sigma-Delta STAP. In the hybrid algorithm, statistical and non-statistical direct data domain (D3 ) algo- rithms are combined
SPAM: Set Preference Algorithm for Multiobjective Optimization
Zitzler, Eckart
SPAM: Set Preference Algorithm for Multiobjective Optimization Eckart Zitzler, Lothar Thiele to arbitrary user preferences-- assuming that the goal is to approximate the Pareto-optimal set. It proposes the Set Preference Algorithm for Multiobjective Optimization (SPAM) the working principle of which
Optimizing hybrid spreading in metapopulations.
Zhang, Changwang; Zhou, Shi; Miller, Joel C; Cox, Ingemar J; Chain, Benjamin M
2015-01-01
Epidemic spreading phenomena are ubiquitous in nature and society. Examples include the spreading of diseases, information, and computer viruses. Epidemics can spread by local spreading, where infected nodes can only infect a limited set of direct target nodes and global spreading, where an infected node can infect every other node. In reality, many epidemics spread using a hybrid mixture of both types of spreading. In this study we develop a theoretical framework for studying hybrid epidemics, and examine the optimum balance between spreading mechanisms in terms of achieving the maximum outbreak size. We show the existence of critically hybrid epidemics where neither spreading mechanism alone can cause a noticeable spread but a combination of the two spreading mechanisms would produce an enormous outbreak. Our results provide new strategies for maximising beneficial epidemics and estimating the worst outcome of damaging hybrid epidemics. PMID:25923411
Global optimization of hybrid systems
Lee, Cha Kun
2006-01-01
Systems that exhibit both discrete state and continuous state dynamics are called hybrid systems. In most nontrivial cases, these two aspects of system behavior interact to such a significant extent that they cannot be ...
Hybrid ECAL: Optimization and Related Developments
Suehara, T; Sumida, H; Ueno, H; Sudo, Y; Yoshioka, T; Kawagoe, K
2015-01-01
Hybrid ECAL is a cost-conscious option of electromagnetic calorimeter (ECAL) for particle flow calorimetry to be used in a detector of International Linear Collider (ILC). It is a combination of silicon-tungsten ECAL, which realizes high granularity and robust measurement of electromagnetic shower, and scintillator-tungsten ECAL, which gives affordable cost with similar performance to silicon. Optimization and a data acquisition trial in a test bench for the hybrid ECAL are described in this article.
Hybrid ECAL: Optimization and Related Developments
T. Suehara; H. Hirai; H. Sumida; H. Ueno; Y. Sudo; T. Yoshioka; K. Kawagoe
2015-03-30
Hybrid ECAL is a cost-conscious option of electromagnetic calorimeter (ECAL) for particle flow calorimetry to be used in a detector of International Linear Collider (ILC). It is a combination of silicon-tungsten ECAL, which realizes high granularity and robust measurement of electromagnetic shower, and scintillator-tungsten ECAL, which gives affordable cost with similar performance to silicon. Optimization and a data acquisition trial in a test bench for the hybrid ECAL are described in this article.
NASA Astrophysics Data System (ADS)
Izui, K.; Nishiwaki, S.; Yoshimura, M.
2007-12-01
Swarm algorithms such as particle swarm optimization (PSO) are non-gradient probabilistic optimization algorithms that have been successfully applied for global searches in complex problems such as multi-peak problems. However, application of these algorithms to structural and mechanical optimization problems still remains a complex matter since local optimization capability is still inferior to general numerical optimization methods. This article discusses new swarm metaphors that incorporate design sensitivities concerning objective and constraint functions and are applicable to structural and mechanical design optimization problems. Single- and multi-objective optimization techniques using swarm algorithms are combined with a gradient-based method. In the proposed techniques, swarm optimization algorithms and a sequential linear programming (SLP) method are conducted simultaneously. Finally, truss structure design optimization problems are solved by the proposed hybrid method to verify the optimization efficiency.
Simulated annealing algorithm for optimal capital growth
NASA Astrophysics Data System (ADS)
Luo, Yong; Zhu, Bo; Tang, Yong
2014-08-01
We investigate the problem of dynamic optimal capital growth of a portfolio. A general framework that one strives to maximize the expected logarithm utility of long term growth rate was developed. Exact optimization algorithms run into difficulties in this framework and this motivates the investigation of applying simulated annealing optimized algorithm to optimize the capital growth of a given portfolio. Empirical results with real financial data indicate that the approach is inspiring for capital growth portfolio.
An improved GA and a novel PSO-GA-based hybrid algorithm
X. H. Shi; Y. C. Liang; H. P. Lee; C. Lu; L. M. Wang
2005-01-01
Inspired by the natural features of the variable size of the population, we present a variable population-size genetic algorithm (VPGA) by introducing the “dying probability” for the individuals and the “war\\/disease process” for the population. Based on the VPGA and the particle swarm optimization (PSO) algorithms, a novel PSO-GA-based hybrid algorithm (PGHA) is also proposed in this paper. Simulation results
Ensemble of hybrid genetic algorithm for two-dimensional phase unwrapping
NASA Astrophysics Data System (ADS)
Balakrishnan, D.; Quan, C.; Tay, C. J.
2013-06-01
The phase unwrapping is the final and trickiest step in any phase retrieval technique. Phase unwrapping by artificial intelligence methods (optimization algorithms) such as hybrid genetic algorithm, reverse simulated annealing, particle swarm optimization, minimum cost matching showed better results than conventional phase unwrapping methods. In this paper, Ensemble of hybrid genetic algorithm with parallel populations is proposed to solve the branch-cut phase unwrapping problem. In a single populated hybrid genetic algorithm, the selection, cross-over and mutation operators are applied to obtain new population in every generation. The parameters and choice of operators will affect the performance of the hybrid genetic algorithm. The ensemble of hybrid genetic algorithm will facilitate to have different parameters set and different choice of operators simultaneously. Each population will use different set of parameters and the offspring of each population will compete against the offspring of all other populations, which use different set of parameters. The effectiveness of proposed algorithm is demonstrated by phase unwrapping examples and advantages of the proposed method are discussed.
Improved Clonal Selection Algorithm Combined with Ant Colony Optimization
NASA Astrophysics Data System (ADS)
Gao, Shangce; Wang, Wei; Dai, Hongwei; Li, Fangjia; Tang, Zheng
Both the clonal selection algorithm (CSA) and the ant colony optimization (ACO) are inspired by natural phenomena and are effective tools for solving complex problems. CSA can exploit and explore the solution space parallely and effectively. However, it can not use enough environment feedback information and thus has to do a large redundancy repeat during search. On the other hand, ACO is based on the concept of indirect cooperative foraging process via secreting pheromones. Its positive feedback ability is nice but its convergence speed is slow because of the little initial pheromones. In this paper, we propose a pheromone-linker to combine these two algorithms. The proposed hybrid clonal selection and ant colony optimization (CSA-ACO) reasonably utilizes the superiorities of both algorithms and also overcomes their inherent disadvantages. Simulation results based on the traveling salesman problems have demonstrated the merit of the proposed algorithm over some traditional techniques.
Optimal Newton-type algorithms for nonconvex smooth optimization
Sidorov, Nikita
Optimal Newton-type algorithms for nonconvex smooth optimization Coralia Cartis Mathematical-HPC Workshop on New Directions in Nonlinear Optimization University of Manchester, School of Mathematics, March 19, 2014 New Directions in Nonlinear Optimization: Manchester, 2014 Â p. 1/33 #12;Unconstrained
A hybrid neural-genetic multimodel parameter estimation algorithm
Vassilios Petridis; Emmanuel Paterakis; Athanasios Kehagias
1998-01-01
We introduce a hybrid neural-genetic multimodel parameter estimation algorithm. The algorithm is applied to structured system identification of nonlinear dynamical systems. The main components of the algorithm are: 1) a recurrent incremental credit assignment neural network which computes a credit function for each member of a generation of models; and 2) a genetic algorithm which uses the credit functions as
Optimal Control of Switched Hybrid Systems: A Brief Survey
Antsaklis, Panos
Optimal Control of Switched Hybrid Systems: A Brief Survey Technical Report of the ISIS Group;1 Optimal Control of Switched Hybrid Systems: A Brief Survey Feng Zhu and Panos J. Antsaklis Department a few. The problem of determining optimal control laws for hybrid systems and in particular for switched
Intelligent perturbation algorithms for space scheduling optimization
NASA Technical Reports Server (NTRS)
Kurtzman, Clifford R.
1991-01-01
Intelligent perturbation algorithms for space scheduling optimization are presented in the form of the viewgraphs. The following subject areas are covered: optimization of planning, scheduling, and manifesting; searching a discrete configuration space; heuristic algorithms used for optimization; use of heuristic methods on a sample scheduling problem; intelligent perturbation algorithms are iterative refinement techniques; properties of a good iterative search operator; dispatching examples of intelligent perturbation algorithm and perturbation operator attributes; scheduling implementations using intelligent perturbation algorithms; major advances in scheduling capabilities; the prototype ISF (industrial Space Facility) experiment scheduler; optimized schedule (max revenue); multi-variable optimization; Space Station design reference mission scheduling; ISF-TDRSS command scheduling demonstration; and example task - communications check.
Traffic sharing algorithms for hybrid mobile networks
NASA Technical Reports Server (NTRS)
Arcand, S.; Murthy, K. M. S.; Hafez, R.
1995-01-01
In a hybrid (terrestrial + satellite) mobile personal communications networks environment, a large size satellite footprint (supercell) overlays on a large number of smaller size, contiguous terrestrial cells. We assume that the users have either a terrestrial only single mode terminal (SMT) or a terrestrial/satellite dual mode terminal (DMT) and the ratio of DMT to the total terminals is defined gamma. It is assumed that the call assignments to and handovers between terrestrial cells and satellite supercells take place in a dynamic fashion when necessary. The objectives of this paper are twofold, (1) to propose and define a class of traffic sharing algorithms to manage terrestrial and satellite network resources efficiently by handling call handovers dynamically, and (2) to analyze and evaluate the algorithms by maximizing the traffic load handling capability (defined in erl/cell) over a wide range of terminal ratios (gamma) given an acceptable range of blocking probabilities. Two of the algorithms (G & S) in the proposed class perform extremely well for a wide range of gamma.
Communication-Efficient Algorithms for Statistical Optimization
McAuliffe, Jon
Communication-Efficient Algorithms for Statistical Optimization Yuchen Zhang1 John C. Duchi1 Martin We study two communication-efficient algorithms for distributed statistical op- timization on large and amount of data, a central challenge in machine learning is to design efficient algorithms for solving
Hybridizing Evolutionary Algorithms and Clustering Algorithms to Find Source-Code Clones
Maletic, Jonathan I.
Hybridizing Evolutionary Algorithms and Clustering Algorithms to Find Source-Code Clones Andrew a hybrid approach to detect source-code clones that combines evolutionary algorithms and clustering. A case is effective in detecting groups of source-code clones. Categories and Subject Descriptors D.2.7 [Software
Optimization methods applied to hybrid vehicle design
NASA Technical Reports Server (NTRS)
Donoghue, J. F.; Burghart, J. H.
1983-01-01
The use of optimization methods as an effective design tool in the design of hybrid vehicle propulsion systems is demonstrated. Optimization techniques were used to select values for three design parameters (battery weight, heat engine power rating and power split between the two on-board energy sources) such that various measures of vehicle performance (acquisition cost, life cycle cost and petroleum consumption) were optimized. The apporach produced designs which were often significant improvements over hybrid designs already reported on in the literature. The principal conclusions are as follows. First, it was found that the strategy used to split the required power between the two on-board energy sources can have a significant effect on life cycle cost and petroleum consumption. Second, the optimization program should be constructed so that performance measures and design variables can be easily changed. Third, the vehicle simulation program has a significant effect on the computer run time of the overall optimization program; run time can be significantly reduced by proper design of the types of trips the vehicle takes in a one year period. Fourth, care must be taken in designing the cost and constraint expressions which are used in the optimization so that they are relatively smooth functions of the design variables. Fifth, proper handling of constraints on battery weight and heat engine rating, variables which must be large enough to meet power demands, is particularly important for the success of an optimization study. Finally, the principal conclusion is that optimization methods provide a practical tool for carrying out the design of a hybrid vehicle propulsion system.
Tunneling hybrid Monte-Carlo algorithm
Golterman, Maarten [Department of Physics and Astronomy, San Francisco State University, San Francisco, California 94132 (United States); Shamir, Yigal [Raymond and Beverly Sackler School of Physics and Astronomy, Tel-Aviv University, Ramat Aviv, 69978 (Israel)
2007-11-01
The Hermitian Wilson kernel used in the construction of the domain-wall and overlap Dirac operators has exceptionally small eigenvalues that make it expensive to reach high-quality chiral symmetry for domain-wall fermions, or high precision in the case of the overlap operator. An efficient way of suppressing such eigenmodes consists of including a positive power of the determinant of the Wilson kernel in the Boltzmann weight, but doing this also suppresses tunneling between topological sectors. Here we propose a modification of the hybrid Monte-Carlo algorithm which aims to restore tunneling between topological sectors by excluding the lowest eigenmodes of the Wilson kernel from the molecular-dynamics evolution, and correcting for this at the accept/reject step. We discuss the implications of this modification for the acceptance rate.
Evaluating the Performance of Map Optimization Algorithms
Kaess, Michael
Evaluating the Performance of Map Optimization Algorithms Edwin Olson, University of Michigan Michael Kaess, MIT Abstract-- Localization and mapping are essential capabilities of virtually all mobile-problem of robot mapping, map optimization. We explore aspects underlying the evaluation of map optimization
Design of optimal correlation filters for hybrid vision systems
NASA Technical Reports Server (NTRS)
Rajan, Periasamy K.
1990-01-01
Research is underway at the NASA Johnson Space Center on the development of vision systems that recognize objects and estimate their position by processing their images. This is a crucial task in many space applications such as autonomous landing on Mars sites, satellite inspection and repair, and docking of space shuttle and space station. Currently available algorithms and hardware are too slow to be suitable for these tasks. Electronic digital hardware exhibits superior performance in computing and control; however, they take too much time to carry out important signal processing operations such as Fourier transformation of image data and calculation of correlation between two images. Fortunately, because of the inherent parallelism, optical devices can carry out these operations very fast, although they are not quite suitable for computation and control type operations. Hence, investigations are currently being conducted on the development of hybrid vision systems that utilize both optical techniques and digital processing jointly to carry out the object recognition tasks in real time. Algorithms for the design of optimal filters for use in hybrid vision systems were developed. Specifically, an algorithm was developed for the design of real-valued frequency plane correlation filters. Furthermore, research was also conducted on designing correlation filters optimal in the sense of providing maximum signal-to-nose ratio when noise is present in the detectors in the correlation plane. Algorithms were developed for the design of different types of optimal filters: complex filters, real-value filters, phase-only filters, ternary-valued filters, coupled filters. This report presents some of these algorithms in detail along with their derivations.
Malikopoulos, Andreas [ORNL
2015-01-01
The increasing urgency to extract additional efficiency from hybrid propulsion systems has led to the development of advanced power management control algorithms. In this paper we address the problem of online optimization of the supervisory power management control in parallel hybrid electric vehicles (HEVs). We model HEV operation as a controlled Markov chain and we show that the control policy yielding the Pareto optimal solution minimizes online the long-run expected average cost per unit time criterion. The effectiveness of the proposed solution is validated through simulation and compared to the solution derived with dynamic programming using the average cost criterion. Both solutions achieved the same cumulative fuel consumption demonstrating that the online Pareto control policy is an optimal control policy.
A comprehensive review of swarm optimization algorithms.
Ab Wahab, Mohd Nadhir; Nefti-Meziani, Samia; Atyabi, Adham
2015-01-01
Many swarm optimization algorithms have been introduced since the early 60's, Evolutionary Programming to the most recent, Grey Wolf Optimization. All of these algorithms have demonstrated their potential to solve many optimization problems. This paper provides an in-depth survey of well-known optimization algorithms. Selected algorithms are briefly explained and compared with each other comprehensively through experiments conducted using thirty well-known benchmark functions. Their advantages and disadvantages are also discussed. A number of statistical tests are then carried out to determine the significant performances. The results indicate the overall advantage of Differential Evolution (DE) and is closely followed by Particle Swarm Optimization (PSO), compared with other considered approaches. PMID:25992655
Full Glowworm Swarm Optimization Algorithm for Whole-Set Orders Scheduling in Single Machine
Yu, Zhang; Yang, Xiaomei
2013-01-01
By analyzing the characteristics of whole-set orders problem and combining the theory of glowworm swarm optimization, a new glowworm swarm optimization algorithm for scheduling is proposed. A new hybrid-encoding schema combining with two-dimensional encoding and random-key encoding is given. In order to enhance the capability of optimal searching and speed up the convergence rate, the dynamical changed step strategy is integrated into this algorithm. Furthermore, experimental results prove its feasibility and efficiency. PMID:24294135
Stochastic optimization algorithms for barrier dividend strategies
NASA Astrophysics Data System (ADS)
Yin, G.; Song, Q. S.; Yang, H.
2009-01-01
This work focuses on finding optimal barrier policy for an insurance risk model when the dividends are paid to the share holders according to a barrier strategy. A new approach based on stochastic optimization methods is developed. Compared with the existing results in the literature, more general surplus processes are considered. Precise models of the surplus need not be known; only noise-corrupted observations of the dividends are used. Using barrier-type strategies, a class of stochastic optimization algorithms are developed. Convergence of the algorithm is analyzed; rate of convergence is also provided. Numerical results are reported to demonstrate the performance of the algorithm.
Solving Fuzzy Optimization Problem Using Hybrid Ls-Sa Method
NASA Astrophysics Data System (ADS)
Vasant, Pandian
2011-06-01
Fuzzy optimization problem has been one of the most and prominent topics inside the broad area of computational intelligent. It's especially relevant in the filed of fuzzy non-linear programming. It's application as well as practical realization can been seen in all the real world problems. In this paper a large scale non-linear fuzzy programming problem has been solved by hybrid optimization techniques of Line Search (LS), Simulated Annealing (SA) and Pattern Search (PS). As industrial production planning problem with cubic objective function, 8 decision variables and 29 constraints has been solved successfully using LS-SA-PS hybrid optimization techniques. The computational results for the objective function respect to vagueness factor and level of satisfaction has been provided in the form of 2D and 3D plots. The outcome is very promising and strongly suggests that the hybrid LS-SA-PS algorithm is very efficient and productive in solving the large scale non-linear fuzzy programming problem.
A data locality optimizing algorithm
Monica S. Lam; Michael E. Wolf
2004-01-01
This paper proposes an algorithm that improves the locality of a loop nest by transforming the code via interchange, reversal, skewing and tiling. The loop transformation algorithm is based on two concepts: a mathematical formulation of reuse and locality, and a loop transformation theory that unifies the various transforms as unimodular matrix transformations.The algorithm has been implemented in the SUIF
An effective hybrid PSO-based algorithm for flow shop scheduling with limited buffers
Bo Liu; Ling Wang; Yi-Hui Jin
2008-01-01
In this paper, an effective hybrid algorithm based on particle swarm optimization (HPSO) is proposed for permutation flow shop scheduling problem (PFSSP) with the limited buffers between consecutive machines to minimize the maximum completion time (i.e., makespan). First, a novel encoding scheme based on random key representation is developed, which converts the continuous position values of particles in PSO to
Spacecraft long-duration phasing maneuver optimization using hybrid approach
NASA Astrophysics Data System (ADS)
Zhang, Jin; Wang, Xiang; Ma, Xiao-bing; Tang, Yi; Huang, Hai-bing
2012-03-01
Before a manned orbital rendezvous mission, the target spacecraft usually performs several maneuvers to adjust the initial phase angle of the orbital rendezvous and to coordinate the injection of the chaser. This maneuvering process is referred to as the "target phasing mission". This target phasing presents an orbital long-duration two-point boundary value problem. Further, when the maneuver revolution numbers are used as design variables, the target phasing maneuver's optimization becomes a mixed integer nonlinear programming problem. This paper presents a new optimization method for this phasing maneuver mission, employing a hybrid approach. First, we provide an approximate phasing optimization problem that considers the phase angle influences of node drift and orbital altitude decay. This problem is then optimized using a hybrid approach that integrates branch-and-bound and sequential quadratic programming. Second, a shooting iteration method is adopted to improve the solution to the approximate problem in order to satisfy the terminal constraints of high-precision numerical integration. The proposed method is then applied to an operational target phasing maneuver problem. The results lead to four major conclusions: (1) The proposed approximate phasing optimization model presents a good approximation of the operational mission. (2) The hybrid optimization approach can solve the approximate problem effectively, and the shooting iteration used to arrive at a high-precision solution converges steadily and rapidly. (3) Compared with mixed-code genetic algorithm, the proposed method can obtain a similar result with a lower computation cost and, compared with the approximate model that does not consider node drift and orbital altitude decay, the proposed method has better convergence efficiency. (4) The terminal time of target phasing remains almost constant when the initial semi-major axis increases in a limited interval, and the transition appears only when there is a change in the terminal revolution number.
Hybrid evolutionary algorithms based on PSO and GA
X. H. Shi; Y. H. Lu; C. G. Zhou; H. P. Lee; W. Z. Lin; Y. C. Liang
2003-01-01
Inspired by the idea of genetic algorithm, we propose two hybrid evolutionary algorithms based on PSO and GA methods through crossing over the PSO and GA algorithms. The main ideas of the two proposed methods are to integrate PSO and GA methods in parallel and series forms respectively. Simulations for a series of benchmark test functions show that both of
A Hybrid Intelligent Algorithm for Stochastic Dependent-Chance Programming
Xiao Ning; Zeng Jianchao
2008-01-01
The stochastic dependent-chance programming belongs to a class of stochastic programming problems, which has wide application backgrounds, in order to search an algorithm which can more effectively solve this problem, in the paper, stochastic simulation is used to produce training samples for BP neural networks, and a hybrid intelligent algorithm for stochastic dependent-chance programming combined PSO algorithm with BP neural
A Hybrid Fault-Tolerant Algorithm for MPLS Networks
Pitsillides, Andreas
A Hybrid Fault-Tolerant Algorithm for MPLS Networks Maria Hadjiona, Chryssis Georgiou, Maria Papa, path maintaining, algorithm for use in MPLS based networks. The novelty of the algorithm lies upon the fact that it is the first to employ both path restoration mechanisms typically used in MPLS networks
A Hybrid Fault-Tolerant Algorithm for MPLS Networks
Georgiou, Chryssis
A Hybrid Fault-Tolerant Algorithm for MPLS Networks Maria Hadjiona, Chryssis Georgiou, Maria Papa maintaining, algorithm for use in MPLS based networks. The novelty of the algorithm lies upon the fact that it is the first to employ both path restoration mechanisms typically used in MPLS networks: protection switching
HYBRID INTERVAL METHOD FOR GLOBAL OPTIMIZATION OF ENGINEERING STRUCTURES
Pownuk, Andrzej
HYBRID INTERVAL METHOD FOR GLOBAL OPTIMIZATION OF ENGINEERING STRUCTURES Andrzej Pownuk1 Abstract structures. Examples of optimization of truss structures with uncertain parameters was also presented. 1 The mathematical modelling of structural design optimization problem is as follows: ( ) ( ) ( ) == = eqi inj
Joint Algorithm-Architecture Optimization of CABAC
Sze, Vivienne
This paper uses joint algorithm and architecture design to enable high coding efficiency in conjunction with high processing speed and low area cost. Specifically, it presents several optimizations that can be performed ...
Aerodynamic Shape Optimization using an Evolutionary Algorithm
NASA Technical Reports Server (NTRS)
Holst, Terry L.; Pulliam, Thomas H.; Kwak, Dochan (Technical Monitor)
2003-01-01
A method for aerodynamic shape optimization based on an evolutionary algorithm approach is presented and demonstrated. Results are presented for a number of model problems to access the effect of algorithm parameters on convergence efficiency and reliability. A transonic viscous airfoil optimization problem, both single and two-objective variations, is used as the basis for a preliminary comparison with an adjoint-gradient optimizer. The evolutionary algorithm is coupled with a transonic full potential flow solver and is used to optimize the inviscid flow about transonic wings including multi-objective and multi-discipline solutions that lead to the generation of pareto fronts. The results indicate that the evolutionary algorithm approach is easy to implement, flexible in application and extremely reliable.
Aerodynamic Shape Optimization using an Evolutionary Algorithm
NASA Technical Reports Server (NTRS)
Hoist, Terry L.; Pulliam, Thomas H.
2003-01-01
A method for aerodynamic shape optimization based on an evolutionary algorithm approach is presented and demonstrated. Results are presented for a number of model problems to access the effect of algorithm parameters on convergence efficiency and reliability. A transonic viscous airfoil optimization problem-both single and two-objective variations is used as the basis for a preliminary comparison with an adjoint-gradient optimizer. The evolutionary algorithm is coupled with a transonic full potential flow solver and is used to optimize the inviscid flow about transonic wings including multi-objective and multi-discipline solutions that lead to the generation of pareto fronts. The results indicate that the evolutionary algorithm approach is easy to implement, flexible in application and extremely reliable.
Hybrid Simulated Annealing and Genetic Algorithms for Industrial Production Management Problems
NASA Astrophysics Data System (ADS)
Vasant, Pandian; Barsoum, Nader
2009-08-01
This paper describes the origin and significant contribution on the development of the Hybrid Simulated Annealing and Genetic Algorithms (HSAGA) approach for finding global optimization. HSAGA provide an insight approach to handle in solving complex optimization problems. The method is, the combination of meta-heuristic approaches of Simulated Annealing and novel Genetic Algorithms for solving a non-linear objective function with uncertain technical coefficients in an industrial production management problems. The proposed novel hybrid method is designed to search for global optimal for the non-linear objective function and search for the best feasible solutions of the decision variables. Simulated experiments were carried out rigorously to reflect the advantages of the proposed method. A description of the well developed method and the advanced computational experiment with MATLAB technical tool is presented. An industrial production management optimization problem is solved using HSAGA technique. The results are very much promising.
Adaptive Cuckoo Search Algorithm for Unconstrained Optimization
2014-01-01
Modification of the intensification and diversification approaches in the recently developed cuckoo search algorithm (CSA) is performed. The alteration involves the implementation of adaptive step size adjustment strategy, and thus enabling faster convergence to the global optimal solutions. The feasibility of the proposed algorithm is validated against benchmark optimization functions, where the obtained results demonstrate a marked improvement over the standard CSA, in all the cases. PMID:25298971
Adaptive cuckoo search algorithm for unconstrained optimization.
Ong, Pauline
2014-01-01
Modification of the intensification and diversification approaches in the recently developed cuckoo search algorithm (CSA) is performed. The alteration involves the implementation of adaptive step size adjustment strategy, and thus enabling faster convergence to the global optimal solutions. The feasibility of the proposed algorithm is validated against benchmark optimization functions, where the obtained results demonstrate a marked improvement over the standard CSA, in all the cases. PMID:25298971
Belief Propagation Algorithm for Portfolio Optimization Problems
2015-01-01
The typical behavior of optimal solutions to portfolio optimization problems with absolute deviation and expected shortfall models using replica analysis was pioneeringly estimated by S. Ciliberti et al. [Eur. Phys. B. 57, 175 (2007)]; however, they have not yet developed an approximate derivation method for finding the optimal portfolio with respect to a given return set. In this study, an approximation algorithm based on belief propagation for the portfolio optimization problem is presented using the Bethe free energy formalism, and the consistency of the numerical experimental results of the proposed algorithm with those of replica analysis is confirmed. Furthermore, the conjecture of H. Konno and H. Yamazaki, that the optimal solutions with the absolute deviation model and with the mean-variance model have the same typical behavior, is verified using replica analysis and the belief propagation algorithm. PMID:26305462
Saeidi, Iman; Barfi, Behruz; Asghari, Alireza; Gharahbagh, Abdorreza Alavi; Barfi, Azadeh; Peyrovi, Moazameh; Afsharzadeh, Maryam; Hojatinasab, Mostafa
2015-10-01
A novel and environmentally friendly ionic-liquid-based hollow-fiber liquid-phase microextraction method combined with a hybrid artificial neural network (ANN)-genetic algorithm (GA) strategy was developed for ferro and ferric ions speciation as model analytes. Different parameters such as type and volume of extraction solvent, amounts of chelating agent, volume and pH of sample, ionic strength, stirring rate, and extraction time were investigated. Much more effective parameters were firstly examined based on one-variable-at-a-time design, and obtained results were used to construct an independent model for each parameter. The models were then applied to achieve the best and minimum numbers of candidate points as inputs for the ANN process. The maximum extraction efficiencies were achieved after 9 min using 22.0 ?L of 1-hexyl-3-methylimidazolium hexafluorophosphate ([C6MIM][PF6]) as the acceptor phase and 10 mL of sample at pH?=?7.0 containing 64.0 ?g L(-1) of benzohydroxamic acid (BHA) as the complexing agent, after the GA process. Once optimized, analytical performance of the method was studied in terms of linearity (1.3-316 ?g L(-1), R (2)?=?0.999), accuracy (recovery?=?90.1-92.3 %), and precision (relative standard deviation (RSD) <3.1). Finally, the method was successfully applied to speciate the iron species in the environmental and wastewater samples. PMID:26383736
Karimi, Mahmoud; Keyhani, Alireza; Akram, Asadolah; Rahman, Masoud; Jenkins, Bryan; Stroeve, Pieter
2013-01-01
The production ofbiodiesel by transesterification of waste cooking oil (WCO) to partially substitute petroleum diesel is one of the measures for solving the twin problems of environment pollution and energy demand. An environmentally benign process for the enzymatic transesterification using immobilized lipase has attracted considerable attention for biodiesel production. Here, a superparamagnetic, high surface area substrate for lipase immobilization is evaluated. These immobilization substrates are composed of mesoporous silica/superparamagnetic iron oxide core-shell nanoparticles. The effects of methanol ratio to WCO, lipase concentration, water content and reaction time on the synthesis of biodiesel were analysed by utilizing the response surface methodology (RSM). A quadratic response surface equation for calculating fatty acid methyl ester (FAME) content as the objective function was established based on experimental data obtained in accordance with the central composite design. The RSM-based model was then used as the fitness function for genetic algorithm (GA) to optimize its input space. Hybrid RSM-GA predicted the maximum FAME content (91%) at the optimum level of medium variables: methanol ratio to WCO, 4.34; lipase content, 43.6%; water content, 10.22%; and reaction time, 6h. Moreover, the immobilized lipase could be used for four times without considerable loss of the activity. PMID:24350474
Finding Tradeoffs by Using Multiobjective Optimization Algorithms
Shigeru Obayashi; Daisuke Sasaki; Akira Oyama
2005-01-01
The objective of the present study is to demonstrate performances of Evolutionary Algorithms (EAs) and conventional gradient-based methods for finding Pareto fronts. The multiobjective optimization algorithms are applied to analytical test problems as well as to the real-world problems of a compressor design. The comparison results clearly indicate the superiority of EAs in finding tradeoffs.
A Computational Intelligence Optimization Algorithm Based
Ziegler, Günter M.
of spiders which interact to each other based on the biological laws of the cooperative colony. The algorithm intelligence have been successfully developed in areas such as neural networks, fuzzy systems and evolutionaryA Computational Intelligence Optimization Algorithm Based on the Behavior of the Social-Spider Erik
Evolutionary Algorithm for Optimal Vaccination Scheme
NASA Astrophysics Data System (ADS)
Parousis-Orthodoxou, K. J.; Vlachos, D. S.
2014-03-01
The following work uses the dynamic capabilities of an evolutionary algorithm in order to obtain an optimal immunization strategy in a user specified network. The produced algorithm uses a basic genetic algorithm with crossover and mutation techniques, in order to locate certain nodes in the inputted network. These nodes will be immunized in an SIR epidemic spreading process, and the performance of each immunization scheme, will be evaluated by the level of containment that provides for the spreading of the disease.
Social Emotional Optimization Algorithm for Nonlinear Constrained Optimization Problems
NASA Astrophysics Data System (ADS)
Xu, Yuechun; Cui, Zhihua; Zeng, Jianchao
Nonlinear programming problem is one important branch in operational research, and has been successfully applied to various real-life problems. In this paper, a new approach called Social emotional optimization algorithm (SEOA) is used to solve this problem which is a new swarm intelligent technique by simulating the human behavior guided by emotion. Simulation results show that the social emotional optimization algorithm proposed in this paper is effective and efficiency for the nonlinear constrained programming problems.
A data locality optimizing algorithm
Michael E. Wolf; Monica S. Lam
1991-01-01
This paper proposes an algorithm that improves the locality of a loop nest by transforming the code via interchange, reversal, skewing and tiling. The loop transformation rrlgorithm is based on two concepts: a mathematical formulation of reuse and locality, and a loop transformation theory that unifies the various transforms as unimodular matrix tmnsfonnations. The algorithm haa been implemented in the
A data locality optimizing algorithm
Monica S. Lam
1991-01-01
This paper proposes an algorithm that improves the locality of a loop nest by transforming the code via interchange,reversal, skewing and tiling. The loop transformation rrlgorithm is based on two concepts: a mathematical formulation of reuse and locality, and a loop transformation theory that unifies the various transforms as unimodular matrix tmnsfonnations.The algorithm haa been implemented in the SUIF (Stanford
Algorithms for optimal dyadic decision trees
Hush, Don; Porter, Reid
2009-01-01
A new algorithm for constructing optimal dyadic decision trees was recently introduced, analyzed, and shown to be very effective for low dimensional data sets. This paper enhances and extends this algorithm by: introducing an adaptive grid search for the regularization parameter that guarantees optimal solutions for all relevant trees sizes, revising the core tree-building algorithm so that its run time is substantially smaller for most regularization parameter values on the grid, and incorporating new data structures and data pre-processing steps that provide significant run time enhancement in practice.
A hybrid of the genetic algorithm and concurrent simplex
Randolph, David Ethan
1995-01-01
A HYBRID OF THE GENETIC ALGORITHM AND CONCURRENT SIMPLEX A Thesis by DAVID ETHAN RANDOLPH Submitted to the Office of Graduate Studies of Texas ARM University in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE May... 1995 Major Subject: Computer Science A HYBRID OF THE GENETIC ALGORITHM AND CONCURRENT SIMPLEX A Thesis DAVID ETHAN RANDOLPH Submitted to Texas AkM University in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE...
Hybrid Optimization Schemes for Quantum Control
Michael H Goerz; K. Birgitta Whaley; Christiane P Koch
2015-05-20
Optimal control theory is a powerful tool for solving control problems in quantum mechanics, ranging from the control of chemical reactions to the implementation of gates in a quantum computer. Gradient-based optimization methods are able to find high fidelity controls, but require considerable numerical effort and often yield highly complex solutions. We propose here to employ a two-stage optimization scheme to significantly speed up convergence and achieve simpler controls. The control is initially parametrized using only a few free parameters, such that optimization in this pruned search space can be performed with a simplex method. The result, considered now simply as an arbitrary function on a time grid, is the starting point for further optimization with a gradient-based method that can quickly converge to high fidelities. We illustrate the success of this hybrid technique by optimizing a holonomic phasegate for two superconducting transmon qubits coupled with a shared transmission line resonator, showing that a combination of Nelder-Mead simplex and Krotov's method yields considerably better results than either one of the two methods alone.
Hybrid Optimization Schemes for Quantum Control
Michael H. Goerz; K. Birgitta Whaley; Christiane P. Koch
2015-10-03
Optimal control theory is a powerful tool for solving control problems in quantum mechanics, ranging from the control of chemical reactions to the implementation of gates in a quantum computer. Gradient-based optimization methods are able to find high fidelity controls, but require considerable numerical effort and often yield highly complex solutions. We propose here to employ a two-stage optimization scheme to significantly speed up convergence and achieve simpler controls. The control is initially parametrized using only a few free parameters, such that optimization in this pruned search space can be performed with a simplex method. The result, considered now simply as an arbitrary function on a time grid, is the starting point for further optimization with a gradient-based method that can quickly converge to high fidelities. We illustrate the success of this hybrid technique by optimizing a holonomic phasegate for two superconducting transmon qubits coupled with a shared transmission line resonator, showing that a combination of Nelder-Mead simplex and Krotov's method yields considerably better results than either one of the two methods alone.
Two stochastic optimization algorithms applied to nuclear reactor core design
Wagner F. Sacco; Cassiano R. E. de oliveira; Cláudio M. N. A. Pereira
2006-01-01
Two stochastic optimization algorithms conceptually similar to Simulated Annealing are presented and applied to a core design optimization problem previously solved with Genetic Algorithms. The two algorithms are the novel Particle Collision Algorithm (PCA), which is introduced in detail, and Dueck's Great Deluge Algorithm (GDA). The optimization problem consists in adjusting several reactor cell parameters, such as dimensions, enrichment and
Optimal Microdata File Merging: A New Model & Network Optimization Algorithm
Barr, Richard
Betty L. Hickman, U Nebraska Omaha J. Scott Turner, Oklahoma State ©2000 Richard Barr, Betty Hickman, J1 Optimal Microdata File Merging: A New Model & Network Optimization Algorithm Richard S. Barr, SMU. Scott Turner #12;2 Microdata Files · Stratified samples of large populations · Multi
Analysis and optimization of hybrid electric vehicle thermal management systems
NASA Astrophysics Data System (ADS)
Hamut, H. S.; Dincer, I.; Naterer, G. F.
2014-02-01
In this study, the thermal management system of a hybrid electric vehicle is optimized using single and multi-objective evolutionary algorithms in order to maximize the exergy efficiency and minimize the cost and environmental impact of the system. The objective functions are defined and decision variables, along with their respective system constraints, are selected for the analysis. In the multi-objective optimization, a Pareto frontier is obtained and a single desirable optimal solution is selected based on LINMAP decision-making process. The corresponding solutions are compared against the exergetic, exergoeconomic and exergoenvironmental single objective optimization results. The results show that the exergy efficiency, total cost rate and environmental impact rate for the baseline system are determined to be 0.29, ¢28 h-1 and 77.3 mPts h-1 respectively. Moreover, based on the exergoeconomic optimization, 14% higher exergy efficiency and 5% lower cost can be achieved, compared to baseline parameters at an expense of a 14% increase in the environmental impact. Based on the exergoenvironmental optimization, a 13% higher exergy efficiency and 5% lower environmental impact can be achieved at the expense of a 27% increase in the total cost.
Parallel Algorithms for Graph Optimization using Tree Decompositions
Sullivan, Blair D [ORNL; Weerapurage, Dinesh P [ORNL; Groer, Christopher S [ORNL
2012-06-01
Although many $\\cal{NP}$-hard graph optimization problems can be solved in polynomial time on graphs of bounded tree-width, the adoption of these techniques into mainstream scientific computation has been limited due to the high memory requirements of the necessary dynamic programming tables and excessive runtimes of sequential implementations. This work addresses both challenges by proposing a set of new parallel algorithms for all steps of a tree decomposition-based approach to solve the maximum weighted independent set problem. A hybrid OpenMP/MPI implementation includes a highly scalable parallel dynamic programming algorithm leveraging the MADNESS task-based runtime, and computational results demonstrate scaling. This work enables a significant expansion of the scale of graphs on which exact solutions to maximum weighted independent set can be obtained, and forms a framework for solving additional graph optimization problems with similar techniques.
Mohanasundaram, Ranganathan; Periasamy, Pappampalayam Sanmugam
2015-01-01
The current high profile debate with regard to data storage and its growth have become strategic task in the world of networking. It mainly depends on the sensor nodes called producers, base stations, and also the consumers (users and sensor nodes) to retrieve and use the data. The main concern dealt here is to find an optimal data storage position in wireless sensor networks. The works that have been carried out earlier did not utilize swarm intelligence based optimization approaches to find the optimal data storage positions. To achieve this goal, an efficient swam intelligence approach is used to choose suitable positions for a storage node. Thus, hybrid particle swarm optimization algorithm has been used to find the suitable positions for storage nodes while the total energy cost of data transmission is minimized. Clustering-based distributed data storage is utilized to solve clustering problem using fuzzy-C-means algorithm. This research work also considers the data rates and locations of multiple producers and consumers to find optimal data storage positions. The algorithm is implemented in a network simulator and the experimental results show that the proposed clustering and swarm intelligence based ODS strategy is more effective than the earlier approaches. PMID:25734182
Mohanasundaram, Ranganathan; Periasamy, Pappampalayam Sanmugam
2015-01-01
The current high profile debate with regard to data storage and its growth have become strategic task in the world of networking. It mainly depends on the sensor nodes called producers, base stations, and also the consumers (users and sensor nodes) to retrieve and use the data. The main concern dealt here is to find an optimal data storage position in wireless sensor networks. The works that have been carried out earlier did not utilize swarm intelligence based optimization approaches to find the optimal data storage positions. To achieve this goal, an efficient swam intelligence approach is used to choose suitable positions for a storage node. Thus, hybrid particle swarm optimization algorithm has been used to find the suitable positions for storage nodes while the total energy cost of data transmission is minimized. Clustering-based distributed data storage is utilized to solve clustering problem using fuzzy-C-means algorithm. This research work also considers the data rates and locations of multiple producers and consumers to find optimal data storage positions. The algorithm is implemented in a network simulator and the experimental results show that the proposed clustering and swarm intelligence based ODS strategy is more effective than the earlier approaches. PMID:25734182
Iterative phase retrieval algorithms. I: optimization.
Guo, Changliang; Liu, Shi; Sheridan, John T
2015-05-20
Two modified Gerchberg-Saxton (GS) iterative phase retrieval algorithms are proposed. The first we refer to as the spatial phase perturbation GS algorithm (SPP GSA). The second is a combined GS hybrid input-output algorithm (GS/HIOA). In this paper (Part I), it is demonstrated that the SPP GS and GS/HIO algorithms are both much better at avoiding stagnation during phase retrieval, allowing them to successfully locate superior solutions compared with either the GS or the HIO algorithms. The performances of the SPP GS and GS/HIO algorithms are also compared. Then, the error reduction (ER) algorithm is combined with the HIO algorithm (ER/HIOA) to retrieve the input object image and the phase, given only some knowledge of its extent and the amplitude in the Fourier domain. In Part II, the algorithms developed here are applied to carry out known plaintext and ciphertext attacks on amplitude encoding and phase encoding double random phase encryption systems. Significantly, ER/HIOA is then used to carry out a ciphertext-only attack on AE DRPE systems. PMID:26192504
A Hybrid Electromagnetism-Like Algorithm for Single Machine Scheduling Problem
NASA Astrophysics Data System (ADS)
Chen, Shih-Hsin; Chang, Pei-Chann; Chan, Chien-Lung; Mani, V.
Electromagnetism-like algorithm (EM) is a population-based meta-heuristic which has been proposed to solve continuous problems effectively. In this paper, we present a new meta-heuristic that uses the EM methodology to solve the single machine scheduling problem. Single machine scheduling is a combinatorial optimization problem. Schedule representation for our problem is based on random keys. Because there is little research in solving the combinatorial optimization problem (COP) by EM, the paper attempts to employ the random-key concept enabling EM to solve COP in single machine scheduling problem. We present a hybrid algorithm that combines the EM methodology and genetic operators to obtain the best/optimal schedule for this single machine scheduling problem, which attempts to achieve convergence and diversity effect when they iteratively solve the problem. The objective in our problem is minimization of the sum of earliness and tardiness. This hybrid algorithm was tested on a set of standard test problems available in the literature. The computational results show that this hybrid algorithm performs better than the standard genetic algorithm.
An Efficient Chemical Reaction Optimization Algorithm for Multiobjective Optimization.
Bechikh, Slim; Chaabani, Abir; Ben Said, Lamjed
2015-10-01
Recently, a new metaheuristic called chemical reaction optimization was proposed. This search algorithm, inspired by chemical reactions launched during collisions, inherits several features from other metaheuristics such as simulated annealing and particle swarm optimization. This fact has made it, nowadays, one of the most powerful search algorithms in solving mono-objective optimization problems. In this paper, we propose a multiobjective variant of chemical reaction optimization, called nondominated sorting chemical reaction optimization, in an attempt to exploit chemical reaction optimization features in tackling problems involving multiple conflicting criteria. Since our approach is based on nondominated sorting, one of the main contributions of this paper is the proposal of a new quasi-linear average time complexity quick nondominated sorting algorithm; thereby making our multiobjective algorithm efficient from a computational cost viewpoint. The experimental comparisons against several other multiobjective algorithms on a variety of benchmark problems involving various difficulties show the effectiveness and the efficiency of this multiobjective version in providing a well-converged and well-diversified approximation of the Pareto front. PMID:25373137
SFC Optimization for Aero Engine Based on Hybrid GA-SQP Method
NASA Astrophysics Data System (ADS)
Li, Jie; Fan, Ding; Sreeram, Victor
2013-12-01
This study focuses on on-line specific fuel consumption (SFC) optimization of aero engines. For solving this optimization problem, a nonlinear pneumatic and thermodynamics model of the aero engine is built and a hybrid optimization technique which is formed by combining the genetic algorithm (GA) and the sequential quadratic programming (SQP) is presented. The ability of standard GA and standard SQP in solving this type of problem is investigated. It has been found that, although the SQP is fast, very little SFC reductions can be obtained. The GA is able to solve the problem well but a lot of computational time is needed. The presented hybrid GA-SQP gives a good SFC optimization effect and saves 76.6% computational time when compared to the standard GA. It has been shown that the hybrid GA-SQP is a more effective and higher real-time method for SFC on-line optimization of the aero engine.
A novel bee swarm optimization algorithm for numerical function optimization
NASA Astrophysics Data System (ADS)
Akbari, Reza; Mohammadi, Alireza; Ziarati, Koorush
2010-10-01
The optimization algorithms which are inspired from intelligent behavior of honey bees are among the most recently introduced population based techniques. In this paper, a novel algorithm called bee swarm optimization, or BSO, and its two extensions for improving its performance are presented. The BSO is a population based optimization technique which is inspired from foraging behavior of honey bees. The proposed approach provides different patterns which are used by the bees to adjust their flying trajectories. As the first extension, the BSO algorithm introduces different approaches such as repulsion factor and penalizing fitness (RP) to mitigate the stagnation problem. Second, to maintain efficiently the balance between exploration and exploitation, time-varying weights (TVW) are introduced into the BSO algorithm. The proposed algorithm (BSO) and its two extensions (BSO-RP and BSO-RPTVW) are compared with existing algorithms which are based on intelligent behavior of honey bees, on a set of well known numerical test functions. The experimental results show that the BSO algorithms are effective and robust; produce excellent results, and outperform other algorithms investigated in this consideration.
Algorithm selection in structural optimization
Clune, Rory P. (Rory Patrick)
2013-01-01
Structural optimization is largely unused as a practical design tool, despite an extensive academic literature which demonstrates its potential to dramatically improve design processes and outcomes. Many factors inhibit ...
A Cuckoo Search Algorithm for Multimodal Optimization
2014-01-01
Interest in multimodal optimization is expanding rapidly, since many practical engineering problems demand the localization of multiple optima within a search space. On the other hand, the cuckoo search (CS) algorithm is a simple and effective global optimization algorithm which can not be directly applied to solve multimodal optimization problems. This paper proposes a new multimodal optimization algorithm called the multimodal cuckoo search (MCS). Under MCS, the original CS is enhanced with multimodal capacities by means of (1) the incorporation of a memory mechanism to efficiently register potential local optima according to their fitness value and the distance to other potential solutions, (2) the modification of the original CS individual selection strategy to accelerate the detection process of new local minima, and (3) the inclusion of a depuration procedure to cyclically eliminate duplicated memory elements. The performance of the proposed approach is compared to several state-of-the-art multimodal optimization algorithms considering a benchmark suite of fourteen multimodal problems. Experimental results indicate that the proposed strategy is capable of providing better and even a more consistent performance over existing well-known multimodal algorithms for the majority of test problems yet avoiding any serious computational deterioration. PMID:25147850
A cuckoo search algorithm for multimodal optimization.
Cuevas, Erik; Reyna-Orta, Adolfo
2014-01-01
Interest in multimodal optimization is expanding rapidly, since many practical engineering problems demand the localization of multiple optima within a search space. On the other hand, the cuckoo search (CS) algorithm is a simple and effective global optimization algorithm which can not be directly applied to solve multimodal optimization problems. This paper proposes a new multimodal optimization algorithm called the multimodal cuckoo search (MCS). Under MCS, the original CS is enhanced with multimodal capacities by means of (1) the incorporation of a memory mechanism to efficiently register potential local optima according to their fitness value and the distance to other potential solutions, (2) the modification of the original CS individual selection strategy to accelerate the detection process of new local minima, and (3) the inclusion of a depuration procedure to cyclically eliminate duplicated memory elements. The performance of the proposed approach is compared to several state-of-the-art multimodal optimization algorithms considering a benchmark suite of fourteen multimodal problems. Experimental results indicate that the proposed strategy is capable of providing better and even a more consistent performance over existing well-known multimodal algorithms for the majority of test problems yet avoiding any serious computational deterioration. PMID:25147850
New algorithms for binary wavefront optimization
NASA Astrophysics Data System (ADS)
Zhang, Xiaolong; Kner, Peter
2015-03-01
Binary amplitude modulation promises to allow rapid focusing through strongly scattering media with a large number of segments due to the faster update rates of digital micromirror devices (DMDs) compared to spatial light modulators (SLMs). While binary amplitude modulation has a lower theoretical enhancement than phase modulation, the faster update rate should more than compensate for the difference - a factor of ?2 /2. Here we present two new algorithms, a genetic algorithm and a transmission matrix algorithm, for optimizing the focus with binary amplitude modulation that achieve enhancements close to the theoretical maximum. Genetic algorithms have been shown to work well in noisy environments and we show that the genetic algorithm performs better than a stepwise algorithm. Transmission matrix algorithms allow complete characterization and control of the medium but require phase control either at the input or output. Here we introduce a transmission matrix algorithm that works with only binary amplitude control and intensity measurements. We apply these algorithms to binary amplitude modulation using a Texas Instruments Digital Micromirror Device. Here we report an enhancement of 152 with 1536 segments (9.90%×N) using a genetic algorithm with binary amplitude modulation and an enhancement of 136 with 1536 segments (8.9%×N) using an intensity-only transmission matrix algorithm.
Hybrid method for aerodynamic shape optimization in automotive industry
Dumas, Laurent
Hybrid method for aerodynamic shape optimization in automotive industry Freedeerique Muyl April 2003; accepted 4 June 2003 Abstract An aerodynamic shape optimization tool for complex industrial reasons, concerns car manufacturers. Consequently, the improvement of the aerodynamics of car shapes, more
A hybrid algorithm and its applications to fuzzy logic modeling of nonlinear systems
NASA Astrophysics Data System (ADS)
Wang, Zhongjun
System models allow us to simulate and analyze system dynamics efficiently. Most importantly, system models allow us to make prediction about system behaviors and to perform system parametric variation analysis without having to build the actual systems. The fuzzy logic modeling technique has been successfully applied in complex nonlinear system modeling such as unsteady aerodynamics modeling etc. recently. However, the current forward search algorithm to identify fuzzy logic model structures is very time-consuming. It is not unusual to spend several days or even a few weeks in computer CPU time to obtain better nonlinear system model structures by this forward search. Moreover, how to speed up the fuzzy logic model parameter identification process is also challenging when the number of influencing variables of nonlinear systems is large. To solve these problems, a hybrid algorithm for the nonlinear system modeling is proposed, formalized, implemented, and evaluated in this dissertation. By combining the fuzzy logic modeling technique with genetic algorithms, the developed hybrid algorithm is applied to both fuzzy logic model structure identification and model parameter identification. In the model structure identification process, the hybrid algorithm has the ability to find feasible structures more efficiently and effectively than the forward search. In the model parameter identification process (by using Newton gradient descent algorithm), the proposed hybrid algorithm incorporates genetic search algorithm to dynamically select convergence factors. It has the advantages of quick search yet maintains the monotonically convergent properties of the Newton gradient descent algorithm. To evaluate the properties of the developed hybrid algorithm, a nonlinear, unsteady aerodynamic normal force model with a complex system involving fourteen influencing variables is established from flight data. The results show that this hybrid algorithm can identify the aerodynamic model structures much quicker than the forward search. In addition, the results also show that this hybrid algorithm can identify model parameters much quicker than the one with fixed and arbitrary convergence factors. Finally, an application of the fuzzy logic modeling technique to Kansas Arbuckle oil well performance analysis is performed. It gives oil operators a powerful decision-making tool for candidate-well selection and treatment to optimize performance.
Structural Optimization Using Harmony Search Algorithm
D. Srikanth; S. Barai
\\u000a The paper presents the Structural optimization based on the New Harmony Search (HS) meta-heuristic algorithm. HS was conceptualized\\u000a using the musical process of searching for a perfect state of harmony. The HS algorithm does not require initial values and\\u000a uses a random search instead of a gradient search. 3D truss structure examples with fixed geometries are presented to demonstrate\\u000a the
Ant Algorithms for Discrete Optimization
Libre de Bruxelles, UniversitÃ©
optimization that took inspiration from the observation of ant colonies' foraging behavior, and introduces and interesting behavior of ant colonies is their foraging behavior, and, in particular, how ants can find behavior can give rise, once employed by a colony of ants, to the emergence of shortest paths. That is
Ant Algorithms for Discrete Optimization
Conati, Cristina
optimization that took inspiration from the observation of ant colonies' foraging behavior, and introduces to the relative simplicity of the colony's individuals. An important and interesting behavior of ant colonies is their foraging behavior, and, in particular, how ants can nd the shortest paths between food sources
Ant Algorithms for Discrete Optimization
Gambardella, Luca Maria
for discrete optimization which took inspiration from the observation of ant colonies foraging behavior to the relative sim- plicity of the colony's individuals. An important and interesting behavior of ant colonies is their foraging behavior, and, in particular, how ants can find shortest paths between food sources and their nest
Fast Hybrid PSO and Tabu Search Approach for Optimization of a Fuzzy Controller
Talbi, Nesrine
2011-01-01
In this paper, a fuzzy controller type Takagi_Sugeno zero order is optimized by the method of hybrid Particle Swarm Optimization (PSO) and Tabu Search (TS). The algorithm automatically adjusts the membership functions of fuzzy controller inputs and the conclusions of fuzzy rules. At each iteration of PSO, we calculate the best solution and we seek the best neighbor by Tabu search, this operation minimizes the number of iterations and computation time while maintaining accuracy and minimum response time. We apply this algorithm to optimize a fuzzy controller for a simple inverted pendulum with three rules.
Applications of Numerical Optimal Control to Nonlinear Hybrid Systems
Shangming Wei; Kasemsak Uthaichana; Raymond A. DeCarlo
2006-01-01
This paper develops a technique for numerically solving hybrid optimal control prob- lems. The theoretical foundation of the approach is a recently developed methodol- ogy by Bengea & DeCarlo (1) for solving switched optimal control problems through embedding. The methodology is extended to incorporate hybrid behavior stemming from autonomous (uncontrolled) switches that results in the plant equations with piecewise smooth
SIMULATION AND OPTIMAL CONTROL OF HYBRID GROUND SOURCE HEAT
SIMULATION AND OPTIMAL CONTROL OF HYBRID GROUND SOURCE HEAT PUMP SYSTEMS By XIAOWEI XU Bachelor #12;ii SIMULATION AND OPTIMAL CONTROL OF HYBRID GROUND SOURCE HEAT PUMP SYSTEMS Dissertation Approved of Vertical Ground Loop Heat Exchangers - Analytical....... 21 2.1.1.2 Modeling of Vertical Ground Loop Heat
Evolving digital circuits using hybrid particle swarm optimization and differential evolution.
Moore, Phillip W; Venayagamoorthy, Ganesh K
2006-06-01
This paper presents the evolution of combinational logic circuits by a new hybrid algorithm known as the Differential Evolution Particle Swarm Optimization (DEPSO), formulated from the concepts of a modified particle swarm and differential evolution. The particle swarm in the hybrid algorithm is represented by a discrete 3-integer approach. A hybrid multi-objective fitness function is coined to achieve two goals for the evolution of circuits. The first goal is to evolve combinational logic circuits with 100% functionality, called the feasible circuits. The second goal is to minimize the number of logic gates needed to realize the feasible circuits. In addition, the paper presents modifications to enhance performance and robustness of particle swarm and evolutionary techniques for discrete optimization problems. Comparison of the performance of the hybrid algorithm to the conventional Karnaugh map and evolvable hardware techniques such as genetic algorithm, modified particle swarm, and differential evolution are presented on a number of case studies. Results show that feasible circuits are always achieved by the DEPSO algorithm unlike with other algorithms and the percentage of best solutions (minimal logic gates) is higher. PMID:17044238
Randomized Algorithm for UAVs Group Flight Optimization
Granichin, Oleg
Randomized Algorithm for UAVs Group Flight Optimization Konstantin Amelin Natalia Amelina , Oleg of Mechanical Engineering of RAS, Russia Saint Petersburg National Research University of Information Technologies, Mechanics and Optics, St. Petersburg, Russia, e-mail: boris.andrievsky@gmail.com Abstract
Optimization of a chemical identification algorithm
NASA Astrophysics Data System (ADS)
Chyba, Thomas H.; Fisk, Brian; Gunning, Christin; Farley, Kevin; Polizzi, Amber; Baughman, David; Simpson, Steven; Slamani, Mohamed-Adel; Almassy, Robert; Da Re, Ryan; Li, Eunice; MacDonald, Steve; Slamani, Ahmed; Mitchell, Scott A.; Pendell-Jones, Jay; Reed, Timothy L.; Emge, Darren
2010-04-01
A procedure to evaluate and optimize the performance of a chemical identification algorithm is presented. The Joint Contaminated Surface Detector (JCSD) employs Raman spectroscopy to detect and identify surface chemical contamination. JCSD measurements of chemical warfare agents, simulants, toxic industrial chemicals, interferents and bare surface backgrounds were made in the laboratory and under realistic field conditions. A test data suite, developed from these measurements, is used to benchmark algorithm performance throughout the improvement process. In any one measurement, one of many possible targets can be present along with interferents and surfaces. The detection results are expressed as a 2-category classification problem so that Receiver Operating Characteristic (ROC) techniques can be applied. The limitations of applying this framework to chemical detection problems are discussed along with means to mitigate them. Algorithmic performance is optimized globally using robust Design of Experiments and Taguchi techniques. These methods require figures of merit to trade off between false alarms and detection probability. Several figures of merit, including the Matthews Correlation Coefficient and the Taguchi Signal-to-Noise Ratio are compared. Following the optimization of global parameters which govern the algorithm behavior across all target chemicals, ROC techniques are employed to optimize chemical-specific parameters to further improve performance.
GENERALIZED OPTIMIZATION ALGORITHM FOR SPEECH RECOGNITION TRANSDUCERS
Mohri, Mehryar
GENERALIZED OPTIMIZATION ALGORITHM FOR SPEECH RECOGNITION TRANSDUCERS Cyril Allauzen and Mehryar provide a common representation for the components of a speech recognition system. In previous work, we, determinization. However, not all weighted automata and transducers used in large vocabulary speech recognition
GENERALIZED OPTIMIZATION ALGORITHM FOR SPEECH RECOGNITION TRANSDUCERS
Allauzen, Cyril
GENERALIZED OPTIMIZATION ALGORITHM FOR SPEECH RECOGNITION TRANSDUCERS Cyril Allauzen and Mehryar provide a common representation for the components of a speech recognition system. In previous work, we, determinization. However, not all weighted automata and transducers used in large- vocabulary speech recognition
Generalized Weiszfeld Algorithms for Lq Optimization
Trumpf, Jochen
1 Generalized Weiszfeld Algorithms for Lq Optimization Khurrum Aftab, Richard Hartley, Jochen advantages. Given a set of points {y1, y2, . . . , yk} in some metric space Â· K. Aftab and R. Hartley-mail: Khurrum.Aftab@anu.edu.au, Richard.Hartley@anu.edu.au Â· J. Trumpf is with the Research School
Combinatorial Multiobjective Optimization Using Genetic Algorithms
NASA Technical Reports Server (NTRS)
Crossley, William A.; Martin. Eric T.
2002-01-01
The research proposed in this document investigated multiobjective optimization approaches based upon the Genetic Algorithm (GA). Several versions of the GA have been adopted for multiobjective design, but, prior to this research, there had not been significant comparisons of the most popular strategies. The research effort first generalized the two-branch tournament genetic algorithm in to an N-branch genetic algorithm, then the N-branch GA was compared with a version of the popular Multi-Objective Genetic Algorithm (MOGA). Because the genetic algorithm is well suited to combinatorial (mixed discrete / continuous) optimization problems, the GA can be used in the conceptual phase of design to combine selection (discrete variable) and sizing (continuous variable) tasks. Using a multiobjective formulation for the design of a 50-passenger aircraft to meet the competing objectives of minimizing takeoff gross weight and minimizing trip time, the GA generated a range of tradeoff designs that illustrate which aircraft features change from a low-weight, slow trip-time aircraft design to a heavy-weight, short trip-time aircraft design. Given the objective formulation and analysis methods used, the results of this study identify where turboprop-powered aircraft and turbofan-powered aircraft become more desirable for the 50 seat passenger application. This aircraft design application also begins to suggest how a combinatorial multiobjective optimization technique could be used to assist in the design of morphing aircraft.
Threshold matrix for digital halftoning by genetic algorithm optimization
NASA Astrophysics Data System (ADS)
Alander, Jarmo T.; Mantere, Timo J.; Pyylampi, Tero
1998-10-01
Digital halftoning is used both in low and high resolution high quality printing technologies. Our method is designed to be mainly used for low resolution ink jet marking machines to produce both gray tone and color images. The main problem with digital halftoning is pink noise caused by the human eye's visual transfer function. To compensate for this the random dot patterns used are optimized to contain more blue than pink noise. Several such dot pattern generator threshold matrices have been created automatically by using genetic algorithm optimization, a non-deterministic global optimization method imitating natural evolution and genetics. A hybrid of genetic algorithm with a search method based on local backtracking was developed together with several fitness functions evaluating dot patterns for rectangular grids. By modifying the fitness function, a family of dot generators results, each with its particular statistical features. Several versions of genetic algorithms, backtracking and fitness functions were tested to find a reasonable combination. The generated threshold matrices have been tested by simulating a set of test images using the Khoros image processing system. Even though the work was focused on developing low resolution marking technology, the resulting family of dot generators can be applied also in other halftoning application areas including high resolution printing technology.
A Hybrid Metaheuristic DE/CS Algorithm for UCAV Three-Dimension Path Planning
Wang, Gaige; Guo, Lihong; Duan, Hong; Wang, Heqi; Liu, Luo; Shao, Mingzhen
2012-01-01
Three-dimension path planning for uninhabited combat air vehicle (UCAV) is a complicated high-dimension optimization problem, which primarily centralizes on optimizing the flight route considering the different kinds of constrains under complicated battle field environments. A new hybrid metaheuristic differential evolution (DE) and cuckoo search (CS) algorithm is proposed to solve the UCAV three-dimension path planning problem. DE is applied to optimize the process of selecting cuckoos of the improved CS model during the process of cuckoo updating in nest. The cuckoos can act as an agent in searching the optimal UCAV path. And then, the UCAV can find the safe path by connecting the chosen nodes of the coordinates while avoiding the threat areas and costing minimum fuel. This new approach can accelerate the global convergence speed while preserving the strong robustness of the basic CS. The realization procedure for this hybrid metaheuristic approach DE/CS is also presented. In order to make the optimized UCAV path more feasible, the B-Spline curve is adopted for smoothing the path. To prove the performance of this proposed hybrid metaheuristic method, it is compared with basic CS algorithm. The experiment shows that the proposed approach is more effective and feasible in UCAV three-dimension path planning than the basic CS model. PMID:23193383
A hybrid metaheuristic DE/CS algorithm for UCAV three-dimension path planning.
Wang, Gaige; Guo, Lihong; Duan, Hong; Wang, Heqi; Liu, Luo; Shao, Mingzhen
2012-01-01
Three-dimension path planning for uninhabited combat air vehicle (UCAV) is a complicated high-dimension optimization problem, which primarily centralizes on optimizing the flight route considering the different kinds of constrains under complicated battle field environments. A new hybrid metaheuristic differential evolution (DE) and cuckoo search (CS) algorithm is proposed to solve the UCAV three-dimension path planning problem. DE is applied to optimize the process of selecting cuckoos of the improved CS model during the process of cuckoo updating in nest. The cuckoos can act as an agent in searching the optimal UCAV path. And then, the UCAV can find the safe path by connecting the chosen nodes of the coordinates while avoiding the threat areas and costing minimum fuel. This new approach can accelerate the global convergence speed while preserving the strong robustness of the basic CS. The realization procedure for this hybrid metaheuristic approach DE/CS is also presented. In order to make the optimized UCAV path more feasible, the B-Spline curve is adopted for smoothing the path. To prove the performance of this proposed hybrid metaheuristic method, it is compared with basic CS algorithm. The experiment shows that the proposed approach is more effective and feasible in UCAV three-dimension path planning than the basic CS model. PMID:23193383
A novel harmony search-K means hybrid algorithm for clustering gene expression data
Nazeer, KA Abdul; Sebastian, MP; Kumar, SD Madhu
2013-01-01
Recent progress in bioinformatics research has led to the accumulation of huge quantities of biological data at various data sources. The DNA microarray technology makes it possible to simultaneously analyze large number of genes across different samples. Clustering of microarray data can reveal the hidden gene expression patterns from large quantities of expression data that in turn offers tremendous possibilities in functional genomics, comparative genomics, disease diagnosis and drug development. The k- ¬means clustering algorithm is widely used for many practical applications. But the original k-¬means algorithm has several drawbacks. It is computationally expensive and generates locally optimal solutions based on the random choice of the initial centroids. Several methods have been proposed in the literature for improving the performance of the k-¬means algorithm. A meta-heuristic optimization algorithm named harmony search helps find out near-global optimal solutions by searching the entire solution space. Low clustering accuracy of the existing algorithms limits their use in many crucial applications of life sciences. In this paper we propose a novel Harmony Search-K means Hybrid (HSKH) algorithm for clustering the gene expression data. Experimental results show that the proposed algorithm produces clusters with better accuracy in comparison with the existing algorithms. PMID:23390351
A novel harmony search-K means hybrid algorithm for clustering gene expression data.
Nazeer, Ka Abdul; Sebastian, Mp; Kumar, Sd Madhu
2013-01-01
Recent progress in bioinformatics research has led to the accumulation of huge quantities of biological data at various data sources. The DNA microarray technology makes it possible to simultaneously analyze large number of genes across different samples. Clustering of microarray data can reveal the hidden gene expression patterns from large quantities of expression data that in turn offers tremendous possibilities in functional genomics, comparative genomics, disease diagnosis and drug development. The k- ¬means clustering algorithm is widely used for many practical applications. But the original k-¬means algorithm has several drawbacks. It is computationally expensive and generates locally optimal solutions based on the random choice of the initial centroids. Several methods have been proposed in the literature for improving the performance of the k-¬means algorithm. A meta-heuristic optimization algorithm named harmony search helps find out near-global optimal solutions by searching the entire solution space. Low clustering accuracy of the existing algorithms limits their use in many crucial applications of life sciences. In this paper we propose a novel Harmony Search-K means Hybrid (HSKH) algorithm for clustering the gene expression data. Experimental results show that the proposed algorithm produces clusters with better accuracy in comparison with the existing algorithms. PMID:23390351
Optimized TRIAD Algorithm for Attitude Determination
NASA Technical Reports Server (NTRS)
Bar-Itzhack, Itzhack Y.; Harman, Richard R.
1996-01-01
TRIAD is a well known simple algorithm that generates the attitude matrix between two coordinate systems when the components of two abstract vectors are given in the two systems. TRIAD however, is sensitive to the order in which the algorithm handles the vectors, such that the resulting attitude matrix is influenced more by the vector processed first. In this work we present a new algorithm, which we call Optimized TRIAD, that blends in a specified manner the two matrices generated by TRIAD when processing one vector first, and then when processing the other vector first. On the average, Optimized TRIAD yields a matrix which is better than either one of the two matrices in that is ti the closest to the correct matrix. This result is demonstrated through simulation.
Algorithm for fixed-range optimal trajectories
NASA Technical Reports Server (NTRS)
Lee, H. Q.; Erzberger, H.
1980-01-01
An algorithm for synthesizing optimal aircraft trajectories for specified range was developed and implemented in a computer program written in FORTRAN IV. The algorithm, its computer implementation, and a set of example optimum trajectories for the Boeing 727-100 aircraft are described. The algorithm optimizes trajectories with respect to a cost function that is the weighted sum of fuel cost and time cost. The optimum trajectory consists at most of a three segments: climb, cruise, and descent. The climb and descent profiles are generated by integrating a simplified set of kinematic and dynamic equations wherein the total energy of the aircraft is the independent or time like variable. At each energy level the optimum airspeeds and thrust settings are obtained as the values that minimize the variational Hamiltonian. Although the emphasis is on an off-line, open-loop computation, eventually the most important application will be in an on-board flight management system.
Adaptive hybrid optimal quantum control for imprecisely characterized systems.
Egger, D J; Wilhelm, F K
2014-06-20
Optimal quantum control theory carries a huge promise for quantum technology. Its experimental application, however, is often hindered by imprecise knowledge of the input variables, the quantum system's parameters. We show how to overcome this by adaptive hybrid optimal control, using a protocol named Ad-HOC. This protocol combines open- and closed-loop optimal control by first performing a gradient search towards a near-optimal control pulse and then an experimental fidelity estimation with a gradient-free method. For typical settings in solid-state quantum information processing, adaptive hybrid optimal control enhances gate fidelities by an order of magnitude, making optimal control theory applicable and useful. PMID:24996074
Adaptive Hybrid Optimal Quantum Control for Imprecisely Characterized Systems
NASA Astrophysics Data System (ADS)
Egger, D. J.; Wilhelm, F. K.
2014-06-01
Optimal quantum control theory carries a huge promise for quantum technology. Its experimental application, however, is often hindered by imprecise knowledge of the input variables, the quantum system's parameters. We show how to overcome this by adaptive hybrid optimal control, using a protocol named Ad-HOC. This protocol combines open- and closed-loop optimal control by first performing a gradient search towards a near-optimal control pulse and then an experimental fidelity estimation with a gradient-free method. For typical settings in solid-state quantum information processing, adaptive hybrid optimal control enhances gate fidelities by an order of magnitude, making optimal control theory applicable and useful.
An optimal algorithm for counting network motifs
NASA Astrophysics Data System (ADS)
Itzhack, Royi; Mogilevski, Yelena; Louzoun, Yoram
2007-07-01
Network motifs are small connected sub-graphs occurring at significantly higher frequencies in a given graph compared with random graphs of similar degree distribution. Recently, network motifs have attracted attention as a tool to study networks microscopic details. The commonly used algorithm for counting small-scale motifs is the one developed by Milo et al. This algorithm is extremely costly in CPU time and actually cannot work on large networks, consisting of more than 100,000 edges on current CPUs. We here present a new optimal algorithm, based on network decomposition for counting K-size network motifs with constant memory costs and a CPU cost linear with the number of counted motifs. Our algorithm performs better than previous full enumeration algorithms in terms of running time. Moreover, it uses a constant amount of memory. It also outperforms sampling algorithms. Our algorithm permits the counting of three and four motif for large networks that consists of more than 500,000 nodes and 5,000,000 links. For large networks, it performs more than a thousand times faster than current algorithms.
Orbital optimized double-hybrid density functionals
NASA Astrophysics Data System (ADS)
Peverati, Roberto; Head-Gordon, Martin
2013-07-01
This paper advocates development of a new class of double-hybrid (DH) density functionals where the energy is fully orbital optimized (OO) in presence of all correlation, rather than using a final non-iterative second order perturbative correction. The resulting OO-DH functionals resolve a number of artifacts associated with conventional DH functionals, such as first derivative discontinuities. To illustrate the possibilities, two non-empirical OO-DH functionals are obtained from existing DH functionals based on PBE: OO-PBE0-DH and OO-PBE0-2. Both functionals share the same functional form, with parameters determined on the basis of different physical considerations. The new functionals are tested on a variety of bonded, non-bonded and symmetry-breaking problems.
Optimal design of hybrid and non-hybrid fuel cell vehicles
Papalambros, Panos
Optimal design of hybrid and non-hybrid fuel cell vehicles by Jeongwoo Han A thesis submitted performance. Despite the relatively large number of models and prototypes, a model-based vehicle design. Optimal designs using the above modeling environment are compared in terms of fuel economy and vehicle
Lisbon, University of
Shape and Topology Optimization for Periodic Problems Part II: Optimization algorithm and numerical which alternates shape and topology optimization (the theoretical background about shape and topological Regeneration Â· Shape Optimization Â· Topology Optimization Â· Auxetic Materials 1 Introduction The main
A Hybrid Genetic Algorithm for Classification
James D. Kelly Jr.; Lawrence Davis
1991-01-01
In this paper we describe a method for hybridiz ing a genetic algorithm and a k nearest neighbors classification algorithm. We use the genetic algo rithm and a training data set to learn real-valued weights associated with individual attributes in the data set. We use the k nearest neighbors algo rithm to classify new data records based on their weighted
Reactive power optimization by genetic algorithm
Iba, Kenji )
1994-05-01
This paper presents a new approach to optimal reactive power planning based on a genetic algorithm. Many outstanding methods to this problem have been proposed in the past. However, most of these approaches have the common defect of being caught to a local minimum solution. The integer problem which yields integer value solutions for discrete controllers/banks still remains as a difficult one. The genetic algorithm is a kind of search algorithm based on the mechanics of natural selection and genetics. This algorithm can search for a global solution using multiple paths and treat integer problems naturally. The proposed method was applied to practical 51-bus and 224-bus systems to show its feasibility and capabilities. Although this method is not as fast as sophisticated traditional methods, the concept is quite promising and useful.
Hybrid Particle Swarm Optimization and Its Application to Multimodal 3D Medical Image Registration
Lin, Chen-Lun; Mimori, Aya; Chen, Yen-Wei
2012-01-01
In the area of medical image analysis, 3D multimodality image registration is an important issue. In the processing of registration, an optimization approach has been applied to estimate the transformation of the reference image and target image. Some local optimization techniques are frequently used, such as the gradient descent method. However, these methods need a good initial value in order to avoid the local resolution. In this paper, we present a new improved global optimization approach named hybrid particle swarm optimization (HPSO) for medical image registration, which includes two concepts of genetic algorithms—subpopulation and crossover. PMID:22997508
Hybrid particle swarm optimization and its application to multimodal 3D medical image registration.
Lin, Chen-Lun; Mimori, Aya; Chen, Yen-Wei
2012-01-01
In the area of medical image analysis, 3D multimodality image registration is an important issue. In the processing of registration, an optimization approach has been applied to estimate the transformation of the reference image and target image. Some local optimization techniques are frequently used, such as the gradient descent method. However, these methods need a good initial value in order to avoid the local resolution. In this paper, we present a new improved global optimization approach named hybrid particle swarm optimization (HPSO) for medical image registration, which includes two concepts of genetic algorithms-subpopulation and crossover. PMID:22997508
Optimization of advanced telecommunication algorithms from power and performance perspective
Khan, Zahid
2011-11-22
This thesis investigates optimization of advanced telecommunication algorithms from power and performance perspectives. The algorithms chosen are MIMO and LDPC. MIMO is implemented in custom ASIC for power optimization ...
Generalized Weiszfeld Algorithms for Lq Optimization.
Aftab, Khurrum; Hartley, Richard; Trumpf, Jochen
2015-04-01
In many computer vision applications, a desired model of some type is computed by minimizing a cost function based on several measurements. Typically, one may compute the model that minimizes the L2 cost, that is the sum of squares of measurement errors with respect to the model. However, the Lq solution which minimizes the sum of the qth power of errors usually gives more robust results in the presence of outliers for some values of q, for example, q = 1. The Weiszfeld algorithm is a classic algorithm for finding the geometric L1 mean of a set of points in Euclidean space. It is provably optimal and requires neither differentiation, nor line search. The Weiszfeld algorithm has also been generalized to find the L1 mean of a set of points on a Riemannian manifold of non-negative curvature. This paper shows that the Weiszfeld approach may be extended to a wide variety of problems to find an Lq mean for 1 ? q <; 2, while maintaining simplicity and provable convergence. We apply this problem to both single-rotation averaging (under which the algorithm provably finds the global Lq optimum) and multiple rotation averaging (for which no such proof exists). Experimental results of Lq optimization for rotations show the improved reliability and robustness compared to L2 optimization. PMID:26353290
A reliable algorithm for optimal control synthesis
NASA Technical Reports Server (NTRS)
Vansteenwyk, Brett; Ly, Uy-Loi
1992-01-01
In recent years, powerful design tools for linear time-invariant multivariable control systems have been developed based on direct parameter optimization. In this report, an algorithm for reliable optimal control synthesis using parameter optimization is presented. Specifically, a robust numerical algorithm is developed for the evaluation of the H(sup 2)-like cost functional and its gradients with respect to the controller design parameters. The method is specifically designed to handle defective degenerate systems and is based on the well-known Pade series approximation of the matrix exponential. Numerical test problems in control synthesis for simple mechanical systems and for a flexible structure with densely packed modes illustrate positively the reliability of this method when compared to a method based on diagonalization. Several types of cost functions have been considered: a cost function for robust control consisting of a linear combination of quadratic objectives for deterministic and random disturbances, and one representing an upper bound on the quadratic objective for worst case initial conditions. Finally, a framework for multivariable control synthesis has been developed combining the concept of closed-loop transfer recovery with numerical parameter optimization. The procedure enables designers to synthesize not only observer-based controllers but also controllers of arbitrary order and structure. Numerical design solutions rely heavily on the robust algorithm due to the high order of the synthesis model and the presence of near-overlapping modes. The design approach is successfully applied to the design of a high-bandwidth control system for a rotorcraft.
The hybrid Monte Carlo Algorithm and the chiral transition
Gupta, R.
1987-01-01
In this talk the author describes tests of the Hybrid Monte Carlo Algorithm for QCD done in collaboration with Greg Kilcup and Stephen Sharpe. We find that the acceptance in the glubal Metropolis step for Staggered fermions can be tuned and kept large without having to make the step-size prohibitively small. We present results for the finite temperature transition on 4/sup 4/ and 4 x 6/sup 3/ lattices using this algorithm.
Yoon, Byung-Jun
Adaptive Reference Update (ARU) Algorithm: A Stochastic Search Algorithm for Efficient Optimization, that can provide an efficient and systematic way for optimizing multi-drug cocktails. The ARU algorithm propose a novel stochastic search algorithm, called the adaptive reference update (ARU) algorithm
Deb, Suash; Yang, Xin-She
2014-01-01
Traditional K-means clustering algorithms have the drawback of getting stuck at local optima that depend on the random values of initial centroids. Optimization algorithms have their advantages in guiding iterative computation to search for global optima while avoiding local optima. The algorithms help speed up the clustering process by converging into a global optimum early with multiple search agents in action. Inspired by nature, some contemporary optimization algorithms which include Ant, Bat, Cuckoo, Firefly, and Wolf search algorithms mimic the swarming behavior allowing them to cooperatively steer towards an optimal objective within a reasonable time. It is known that these so-called nature-inspired optimization algorithms have their own characteristics as well as pros and cons in different applications. When these algorithms are combined with K-means clustering mechanism for the sake of enhancing its clustering quality by avoiding local optima and finding global optima, the new hybrids are anticipated to produce unprecedented performance. In this paper, we report the results of our evaluation experiments on the integration of nature-inspired optimization methods into K-means algorithms. In addition to the standard evaluation metrics in evaluating clustering quality, the extended K-means algorithms that are empowered by nature-inspired optimization methods are applied on image segmentation as a case study of application scenario. PMID:25202730
Fong, Simon; Deb, Suash; Yang, Xin-She; Zhuang, Yan
2014-01-01
Traditional K-means clustering algorithms have the drawback of getting stuck at local optima that depend on the random values of initial centroids. Optimization algorithms have their advantages in guiding iterative computation to search for global optima while avoiding local optima. The algorithms help speed up the clustering process by converging into a global optimum early with multiple search agents in action. Inspired by nature, some contemporary optimization algorithms which include Ant, Bat, Cuckoo, Firefly, and Wolf search algorithms mimic the swarming behavior allowing them to cooperatively steer towards an optimal objective within a reasonable time. It is known that these so-called nature-inspired optimization algorithms have their own characteristics as well as pros and cons in different applications. When these algorithms are combined with K-means clustering mechanism for the sake of enhancing its clustering quality by avoiding local optima and finding global optima, the new hybrids are anticipated to produce unprecedented performance. In this paper, we report the results of our evaluation experiments on the integration of nature-inspired optimization methods into K-means algorithms. In addition to the standard evaluation metrics in evaluating clustering quality, the extended K-means algorithms that are empowered by nature-inspired optimization methods are applied on image segmentation as a case study of application scenario. PMID:25202730
Hybrid protection algorithms based on game theory in multi-domain optical networks
NASA Astrophysics Data System (ADS)
Guo, Lei; Wu, Jingjing; Hou, Weigang; Liu, Yejun; Zhang, Lincong; Li, Hongming
2011-12-01
With the network size increasing, the optical backbone is divided into multiple domains and each domain has its own network operator and management policy. At the same time, the failures in optical network may lead to a huge data loss since each wavelength carries a lot of traffic. Therefore, the survivability in multi-domain optical network is very important. However, existing survivable algorithms can achieve only the unilateral optimization for profit of either users or network operators. Then, they cannot well find the double-win optimal solution with considering economic factors for both users and network operators. Thus, in this paper we develop the multi-domain network model with involving multiple Quality of Service (QoS) parameters. After presenting the link evaluation approach based on fuzzy mathematics, we propose the game model to find the optimal solution to maximize the user's utility, the network operator's utility, and the joint utility of user and network operator. Since the problem of finding double-win optimal solution is NP-complete, we propose two new hybrid protection algorithms, Intra-domain Sub-path Protection (ISP) algorithm and Inter-domain End-to-end Protection (IEP) algorithm. In ISP and IEP, the hybrid protection means that the intelligent algorithm based on Bacterial Colony Optimization (BCO) and the heuristic algorithm are used to solve the survivability in intra-domain routing and inter-domain routing, respectively. Simulation results show that ISP and IEP have the similar comprehensive utility. In addition, ISP has better resource utilization efficiency, lower blocking probability, and higher network operator's utility, while IEP has better user's utility.
A Reduction Algorithm for Computing the Hybridization Number of Two Trees
Semple, Charles
A Reduction Algorithm for Computing the Hybridization Number of Two Trees Magnus Bordewich1 hybridization include certain groups of plants, birds, and fish (see Mallet, 2005). The effect of hybridization
Global Optimization of Chemical Processes using Stochastic Algorithms
Neumaier, Arnold
Global Optimization of Chemical Processes using Stochastic Algorithms JULIO R. BANGA 1 and WARREN D engineering are difficult to optimize using gradientÂbased algorithms. These include process models with multimodalobjective functions and discontinuities. Herein, a stochastic algorithm is applied for the optimal design
Algorithm For Optimal Control Of Large Structures
NASA Technical Reports Server (NTRS)
Salama, Moktar A.; Garba, John A..; Utku, Senol
1989-01-01
Cost of computation appears competitive with other methods. Problem to compute optimal control of forced response of structure with n degrees of freedom identified in terms of smaller number, r, of vibrational modes. Article begins with Hamilton-Jacobi formulation of mechanics and use of quadratic cost functional. Complexity reduced by alternative approach in which quadratic cost functional expressed in terms of control variables only. Leads to iterative solution of second-order time-integral matrix Volterra equation of second kind containing optimal control vector. Cost of algorithm, measured in terms of number of computations required, is of order of, or less than, cost of prior algoritms applied to similar problems.
Parametric blind-deconvolution algorithm to remove image artifacts in hybrid imaging systems.
Demenikov, Mads; Harvey, Andrew R
2010-08-16
Hybrid imaging systems employing cubic phase modulation in the pupil-plane enable significantly increased depth of field, but artifacts in the recovered images are a major problem. We present a parametric blind-deconvolution algorithm, based on minimization of the high-frequency content of the restored image that enables recovery of artifact-free images for a wide range of defocus. We show that the algorithm enables robust matching of the image recovery kernel with the optical point-spread function to enable, for the first time, optimally low noise levels in recovered images. PMID:20721189
A survey on evolutionary algorithm based hybrid intelligence in bioinformatics.
Li, Shan; Kang, Liying; Zhao, Xing-Ming
2014-01-01
With the rapid advance in genomics, proteomics, metabolomics, and other types of omics technologies during the past decades, a tremendous amount of data related to molecular biology has been produced. It is becoming a big challenge for the bioinformatists to analyze and interpret these data with conventional intelligent techniques, for example, support vector machines. Recently, the hybrid intelligent methods, which integrate several standard intelligent approaches, are becoming more and more popular due to their robustness and efficiency. Specifically, the hybrid intelligent approaches based on evolutionary algorithms (EAs) are widely used in various fields due to the efficiency and robustness of EAs. In this review, we give an introduction about the applications of hybrid intelligent methods, in particular those based on evolutionary algorithm, in bioinformatics. In particular, we focus on their applications to three common problems that arise in bioinformatics, that is, feature selection, parameter estimation, and reconstruction of biological networks. PMID:24729969
Intelligent perturbation algorithms for space scheduling optimization
NASA Technical Reports Server (NTRS)
Kurtzman, Clifford R.
1990-01-01
The optimization of space operations is examined in the light of optimization heuristics for computer algorithms and iterative search techniques. Specific attention is given to the search concepts known collectively as intelligent perturbation algorithms (IPAs) and their application to crew/resource allocation problems. IPAs iteratively examine successive schedules which become progressively more efficient, and the characteristics of good perturbation operators are listed. IPAs can be applied to aerospace systems to efficiently utilize crews, payloads, and resources in the context of systems such as Space-Station scheduling. A program is presented called the MFIVE Space Station Scheduling Worksheet which generates task assignments and resource usage structures. The IPAs can be used to develop flexible manifesting and scheduling for the Industrial Space Facility.
Microscale truss optimization using genetic algorithm
María Belén Prendes-Gero; Jean-Marc Drouet
2011-01-01
This paper describes the development of a genetic algorithm that is capable of optimizing the mass of micro-scale trusses.\\u000a Belonging to the group of periodic cellular materials, micro-scale trusses are characterized by the creation of a base cell\\u000a with a pattern that is repeated in space until a global structure is obtained. Investigation in this field has generally been\\u000a focused
Multidisciplinary design optimization using genetic algorithms
NASA Technical Reports Server (NTRS)
Unal, Resit
1994-01-01
Multidisciplinary design optimization (MDO) is an important step in the conceptual design and evaluation of launch vehicles since it can have a significant impact on performance and life cycle cost. The objective is to search the system design space to determine values of design variables that optimize the performance characteristic subject to system constraints. Gradient-based optimization routines have been used extensively for aerospace design optimization. However, one limitation of gradient based optimizers is their need for gradient information. Therefore, design problems which include discrete variables can not be studied. Such problems are common in launch vehicle design. For example, the number of engines and material choices must be integer values or assume only a few discrete values. In this study, genetic algorithms are investigated as an approach to MDO problems involving discrete variables and discontinuous domains. Optimization by genetic algorithms (GA) uses a search procedure which is fundamentally different from those gradient based methods. Genetic algorithms seek to find good solutions in an efficient and timely manner rather than finding the best solution. GA are designed to mimic evolutionary selection. A population of candidate designs is evaluated at each iteration, and each individual's probability of reproduction (existence in the next generation) depends on its fitness value (related to the value of the objective function). Progress toward the optimum is achieved by the crossover and mutation operations. GA is attractive since it uses only objective function values in the search process, so gradient calculations are avoided. Hence, GA are able to deal with discrete variables. Studies report success in the use of GA for aircraft design optimization studies, trajectory analysis, space structure design and control systems design. In these studies reliable convergence was achieved, but the number of function evaluations was large compared with efficient gradient methods. Applicaiton of GA is underway for a cost optimization study for a launch-vehicle fuel-tank and structural design of a wing. The strengths and limitations of GA for launch vehicle design optimization is studied.
Algorithms for Marketing-Mix Optimization
Gudmundsson, Joachim; Smid, Michiel
2009-01-01
Algorithms for determining quality/cost/price tradeoffs in saturated markets are considered. A product is modeled by $d$ real-valued qualities whose sum determines the unit cost of producing the product. This leads to the following optimization problem: given a set of $n$ customers, each of whom has certain minimum quality requirements and a maximum price they are willing to pay, design a new product and select a price for that product in order to maximize the resulting profit. An $O(n\\log n)$ time algorithm is given for the case, $d=1$, of linear products, and $O(n(\\log n)^{d+1})$ time approximation algorithms are given for products with any constant number, $d$, of qualities. To achieve the latter result, an $O(nk^{d-1})$ bound on the complexity of an arrangement of homothetic simplices in $\\R^d$ is given, where $k$ is the maximum number of simplices that all contain a single points.
Automated design of multiphase space missions using hybrid optimal control
NASA Astrophysics Data System (ADS)
Chilan, Christian Miguel
A modern space mission is assembled from multiple phases or events such as impulsive maneuvers, coast arcs, thrust arcs and planetary flybys. Traditionally, a mission planner would resort to intuition and experience to develop a sequence of events for the multiphase mission and to find the space trajectory that minimizes propellant use by solving the associated continuous optimal control problem. This strategy, however, will most likely yield a sub-optimal solution, as the problem is sophisticated for several reasons. For example, the number of events in the optimal mission structure is not known a priori and the system equations of motion change depending on what event is current. In this work a framework for the automated design of multiphase space missions is presented using hybrid optimal control (HOC). The method developed uses two nested loops: an outer-loop that handles the discrete dynamics and finds the optimal mission structure in terms of the categorical variables, and an inner-loop that performs the optimization of the corresponding continuous-time dynamical system and obtains the required control history. Genetic algorithms (GA) and direct transcription with nonlinear programming (NLP) are introduced as methods of solution for the outer-loop and inner-loop problems, respectively. Automation of the inner-loop, continuous optimal control problem solver, required two new technologies. The first is a method for the automated construction of the NLP problems resulting from the use of a direct solver for systems with different structures, including different numbers of categorical events. The method assembles modules, consisting of parameters and constraints appropriate to each event, sequentially according to the given mission structure. The other new technology is for a robust initial guess generator required by the inner-loop NLP problem solver. Two new methods were developed for cases including low-thrust trajectories. The first method, based on GA, approximates optimal control histories by incorporating boundary conditions explicitly using a conditional penalty function. The second method, feasible region analysis, is based on GA and NLP; the GA approximates the optimal boundary points of low-thrust arcs while NLP finds the required control histories. The solution of two representative multiphase space mission design problems shows the effectiveness of the methods developed.
Optical flow optimization using parallel genetic algorithm
NASA Astrophysics Data System (ADS)
Zavala-Romero, Olmo; Botella, Guillermo; Meyer-Bäse, Anke; Meyer Base, Uwe
2011-06-01
A new approach to optimize the parameters of a gradient-based optical flow model using a parallel genetic algorithm (GA) is proposed. The main characteristics of the optical flow algorithm are its bio-inspiration and robustness against contrast, static patterns and noise, besides working consistently with several optical illusions where other algorithms fail. This model depends on many parameters which conform the number of channels, the orientations required, the length and shape of the kernel functions used in the convolution stage, among many more. The GA is used to find a set of parameters which improve the accuracy of the optical flow on inputs where the ground-truth data is available. This set of parameters helps to understand which of them are better suited for each type of inputs and can be used to estimate the parameters of the optical flow algorithm when used with videos that share similar characteristics. The proposed implementation takes into account the embarrassingly parallel nature of the GA and uses the OpenMP Application Programming Interface (API) to speedup the process of estimating an optimal set of parameters. The information obtained in this work can be used to dynamically reconfigure systems, with potential applications in robotics, medical imaging and tracking.
Genetic algorithm optimization for aerospace electromagnetic design and analysis
J. Michael Johnson; Yahya Rahmat-Samii
1996-01-01
This paper provides a tutorial overview of a new approach to optimization for aerospace electromagnetics known as the Genetic Algorithm. Genetic Algorithm (GA) optimizers are robust, stochastic search methods modeled on the concepts of natural selection and evolution. The relationship between traditional optimization techniques and GA is discussed and the details of GA optimization implementation are explored. The tutorial overview
Global Tree Optimization: A Nongreedy Decision Tree Algorithm
Mitchell, John E.
Global Tree Optimization: A Nongreedy Decision Tree Algorithm Kristin P. Bennett Email bennekgreedy approach for constructing globally optimal multivariate decision trees with fixed structure is pro posed. Previous greedy tree construction algorithms are locally optimal in that they optimize some splitting crite
Algorithms for optimizing CT fluence control
NASA Astrophysics Data System (ADS)
Hsieh, Scott S.; Pelc, Norbert J.
2014-03-01
The ability to customize the incident x-ray fluence in CT via beam-shaping filters or mA modulation is known to improve image quality and/or reduce radiation dose. Previous work has shown that complete control of x-ray fluence (ray-by-ray fluence modulation) would further improve dose efficiency. While complete control of fluence is not currently possible, emerging concepts such as dynamic attenuators and inverse-geometry CT allow nearly complete control to be realized. Optimally using ray-by-ray fluence modulation requires solving a very high-dimensional optimization problem. Most optimization techniques fail or only provide approximate solutions. We present efficient algorithms for minimizing mean or peak variance given a fixed dose limit. The reductions in variance can easily be translated to reduction in dose, if the original variance met image quality requirements. For mean variance, a closed form solution is derived. The peak variance problem is recast as iterated, weighted mean variance minimization, and at each iteration it is possible to bound the distance to the optimal solution. We apply our algorithms in simulations of scans of the thorax and abdomen. Peak variance reductions of 45% and 65% are demonstrated in the abdomen and thorax, respectively, compared to a bowtie filter alone. Mean variance shows smaller gains (about 15%).
Aerodynamic Shape Design of Nozzles Using a Hybrid Optimization Method
Xing, X.Q.
A hybrid design optimization method combining the stochastic method based on simultaneous perturbation stochastic approximation (SPSA) and the deterministic method of Broydon-Fletcher-Goldfarb-Shanno (BFGS) is developed ...
Optimal Power Management for a Hydraulic Hybrid Delivery Truck
Peng, Huei
, the availability of new technologies for improved fuel economy is somewhat limited compared to passenger cars, dueOptimal Power Management for a Hydraulic Hybrid Delivery Truck BIN WU, CHAN-CHIAO LIN, ZORAN FILIPI1 , HUEI PENG AND DENNIS ASSANIS SUMMARY Hydraulic hybrid propulsion and energy storage components
Optimal Scheduling for Energy Harvesting Transmitters with Hybrid Energy Storage
Ulukus, Sennur
Optimal Scheduling for Energy Harvesting Transmitters with Hybrid Energy Storage Omur Ozel Khurram with an energy harvesting transmitter which has a hybrid energy storage unit composed of a perfectly efficient super-capacitor (SC) and an inefficient battery. The SC has finite space for energy storage while
A new algorithm for general multiobjective optimization
NASA Technical Reports Server (NTRS)
Sobieszczanski-Sobieski, Jaroslaw; Dovi, Augustine R.; Wrenn, Gregory A.
1988-01-01
Described is a new technique for converting a constrained optimization problem to an unconstrained one, and a new method for multiobjective optimization based on that technique. The technique transforms the objective functions into goal constraints. The goal constraints are appended to the set of behavior constraints and the envelope of all functions in the set is searched for an unconstrained minimum. The technique may be categorized as a SUMT algorithm. In multiobjective applications, the approach has the advantage of locating a compromise minimum without the need to optimize for each individual objective function separately. The constrained to unconstrained conversion is described, followed by a description of the multiobjective problem. Two example problems are presented to demonstrate the robustness of the method.
Genetic Local Search Algorithm for Optimization Design of Diffractive Optical Elements
NASA Astrophysics Data System (ADS)
Zhou, Guangya; Chen, Yixin; Wang, Zongguang; Song, Hongwei
1999-07-01
We propose a genetic local search algorithm (GLSA) for the optimization design of diffractive optical elements (DOE s). This hybrid algorithm incorporates advantages of both genetic algorithm (GA) and local search techniques. It appears better able to locate the global minimum compared with a canonical GA. Sample cases investigated here include the optimization design of binary-phase Dammann gratings, continuous surface-relief grating array generators, and a uniform top-hat focal plane intensity profile generator. Two GLSA s whose incorporated local search techniques are the hill-climbing method and the simulated annealing algorithm are investigated. Numerical experimental results demonstrate that the proposed algorithm is highly efficient and robust. DOE s that have high diffraction efficiency and excellent uniformity can be achieved by use of the algorithm we propose.
A numerical study of hybrid optimization methods for the molecular conformation problems
Meza, J.C.; Martinez, M.L.
1993-05-01
An important area of research in computational biochemistry is the design of molecules for specific applications. The design of these molecules depends on the accurate determination of their three-dimensional structure or conformation. Under the assumption that molecules will settle into a configuration for which their energy is at a minimum, this design problem can be formulated as a global optimization problem. The solution of the molecular conformation problem can then be obtained, at least in principle, through any number of optimization algorithms. Unfortunately, it can easily be shown that there exist a large number of local minima for most molecules which makes this an extremely difficult problem for any standard optimization method. In this study, we present results for various optimization algorithms applied to a molecular conformation problem. We include results for genetic algorithms, simulated annealing, direct search methods, and several gradient methods. The major result of this study is that none of these standard methods can be used in isolation to efficiently generate minimum energy configurations. We propose instead several hybrid methods that combine properties of several local optimization algorithms. These hybrid methods have yielded better results on representative test problems than single methods.
Bell-Curve Based Evolutionary Optimization Algorithm
NASA Technical Reports Server (NTRS)
Sobieszczanski-Sobieski, J.; Laba, K.; Kincaid, R.
1998-01-01
The paper presents an optimization algorithm that falls in the category of genetic, or evolutionary algorithms. While the bit exchange is the basis of most of the Genetic Algorithms (GA) in research and applications in America, some alternatives, also in the category of evolutionary algorithms, but use a direct, geometrical approach have gained popularity in Europe and Asia. The Bell-Curve Based Evolutionary Algorithm (BCB) is in this alternative category and is distinguished by the use of a combination of n-dimensional geometry and the normal distribution, the bell-curve, in the generation of the offspring. The tool for creating a child is a geometrical construct comprising a line connecting two parents and a weighted point on that line. The point that defines the child deviates from the weighted point in two directions: parallel and orthogonal to the connecting line, the deviation in each direction obeying a probabilistic distribution. Tests showed satisfactory performance of BCB. The principal advantage of BCB is its controllability via the normal distribution parameters and the geometrical construct variables.
An Improved PSO Algorithm to Optimize BP Neural Network
Qing Chen; Wei Guo; Cuihong Li
2009-01-01
This paper presents a new BP neural network algorithm which is based on an improved particle swarm optimization (PSO) algorithm. The improved PSO (which is called IPSO) algorithm adopts adaptive inertia weight and acceleration coefficients to significantly improve the performance of the original PSO algorithm in global search and fine-tuning of the solutions. This study uses the IPSO algorithm to
Registration of range data using a hybrid simulated annealing and iterative closest point algorithm
LUCK,JASON; LITTLE,CHARLES Q.; HOFF,WILLIAM
2000-04-17
The need to register data is abundant in applications such as: world modeling, part inspection and manufacturing, object recognition, pose estimation, robotic navigation, and reverse engineering. Registration occurs by aligning the regions that are common to multiple images. The largest difficulty in performing this registration is dealing with outliers and local minima while remaining efficient. A commonly used technique, iterative closest point, is efficient but is unable to deal with outliers or avoid local minima. Another commonly used optimization algorithm, simulated annealing, is effective at dealing with local minima but is very slow. Therefore, the algorithm developed in this paper is a hybrid algorithm that combines the speed of iterative closest point with the robustness of simulated annealing. Additionally, a robust error function is incorporated to deal with outliers. This algorithm is incorporated into a complete modeling system that inputs two sets of range data, registers the sets, and outputs a composite model.
Two Hybrid Algorithms for Multiple Sequence Alignment
NASA Astrophysics Data System (ADS)
Naznin, Farhana; Sarker, Ruhul; Essam, Daryl
2010-01-01
In order to design life saving drugs, such as cancer drugs, the design of Protein or DNA structures has to be accurate. These structures depend on Multiple Sequence Alignment (MSA). MSA is used to find the accurate structure of Protein and DNA sequences from existing approximately correct sequences. To overcome the overly greedy nature of the well known global progressive alignment method for multiple sequence alignment, we have proposed two different algorithms in this paper; one is using an iterative approach with a progressive alignment method (PAMIM) and the second one is using a genetic algorithm with a progressive alignment method (PAMGA). Both of our methods started with a "kmer" distance table to generate single guide-tree. In the iterative approach, we have introduced two new techniques: the first technique is to generate Guide-trees with randomly selected sequences and the second is of shuffling the sequences inside that tree. The output of the tree is a multiple sequence alignment which has been evaluated by the Sum of Pairs Method (SPM) considering the real value data from PAM250. In our second GA approach, these two techniques are used to generate an initial population and also two different approaches of genetic operators are implemented in crossovers and mutation. To test the performance of our two algorithms, we have compared these with the existing well known methods: T-Coffee, MUSCEL, MAFFT and Probcon, using BAliBase benchmarks. The experimental results show that the first algorithm works well for some situations, where other existing methods face difficulties in obtaining better solutions. The proposed second method works well compared to the existing methods for all situations and it shows better performance over the first one.
Distributed Algorithms for Optimal Power Flow Problem
Lam, Albert Y S; Tse, David
2011-01-01
Optimal power flow (OPF) is an important problem for power generation and it is in general non-convex. With the employment of renewable energy, it will be desirable if OPF can be solved very efficiently so its solution can be used in real time. With some special network structure, e.g. trees, the problem has been shown to have a zero duality gap and the convex dual problem yields the optimal solution. In this paper, we propose a primal and a dual algorithm to coordinate the smaller subproblems decomposed from the convexified OPF. We can arrange the subproblems to be solved sequentially and cumulatively in a central node or solved in parallel in distributed nodes. We test the algorithms on IEEE radial distribution test feeders, some random tree-structured networks, and the IEEE transmission system benchmarks. Simulation results show that the computation time can be improved dramatically with our algorithms over the centralized approach of solving the problem without decomposition, especially in tree-structured...
Intervals in evolutionary algorithms for global optimization
Patil, R.B.
1995-05-01
Optimization is of central concern to a number of disciplines. Interval Arithmetic methods for global optimization provide us with (guaranteed) verified results. These methods are mainly restricted to the classes of objective functions that are twice differentiable and use a simple strategy of eliminating a splitting larger regions of search space in the global optimization process. An efficient approach that combines the efficient strategy from Interval Global Optimization Methods and robustness of the Evolutionary Algorithms is proposed. In the proposed approach, search begins with randomly created interval vectors with interval widths equal to the whole domain. Before the beginning of the evolutionary process, fitness of these interval parameter vectors is defined by evaluating the objective function at the center of the initial interval vectors. In the subsequent evolutionary process the local optimization process returns an estimate of the bounds of the objective function over the interval vectors. Though these bounds may not be correct at the beginning due to large interval widths and complicated function properties, the process of reducing interval widths over time and a selection approach similar to simulated annealing helps in estimating reasonably correct bounds as the population evolves. The interval parameter vectors at these estimated bounds (local optima) are then subjected to crossover and mutation operators. This evolutionary process continues for predetermined number of generations in the search of the global optimum.
Unification of algorithms for minimum mode optimization.
Zeng, Yi; Xiao, Penghao; Henkelman, Graeme
2014-01-28
Minimum mode following algorithms are widely used for saddle point searching in chemical and material systems. Common to these algorithms is a component to find the minimum curvature mode of the second derivative, or Hessian matrix. Several methods, including Lanczos, dimer, Rayleigh-Ritz minimization, shifted power iteration, and locally optimal block preconditioned conjugate gradient, have been proposed for this purpose. Each of these methods finds the lowest curvature mode iteratively without calculating the Hessian matrix, since the full matrix calculation is prohibitively expensive in the high dimensional spaces of interest. Here we unify these iterative methods in the same theoretical framework using the concept of the Krylov subspace. The Lanczos method finds the lowest eigenvalue in a Krylov subspace of increasing size, while the other methods search in a smaller subspace spanned by the set of previous search directions. We show that these smaller subspaces are contained within the Krylov space for which the Lanczos method explicitly finds the lowest curvature mode, and hence the theoretical efficiency of the minimum mode finding methods are bounded by the Lanczos method. Numerical tests demonstrate that the dimer method combined with second-order optimizers approaches but does not exceed the efficiency of the Lanczos method for minimum mode optimization. PMID:25669513
A Hybrid Swarm Intelligence Algorithm for Intrusion Detection Using Significant Features
Amudha, P.; Karthik, S.; Sivakumari, S.
2015-01-01
Intrusion detection has become a main part of network security due to the huge number of attacks which affects the computers. This is due to the extensive growth of internet connectivity and accessibility to information systems worldwide. To deal with this problem, in this paper a hybrid algorithm is proposed to integrate Modified Artificial Bee Colony (MABC) with Enhanced Particle Swarm Optimization (EPSO) to predict the intrusion detection problem. The algorithms are combined together to find out better optimization results and the classification accuracies are obtained by 10-fold cross-validation method. The purpose of this paper is to select the most relevant features that can represent the pattern of the network traffic and test its effect on the success of the proposed hybrid classification algorithm. To investigate the performance of the proposed method, intrusion detection KDDCup'99 benchmark dataset from the UCI Machine Learning repository is used. The performance of the proposed method is compared with the other machine learning algorithms and found to be significantly different. PMID:26221625
The Ordered Clustered Travelling Salesman Problem: A Hybrid Genetic Algorithm
Ahmed, Zakir Hussain
2014-01-01
The ordered clustered travelling salesman problem is a variation of the usual travelling salesman problem in which a set of vertices (except the starting vertex) of the network is divided into some prespecified clusters. The objective is to find the least cost Hamiltonian tour in which vertices of any cluster are visited contiguously and the clusters are visited in the prespecified order. The problem is NP-hard, and it arises in practical transportation and sequencing problems. This paper develops a hybrid genetic algorithm using sequential constructive crossover, 2-opt search, and a local search for obtaining heuristic solution to the problem. The efficiency of the algorithm has been examined against two existing algorithms for some asymmetric and symmetric TSPLIB instances of various sizes. The computational results show that the proposed algorithm is very effective in terms of solution quality and computational time. Finally, we present solution to some more symmetric TSPLIB instances. PMID:24701148
Comparison of optimization algorithms in intensity-modulated radiation therapy planning
NASA Astrophysics Data System (ADS)
Kendrick, Rachel
Intensity-modulated radiation therapy is used to better conform the radiation dose to the target, which includes avoiding healthy tissue. Planning programs employ optimization methods to search for the best fluence of each photon beam, and therefore to create the best treatment plan. The Computational Environment for Radiotherapy Research (CERR), a program written in MATLAB, was used to examine some commonly-used algorithms for one 5-beam plan. Algorithms include the genetic algorithm, quadratic programming, pattern search, constrained nonlinear optimization, simulated annealing, the optimization method used in Varian EclipseTM, and some hybrids of these. Quadratic programing, simulated annealing, and a quadratic/simulated annealing hybrid were also separately compared using different prescription doses. The results of each dose-volume histogram as well as the visual dose color wash were used to compare the plans. CERR's built-in quadratic programming provided the best overall plan, but avoidance of the organ-at-risk was rivaled by other programs. Hybrids of quadratic programming with some of these algorithms seems to suggest the possibility of better planning programs, as shown by the improved quadratic/simulated annealing plan when compared to the simulated annealing algorithm alone. Further experimentation will be done to improve cost functions and computational time.
An improved hybrid global optimization method for protein tertiary structure prediction
McAllister, Scott R.
2009-01-01
First principles approaches to the protein structure prediction problem must search through an enormous conformational space to identify low-energy, near-native structures. In this paper, we describe the formulation of the tertiary structure prediction problem as a nonlinear constrained minimization problem, where the goal is to minimize the energy of a protein conformation subject to constraints on torsion angles and interatomic distances. The core of the proposed algorithm is a hybrid global optimization method that combines the benefits of the ?BB deterministic global optimization approach with conformational space annealing. These global optimization techniques employ a local minimization strategy that combines torsion angle dynamics and rotamer optimization to identify and improve the selection of initial conformations and then applies a sequential quadratic programming approach to further minimize the energy of the protein conformations subject to constraints. The proposed algorithm demonstrates the ability to identify both lower energy protein structures, as well as larger ensembles of low-energy conformations. PMID:20357906
Global optimization algorithm for heat exchanger networks
Quesada, I.; Grossmann, I.E. (Carnegie Mellon Univ., Pittsburgh, PA (United States))
1993-03-01
This paper deals with the global optimization of heat exchanger networks with fixed topology. It is shown that if linear area cost functions are assumed, as well as arithmetic mean driving force temperature differences in networks with isothermal mixing, the corresponding nonlinear programming (NLP) optimization problem involves linear constraints and a sum of linear fractional functions in the objective which are nonconvex. A rigorous algorithm is proposed that is based on a convex NLP underestimator that involves linear and nonlinear estimators for fractional and bilinear terms which provide a tight lower bound to the global optimum. This NLP problem is used within a spatial branch and bound method for which branching rules are given. Basic properties of the proposed method are presented, and its application is illustrated with several example problems. The results show that the proposed method only requires few nodes in the branch and bound search.
Optimal sizing study of hybrid wind/PV/diesel power generation unit
Belfkira, Rachid; Zhang, Lu; Barakat, Georges [Groupe de Recherche en Electrotechnique et Automatique du Havre, University of Le Havre, 25 rue Philippe Lebon, BP 1123, 76063 Le Havre (France)
2011-01-15
In this paper, a methodology of sizing optimization of a stand-alone hybrid wind/PV/diesel energy system is presented. This approach makes use of a deterministic algorithm to suggest, among a list of commercially available system devices, the optimal number and type of units ensuring that the total cost of the system is minimized while guaranteeing the availability of the energy. The collection of 6 months of data of wind speed, solar radiation and ambient temperature recorded for every hour of the day were used. The mathematical modeling of the main elements of the hybrid wind/PV/diesel system is exposed showing the more relevant sizing variables. A deterministic algorithm is used to minimize the total cost of the system while guaranteeing the satisfaction of the load demand. A comparison between the total cost of the hybrid wind/PV/diesel energy system with batteries and the hybrid wind/PV/diesel energy system without batteries is presented. The reached results demonstrate the practical utility of the used sizing methodology and show the influence of the battery storage on the total cost of the hybrid system. (author)
Optimal sizing and selection of hybrid electric vehicle components
Robert A. Weinstock; Philip T. Krein; Robert A. White
1993-01-01
Proper execution of a successful hybrid electric vehicle (HEV) design for transportation applications requires optimal sizing of key mechanical, electrical, and power electronic components. An active program for HEV development is described. The basic objective of an HEV design is to match the performance of a standard automobile while drastically reducing emissions. Constraints imposed while optimizing critical component selection are:
Lunar Habitat Optimization Using Genetic Algorithms
NASA Technical Reports Server (NTRS)
SanScoucie, M. P.; Hull, P. V.; Tinker, M. L.; Dozier, G. V.
2007-01-01
Long-duration surface missions to the Moon and Mars will require bases to accommodate habitats for the astronauts. Transporting the materials and equipment required to build the necessary habitats is costly and difficult. The materials chosen for the habitat walls play a direct role in protection against each of the mentioned hazards. Choosing the best materials, their configuration, and the amount required is extremely difficult due to the immense size of the design region. Clearly, an optimization method is warranted for habitat wall design. Standard optimization techniques are not suitable for problems with such large search spaces; therefore, a habitat wall design tool utilizing genetic algorithms (GAs) has been developed. GAs use a "survival of the fittest" philosophy where the most fit individuals are more likely to survive and reproduce. This habitat design optimization tool is a multiobjective formulation of up-mass, heat loss, structural analysis, meteoroid impact protection, and radiation protection. This Technical Publication presents the research and development of this tool as well as a technique for finding the optimal GA search parameters.
Testing trivializing maps in the Hybrid Monte Carlo algorithm
Georg P. Engel; Stefan Schaefer
2011-02-09
We test a recent proposal to use approximate trivializing maps in a field theory to speed up Hybrid Monte Carlo simulations. Simulating the CP^{N-1} model, we find a small improvement with the leading order transformation, which is however compensated by the additional computational overhead. The scaling of the algorithm towards the continuum is not changed. In particular, the effect of the topological modes on the autocorrelation times is studied.
An integrated evolutionary algorithm for expensive global optimization
Changtong Luo; Chun Wang; Zonglin Jiang; Shao-Liang Zhang
2010-01-01
We propose an integrated algorithm named low dimensional simplex evolution extension (LDSEE) for expensive global optimization in which only a very limited number of function evaluations is allowed. The new algorithm accelerates an existing global optimization, low dimensional simplex evolution (LDSE), by using radial basis function (RBF) interpolation and tabu search. Different from other expensive global optimization methods, LDSEE integrates
A metamodel-assisted evolutionary algorithm for expensive optimization
Changtong Luo; Shao-Liang Zhang; Chun Wang; Zonglin Jiang
2011-01-01
Expensive optimization aims to find the global minimum of a given function within a very limited number of function evaluations. It has drawn much attention in recent years. The present expensive optimization algorithms focus their attention on metamodeling techniques, and call existing global optimization algorithms as subroutines. So it is difficult for them to keep a good balance between model
Stand-alone hybrid wind-photovoltaic power generation systems optimal sizing
NASA Astrophysics Data System (ADS)
Cr?ciunescu, Aurelian; Popescu, Claudia; Popescu, Mihai; Florea, Leonard Marin
2013-10-01
Wind and photovoltaic energy resources have attracted energy sectors to generate power on a large scale. A drawback, common to these options, is their unpredictable nature and dependence on day time and meteorological conditions. Fortunately, the problems caused by the variable nature of these resources can be partially overcome by integrating the two resources in proper combination, using the strengths of one source to overcome the weakness of the other. The hybrid systems that combine wind and solar generating units with battery backup can attenuate their individual fluctuations and can match with the power requirements of the beneficiaries. In order to efficiently and economically utilize the hybrid energy system, one optimum match design sizing method is necessary. In this way, literature offers a variety of methods for multi-objective optimal designing of hybrid wind/photovoltaic (WG/PV) generating systems, one of the last being genetic algorithms (GA) and particle swarm optimization (PSO). In this paper, mathematical models of hybrid WG/PV components and a short description of the last proposed multi-objective optimization algorithms are given.
Stochastic Optimal Control for Series Hybrid Electric Vehicles
Malikopoulos, Andreas [ORNL] [ORNL
2013-01-01
Increasing demand for improving fuel economy and reducing emissions has stimulated significant research and investment in hybrid propulsion systems. In this paper, we address the problem of optimizing online the supervisory control in a series hybrid configuration by modeling its operation as a controlled Markov chain using the average cost criterion. We treat the stochastic optimal control problem as a dual constrained optimization problem. We show that the control policy that yields higher probability distribution to the states with low cost and lower probability distribution to the states with high cost is an optimal control policy, defined as an equilibrium control policy. We demonstrate the effectiveness of the efficiency of the proposed controller in a series hybrid configuration and compare it with a thermostat-type controller.
Local optimality of dictionary learning algorithms Boris Mailh
Plumbley, Mark
Local optimality of dictionary learning algorithms Boris Mailhé Centre for Digital Music School dictionary learning algorithms. We focus on three algorithms: the Olshausen and Field algorithm (Ols-DLA) [1 matrix of training data. We consider the following dictionary learning problem min ,X S - X 2 2 (1
NASA Astrophysics Data System (ADS)
Khatibinia, Mohsen; Sadegh Naseralavi, Seyed
2014-12-01
Structural optimization on shape and sizing with frequency constraints is well-known as a highly nonlinear dynamic optimization problem with several local optimum solutions. Hence, efficient optimization algorithms should be utilized to solve this problem. In this study, orthogonal multi-gravitational search algorithm (OMGSA) as a meta-heuristic algorithm is introduced to solve truss optimization on shape and sizing with frequency constraints. The OMGSA is a hybrid approach based on a combination of multi-gravitational search algorithm (multi-GSA) and an orthogonal crossover (OC). In multi-GSA, the population is split into several sub-populations. Then, each sub-population is independently evaluated by an improved gravitational search algorithm (IGSA). Furthermore, the OC is used in the proposed OMGSA in order to find and exploit the global solution in the search space. The capability of OMGSA is demonstrated through six benchmark examples. Numerical results show that the proposed OMGSA outperform the other optimization techniques.
Expedite Particle Swarm Optimization Algorithm (EPSO) for Optimization of MSA
NASA Astrophysics Data System (ADS)
Rathi, Amit; Vijay, Ritu
This paper presents a new designing method of Rectangular patch Microstrip Antenna using an Artificial searches Algorithm with some constraints. It requires two stages for designing. In first stage, bandwidth of MSA is modeled using bench Mark function. In second stage, output of first stage give to modified Artificial search Algorithm which is Particle Swarm Algorithm (PSO) as input and get output in the form of five parameter- dimensions width, frequency range, dielectric loss tangent, length over a ground plane with a substrate thickness and electrical thickness. In PSO Cognition, factor and Social learning Factor give very important effect on balancing the local search and global search in PSO. Basing the modification of cognition factor and social learning factor, this paper presents the strategy that at the starting process cognition-learning factor has more effect then social learning factor. Gradually social learning factor has more impact after learning cognition factor for find out global best. The aim is to find out under above circumstances these modifications in PSO can give better result for optimization of microstrip Antenna (MSA).
An Efficient Algorithm for Constructing Optimal Design of Computer Experiments
Chen, Wei
structural properties, e.g., the Latin hypercube designs (LHD) (McKay, et al., 1979) with good one the threshold accepting (TA) algorithm (essentially a variant of SA) in constructing optimal LHD. The optimal
Hybrid NN/SVM Computational System for Optimizing Designs
NASA Technical Reports Server (NTRS)
Rai, Man Mohan
2009-01-01
A computational method and system based on a hybrid of an artificial neural network (NN) and a support vector machine (SVM) (see figure) has been conceived as a means of maximizing or minimizing an objective function, optionally subject to one or more constraints. Such maximization or minimization could be performed, for example, to optimize solve a data-regression or data-classification problem or to optimize a design associated with a response function. A response function can be considered as a subset of a response surface, which is a surface in a vector space of design and performance parameters. A typical example of a design problem that the method and system can be used to solve is that of an airfoil, for which a response function could be the spatial distribution of pressure over the airfoil. In this example, the response surface would describe the pressure distribution as a function of the operating conditions and the geometric parameters of the airfoil. The use of NNs to analyze physical objects in order to optimize their responses under specified physical conditions is well known. NN analysis is suitable for multidimensional interpolation of data that lack structure and enables the representation and optimization of a succession of numerical solutions of increasing complexity or increasing fidelity to the real world. NN analysis is especially useful in helping to satisfy multiple design objectives. Feedforward NNs can be used to make estimates based on nonlinear mathematical models. One difficulty associated with use of a feedforward NN arises from the need for nonlinear optimization to determine connection weights among input, intermediate, and output variables. It can be very expensive to train an NN in cases in which it is necessary to model large amounts of information. Less widely known (in comparison with NNs) are support vector machines (SVMs), which were originally applied in statistical learning theory. In terms that are necessarily oversimplified to fit the scope of this article, an SVM can be characterized as an algorithm that (1) effects a nonlinear mapping of input vectors into a higher-dimensional feature space and (2) involves a dual formulation of governing equations and constraints. One advantageous feature of the SVM approach is that an objective function (which one seeks to minimize to obtain coefficients that define an SVM mathematical model) is convex, so that unlike in the cases of many NN models, any local minimum of an SVM model is also a global minimum.
Solar-Diesel Hybrid Power System Optimization and Experimental Validation
NASA Astrophysics Data System (ADS)
Jacobus, Headley Stewart
As of 2008 1.46 billion people, or 22 percent of the World's population, were without electricity. Many of these people live in remote areas where decentralized generation is the only method of electrification. Most mini-grids are powered by diesel generators, but new hybrid power systems are becoming a reliable method to incorporate renewable energy while also reducing total system cost. This thesis quantifies the measurable Operational Costs for an experimental hybrid power system in Sierra Leone. Two software programs, Hybrid2 and HOMER, are used during the system design and subsequent analysis. Experimental data from the installed system is used to validate the two programs and to quantify the savings created by each component within the hybrid system. This thesis bridges the gap between design optimization studies that frequently lack subsequent validation and experimental hybrid system performance studies.
A Hybrid Cellular Genetic Algorithm for Multi-objective Crew Scheduling Problem
NASA Astrophysics Data System (ADS)
Jolai, Fariborz; Assadipour, Ghazal
Crew scheduling is one of the important problems of the airline industry. This problem aims to cover a number of flights by crew members, such that all the flights are covered. In a robust scheduling the assignment should be so that the total cost, delays, and unbalanced utilization are minimized. As the problem is NP-hard and the objectives are in conflict with each other, a multi-objective meta-heuristic called CellDE, which is a hybrid cellular genetic algorithm, is implemented as the optimization method. The proposed algorithm provides the decision maker with a set of non-dominated or Pareto-optimal solutions, and enables them to choose the best one according to their preferences. A set of problems of different sizes is generated and solved using the proposed algorithm. Evaluating the performance of the proposed algorithm, three metrics are suggested, and the diversity and the convergence of the achieved Pareto front are appraised. Finally a comparison is made between CellDE and PAES, another meta-heuristic algorithm. The results show the superiority of CellDE.
Nonlinear inversion of potential-field data using a hybrid-encoding genetic algorithm
NASA Astrophysics Data System (ADS)
Chen, Chao; Xia, Jianghai; Liu, Jiangping; Feng, Guangding
2006-03-01
Using a genetic algorithm to solve an inverse problem of complex nonlinear geophysical equations is advantageous because it does not require computer gradients of models or "good" initial models. The multi-point search of a genetic algorithm makes it easier to find the globally optimal solution while avoiding falling into a local extremum. As is the case in other optimization approaches, the search efficiency for a genetic algorithm is vital in finding desired solutions successfully in a multi-dimensional model space. A binary-encoding genetic algorithm is hardly ever used to resolve an optimization problem such as a simple geophysical inversion with only three unknowns. The encoding mechanism, genetic operators, and population size of the genetic algorithm greatly affect search processes in the evolution. It is clear that improved operators and proper population size promote the convergence. Nevertheless, not all genetic operations perform perfectly while searching under either a uniform binary or a decimal encoding system. With the binary encoding mechanism, the crossover scheme may produce more new individuals than with the decimal encoding. On the other hand, the mutation scheme in a decimal encoding system will create new genes larger in scope than those in the binary encoding. This paper discusses approaches of exploiting the search potential of genetic operations in the two encoding systems and presents an approach with a hybrid-encoding mechanism, multi-point crossover, and dynamic population size for geophysical inversion. We present a method that is based on the routine in which the mutation operation is conducted in the decimal code and multi-point crossover operation in the binary code. The mix-encoding algorithm is called the hybrid-encoding genetic algorithm (HEGA). HEGA provides better genes with a higher probability by a mutation operator and improves genetic algorithms in resolving complicated geophysical inverse problems. Another significant result is that final solution is determined by the average model derived from multiple trials instead of one computation due to the randomness in a genetic algorithm procedure. These advantages were demonstrated by synthetic and real-world examples of inversion of potential-field data.
Optimization of Reactive Power based on Newton-Raphson algorithm
Lavaei, Javad
distribution and flow. Reactive power optimization focuses on mathematical models and optimization algorithms the reactive power optimization procedures. Introduction In modern power system transmission technology, reactive power optimization is so important that it has direct influence on the high quality and stable
Efficiencies and optimization of HMC algorithms in pure gauge theory
Bernd Gehrmann; Ulli Wolff
1999-08-04
As a prerequisite to dynamical fermion simulations a detailed study of optimal parameters and scaling behavior is conducted for the quenched Schr\\"odinger functional at fixed renormalized coupling. We compare standard hybrid overrelaxation techniques with local and global hybrid Monte Carlo. Our efficiency measure is designed to be directly relevant for the strong coupling constant as used by the ALPHA collaboration.
Generalized conflict learning for hybrid discrete/linear optimization
Li, Hui (Hui Xylo)
2005-01-01
Conflict-directed search algorithms have formed the core of practical, model-based reasoning systems for the last three decades. In many of these applications there is a series of discrete constraint optimization problems ...
Generalized Conflict Learning For Hybrid Discrete Linear Optimization
Li, Hui X.
2005-05-20
Conflict-directed search algorithms have formed the core of practical, model-based reasoning systems for the last three decades. In many of these applications there is a series of discrete constraint optimization problems ...
Hybrid Stochastic / Deterministic Optimization for Tracking Sports Players and Pedestrians
associations (Figure 1). We develop a hy- brid optimization algorithm that uses Reversible Jump Markov Chain Monte Carlo (RJMCMC) sampling over the space of detections to "drive" the estima- tion process, while
Honey Bees Inspired Optimization Method: The Bees Algorithm
Yuce, Baris; Packianather, Michael S.; Mastrocinque, Ernesto; Pham, Duc Truong; Lambiase, Alfredo
2013-01-01
Optimization algorithms are search methods where the goal is to find an optimal solution to a problem, in order to satisfy one or more objective functions, possibly subject to a set of constraints. Studies of social animals and social insects have resulted in a number of computational models of swarm intelligence. Within these swarms their collective behavior is usually very complex. The collective behavior of a swarm of social organisms emerges from the behaviors of the individuals of that swarm. Researchers have developed computational optimization methods based on biology such as Genetic Algorithms, Particle Swarm Optimization, and Ant Colony. The aim of this paper is to describe an optimization algorithm called the Bees Algorithm, inspired from the natural foraging behavior of honey bees, to find the optimal solution. The algorithm performs both an exploitative neighborhood search combined with random explorative search. In this paper, after an explanation of the natural foraging behavior of honey bees, the basic Bees Algorithm and its improved versions are described and are implemented in order to optimize several benchmark functions, and the results are compared with those obtained with different optimization algorithms. The results show that the Bees Algorithm offering some advantage over other optimization methods according to the nature of the problem.
NASA Technical Reports Server (NTRS)
Segenreich, S. A.; Mcintosh, S. C., Jr.
1975-01-01
A rigorous optimality criterion is derived and a hybrid weight-reduction algorithm developed for the weight minimization of lifting surfaces with a constraint on flutter speed. The weight-reduction algorithm incorporates a simple recursion formula derived from the optimality criterion. Monotonic weight reduction is accomplished by dynamically adjusting a parameter in the recursion formula so as to achieve a predetermined weight decrease. The algorithm thus combines the simplicity of optimality-criterion methods with the convergence characteristics of mathematical-programming methods. The imposition of the flutter constraint is simplified by forcing to zero the imaginary part of the flutter eigenvalue, with the airspeed fixed. Four examples are discussed. The results suggest that significant improvements in efficiency are possible, in comparison with techniques based purely on mathematical programming.
NASA Astrophysics Data System (ADS)
Ding, Zhe; Xu, Zhanqi; Zeng, Xiaodong; Ma, Tao; Yang, Fan
2014-04-01
By adopting the orthogonal frequency division multiplexing technology, spectrum-sliced elastic optical path networks can offer flexible bandwidth to each connection request and utilize the spectrum resources efficiently. The routing and spectrum assignment (RSA) problems in SLICE networks are solved by using heuristic algorithms in most prior studies and addressed by intelligent algorithms in few investigations. The performance of RSA algorithms can be further improved if we could combine such two types of algorithms. Therefore, we propose three hybrid RSA algorithms: DACE-GMSF, DACE-GLPF, and DACE-GEMkPSF, which are the combination of the heuristic algorithm and coevolution based on distance-adaptive policy. In the proposed algorithms, we first groom the connection requests, then sort the connection requests by using the heuristic algorithm (most subcarriers first, longest path first, and extended most k paths' slots first), and finally search the approximately optimal solution with the coevolutionary policy. We present a model of the RSA problem by using integral linear programming, and key elements in the proposed algorithms are addressed in detail. Simulations under three topologies show that the proposed hybrid RSA algorithms can save spectrum resources efficiently.
Hybrid and optical implementation of the Deutsch-Jozsa algorithm
Luis A. Garcia; Jagdish R. Luthra
2009-12-31
A hybrid model of the Deutsch-Jozsa algorithm is presented, inspired by the proposals of hybrid computation by S. Lloyd and P. van Loock et. al. The model is based on two observations made about both the discrete and continuous algorithms already available. First, the Fourier transform is a single-step operation in a continuous-variable (CV) setting. Additionally, any implementation of the oracle is nontrivial in both schemes. The steps of the computation are very similar to those in the CV algorithm, with the main difference being the way in which the qunats, or quantum units of analogic information, and the qubits interact in the oracle. Using both discrete and continuous states of light, linear devices, and photo-detection, an optical implementation of the oracle is proposed. For simplicity, infinitely squeezed states are used in the continuous register, whereas the optical qubit is encoded in the dual-rail logic of the KLM protocol. The initial assumption of ideal states as qunats will be dropped to study the effects of finite squeezing in the quality of the computation.
Hybrid and optical implementation of the Deutsch-Jozsa algorithm
Garcia, Luis A
2010-01-01
A hybrid model of the Deutsch-Jozsa algorithm is presented, inspired by the proposals of hybrid computation by S. Lloyd and P. van Loock et. al. The model is based on two observations made about both the discrete and continuous algorithms already available. First, the Fourier transform is a single-step operation in a continuous-variable (CV) setting. Additionally, any implementation of the oracle is nontrivial in both schemes. The steps of the computation are very similar to those in the CV algorithm, with the main difference being the way in which the qunats, or quantum units of analogic information, and the qubits interact in the oracle. Using both discrete and continuous states of light, linear devices, and photo-detection, an optical implementation of the oracle is proposed. For simplicity, infinitely squeezed states are used in the continuous register, whereas the optical qubit is encoded in the dual-rail logic of the KLM protocol. The initial assumption of ideal states as qunats will be dropped to study...
An Estimation of Distribution Particle Swarm Optimization Algorithm
Kent, University of
evaluations. 1 Introduction The first Particle Swarm Optimization (PSO) algorithm was introduced by Kennedy other population-based optimization algorithms, PSO is initialized with a population of complete multiplication operator. Clerc and Kennedy [2] introduced the concept of constriction in PSO. Since it is based
Disassembly sequence planning based on ant colony optimization algorithm
Xue Jun Fang; Qian Shao Hua; Zhang Yu Feng
2010-01-01
Disassembly precedence restriction matrix (DPRM) is constructed to describe the Disassembly precedence relation between components of mechanical products. Based on this disassembly matrix, feasible and reasonable transition range which satisfied precedence restriction relation can be derived correctly and completely. Then ant colony optimization (ACO) algorithm is applied to generate geometric feasible and optimal disassembly sequences. In this paper, ACO algorithm
Optimal Algorithms for Generating Discrete Random Variables with Changing Distributions
Mehlhorn, Kurt
Optimal Algorithms for Generating Discrete Random Variables with Changing Distributions T. Hagerup arithmetic and the floor function, 3. generating a uniformly distributed real number between 0 and 1 K. Mehlhorn I. Munro Abstract We give optimal algorithms for generating discrete random variables
Optimal Hypercube Algorithms for Labeled Images (Preliminary version)
Stout, Quentin F.
Optimal Hypercube Algorithms for Labeled Images (Preliminary version) Russ Miller Department. and Computer Science University of Michigan Ann Arbor, MI 48109-2122 USA Abstract--Optimal hypercube algorithms per processor on a fine-grained hypercube. A figure (i.e., connected component) is a maximally
NASA Astrophysics Data System (ADS)
La Foy, Roderick; Vlachos, Pavlos
2011-11-01
An optimally designed MLOS tomographic reconstruction algorithm for use in 3D PIV and PTV applications is analyzed. Using a set of optimized reconstruction parameters, the reconstructions produced by the MLOS algorithm are shown to be comparable to reconstructions produced by the MART algorithm for a range of camera geometries, camera numbers, and particle seeding densities. The resultant velocity field error calculated using PIV and PTV algorithms is further minimized by applying both pre and post processing to the reconstructed data sets.
Hybrid intelligent optimization methods for engineering problems
Yasin Volkan Pehlivanoglu
2010-01-01
The purpose of optimization is to obtain the best solution under certain conditions. There are numerous optimization methods because different problems need different solution methodologies; therefore, it is difficult to construct patterns. Also mathematical modeling of a natural phenomenon is almost based on differentials. Differential equations are constructed with relative increments among the factors related to yield. Therefore, the gradients
Optimal Control of Hybrid Electric Vehicles Based on Pontryagin's Minimum Principle
Peng, Huei
Optimal Control of Hybrid Electric Vehicles Based on Pontryagin's Minimum Principle Namwook Kim simulation for a power-split hybrid vehicle, the fuel economy of the hybrid vehicle using the control. INTRODUCTION he optimal control of HEVs (Hybrid Electric Vehicles) is an important topic not only because
Hybrid Unicast and Multicast Flow Control: A Linear Optimization Approach
Yousefi'zadeh, Homayoun; Fazel, Fatemeh; Jafarkhani, Hamid
2004-01-01
of “Flow Control Optimization Algorithm” is linear in termscontrol prob- lem is a convex optimization problem de?ned over a set of piecewise linearlinear programming schemes and water-?lling scheme re- spectively, our solutions to centralized and decentralized for- mulations of the ?ow control
Genetic Algorithms Applied to Multi-Objective Aerodynamic Shape Optimization
NASA Technical Reports Server (NTRS)
Holst, Terry L.
2004-01-01
A genetic algorithm approach suitable for solving multi-objective optimization problems is described and evaluated using a series of aerodynamic shape optimization problems. Several new features including two variations of a binning selection algorithm and a gene-space transformation procedure are included. The genetic algorithm is suitable for finding pareto optimal solutions in search spaces that are defined by any number of genes and that contain any number of local extrema. A new masking array capability is included allowing any gene or gene subset to be eliminated as decision variables from the design space. This allows determination of the effect of a single gene or gene subset on the pareto optimal solution. Results indicate that the genetic algorithm optimization approach is flexible in application and reliable. The binning selection algorithms generally provide pareto front quality enhancements and moderate convergence efficiency improvements for most of the problems solved.
Celik, Yuksel; Ulker, Erkan
2013-01-01
Marriage in honey bees optimization (MBO) is a metaheuristic optimization algorithm developed by inspiration of the mating and fertilization process of honey bees and is a kind of swarm intelligence optimizations. In this study we propose improved marriage in honey bees optimization (IMBO) by adding Levy flight algorithm for queen mating flight and neighboring for worker drone improving. The IMBO algorithm's performance and its success are tested on the well-known six unconstrained test functions and compared with other metaheuristic optimization algorithms. PMID:23935416
Celik, Yuksel; Ulker, Erkan
2013-01-01
Marriage in honey bees optimization (MBO) is a metaheuristic optimization algorithm developed by inspiration of the mating and fertilization process of honey bees and is a kind of swarm intelligence optimizations. In this study we propose improved marriage in honey bees optimization (IMBO) by adding Levy flight algorithm for queen mating flight and neighboring for worker drone improving. The IMBO algorithm's performance and its success are tested on the well-known six unconstrained test functions and compared with other metaheuristic optimization algorithms. PMID:23935416
An Exact Local Hybrid Monte Carlo Algorithm for Gauge Theories
A. D. Kennedy; K. M. Bitar
1993-11-16
We introduce a new Monte Carlo method for pure gauge theories. It is not intended for use with dynamical fermions. It belongs to the class of Local Hybrid Monte Carlo (LHMC) algorithms, which make use of the locality of the action by updating individual sites or links by following a classical mechanics trajectory in fictitious time. We choose to update a one-parameter subgroup of the gauge field on each link of the lattice, and the classical trajectory can be found in closed form in terms of elliptic functions for this case. We show that this gives an overrelaxation algorithm with a tunable parameter which, unlike some previous methods, does not require the numerical integration of the equations of motion.
On Optimal Scheduling Algorithms for Time-Shared Systems
Leonard Kleinrock; Arne A. Nilsson
1981-01-01
The problem of fmdlng those optimum scheduling algorithms for time-shared systems that mlmmize a cost function that depends on waiting time and required service time IS considered An optimality condmon which sometimes leads to infeasible algorithms is established The procedure is unproved upon by use of a mathematical programming technique but still does not always generate feasible algorithms. These results
An optimal online algorithm for metrical task systems
Allan Borodin; Nathan Linial; Michael E. Saks
1987-01-01
In practice, almost all dynamic systems require decisions to be made online, without full knowledge of their future impact on the system. We introduce a general model for the processing of sequences of tasks and develop a general online decision algorithm. We show that, for an important class of special cases, this algorithm is optimal among all online algorithms.Specifically, a
Using genetic algorithms to optimize controller parameters for HVAC systems
W. Huang; H. N. Lam
1997-01-01
This paper presents an adaptive learning algorithm based on genetic algorithms (GA) for automatic tuning of proportional, integral and derivative (PID) controllers in Heating Ventilating and Air Conditioning (HVAC) systems to achieve optimal performance. Genetic algorithms, which are search procedures based on the mechanics of Darwin's natural selection, are employed since they have been proved to be robust and efficient
Hybrid Model Predictive Control for Optimizing Gestational Weight Gain Behavioral Interventions
Dong, Yuwen; Rivera, Daniel E.; Downs, Danielle S.; Savage, Jennifer S.; Thomas, Diana M.; Collins, Linda M.
2013-01-01
Excessive gestational weight gain (GWG) represents a major public health issue. In this paper, we pursue a control engineering approach to the problem by applying model predictive control (MPC) algorithms to act as decision policies in the intervention for assigning optimal intervention dosages. The intervention components consist of education, behavioral modification and active learning. The categorical nature of the intervention dosage assignment problem dictates the need for hybrid model predictive control (HMPC) schemes, ultimately leading to improved outcomes. The goal is to design a controller that generates an intervention dosage sequence which improves a participant’s healthy eating behavior and physical activity to better control GWG. An improved formulation of self-regulation is also presented through the use of Internal Model Control (IMC), allowing greater flexibility in describing self-regulatory behavior. Simulation results illustrate the basic workings of the model and demonstrate the benefits of hybrid predictive control for optimized GWG adaptive interventions. PMID:24336314
Transonic Wing Shape Optimization Using a Genetic Algorithm
NASA Technical Reports Server (NTRS)
Holst, Terry L.; Pulliam, Thomas H.; Kwak, Dochan (Technical Monitor)
2002-01-01
A method for aerodynamic shape optimization based on a genetic algorithm approach is demonstrated. The algorithm is coupled with a transonic full potential flow solver and is used to optimize the flow about transonic wings including multi-objective solutions that lead to the generation of pareto fronts. The results indicate that the genetic algorithm is easy to implement, flexible in application and extremely reliable.
Towards Optimal Capacity Segmentation with Hybrid Cloud Pricing
Liang, Ben
@eecg.toronto.edu, bli@eecg.toronto.edu, liang@comm.utoronto.ca Abstract--Cloud resources are usually priced in multipleTowards Optimal Capacity Segmentation with Hybrid Cloud Pricing Wei Wang, Baochun Li, and Ben Liang markets with different service guarantees. For example, Amazon EC2 prices virtual instances under three
Optimizing qubit Hamiltonian parameter estimation algorithms using PSO
Alexandr Sergeevich; Stephen D. Bartlett
2012-06-18
We develop qubit Hamiltonian single parameter estimation techniques using a Bayesian approach. The algorithms considered are restricted to projective measurements in a fixed basis, and are derived under the assumption that the qubit measurement is much slower than the characteristic qubit evolution. We optimize a non-adaptive algorithm using particle swarm optimization (PSO) and compare with a previously-developed locally-optimal scheme.
An Optimization Framework for Dynamic Hybrid Energy Systems
Wenbo Du; Humberto E Garcia; Christiaan J.J. Paredis
2014-03-01
A computational framework for the efficient analysis and optimization of dynamic hybrid energy systems (HES) is developed. A microgrid system with multiple inputs and multiple outputs (MIMO) is modeled using the Modelica language in the Dymola environment. The optimization loop is implemented in MATLAB, with the FMI Toolbox serving as the interface between the computational platforms. Two characteristic optimization problems are selected to demonstrate the methodology and gain insight into the system performance. The first is an unconstrained optimization problem that optimizes the dynamic properties of the battery, reactor and generator to minimize variability in the HES. The second problem takes operating and capital costs into consideration by imposing linear and nonlinear constraints on the design variables. The preliminary optimization results obtained in this study provide an essential step towards the development of a comprehensive framework for designing HES.
Lazy skip-lists: An algorithm for fast hybridization-expansion quantum Monte Carlo
NASA Astrophysics Data System (ADS)
Sémon, P.; Yee, Chuck-Hou; Haule, Kristjan; Tremblay, A.-M. S.
2014-08-01
The solution of a generalized impurity model lies at the heart of electronic structure calculations with dynamical mean field theory. In the strongly correlated regime, the method of choice for solving the impurity model is the hybridization-expansion continuous-time quantum Monte Carlo (CT-HYB). Enhancements to the CT-HYB algorithm are critical for bringing new physical regimes within reach of current computational power. Taking advantage of the fact that the bottleneck in the algorithm is a product of hundreds of matrices, we present optimizations based on the introduction and combination of two concepts of more general applicability: (a) skip lists and (b) fast rejection of proposed configurations based on matrix bounds. Considering two very different test cases with d electrons, we find speedups of ˜25 up to ˜500 compared to the direct evaluation of the matrix product. Even larger speedups are likely with f electron systems and with clusters of correlated atoms.
Solving the vehicle routing problem by a hybrid meta-heuristic algorithm
NASA Astrophysics Data System (ADS)
Yousefikhoshbakht, Majid; Khorram, Esmaile
2012-08-01
The vehicle routing problem (VRP) is one of the most important combinational optimization problems that has nowadays received much attention because of its real application in industrial and service problems. The VRP involves routing a fleet of vehicles, each of them visiting a set of nodes such that every node is visited by exactly one vehicle only once. So, the objective is to minimize the total distance traveled by all the vehicles. This paper presents a hybrid two-phase algorithm called sweep algorithm (SW) + ant colony system (ACS) for the classical VRP. At the first stage, the VRP is solved by the SW, and at the second stage, the ACS and 3-opt local search are used for improving the solutions. Extensive computational tests on standard instances from the literature confirm the effectiveness of the presented approach.
Cost of the Generalised Hybrid Monte Carlo Algorithm for Free Field Theory
A. D. Kennedy; Brian Pendleton
2000-08-21
We study analytically the computational cost of the Generalised Hybrid Monte Carlo (GHMC) algorithm for free field theory. We calculate the Metropolis acceptance probability for leapfrog and higher-order discretisations of the Molecular Dynamics (MD) equations of motion. We show how to calculate autocorrelation functions of arbitrary polynomial operators, and use these to optimise the GHMC momentum mixing angle, the trajectory length, and the integration stepsize for the special cases of linear and quadratic operators. We show that long trajectories are optimal for GHMC, and that standard HMC is more efficient than algorithms based on Second Order Langevin Monte Carlo (L2MC), sometimes known as Kramers Equation. We show that contrary to naive expectations HMC and L2MC have the same volume dependence, but their dynamical critical exponents are z = 1 and z = 3/2 respectively.
A Hybrid Metaheuristic for Biclustering Based on Scatter Search and Genetic Algorithms
NASA Astrophysics Data System (ADS)
Nepomuceno, Juan A.; Troncoso, Alicia; Aguilar–Ruiz, Jesús S.
In this paper a hybrid metaheuristic for biclustering based on Scatter Search and Genetic Algorithms is presented. A general scheme of Scatter Search has been used to obtain high-quality biclusters, but a way of generating the initial population and a method of combination based on Genetic Algorithms have been chosen. Experimental results from yeast cell cycle and human B-cell lymphoma are reported. Finally, the performance of the proposed hybrid algorithm is compared with a genetic algorithm recently published.
GenMin: An enhanced genetic algorithm for global optimization
NASA Astrophysics Data System (ADS)
Tsoulos, Ioannis G.; Lagaris, I. E.
2008-06-01
A new method that employs grammatical evolution and a stopping rule for finding the global minimum of a continuous multidimensional, multimodal function is considered. The genetic algorithm used is a hybrid genetic algorithm in conjunction with a local search procedure. We list results from numerical experiments with a series of test functions and we compare with other established global optimization methods. The accompanying software accepts objective functions coded either in Fortran 77 or in C++. Program summaryProgram title: GenMin Catalogue identifier: AEAR_v1_0 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEAR_v1_0.html Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland Licensing provisions: Standard CPC licence, http://cpc.cs.qub.ac.uk/licence/licence.html No. of lines in distributed program, including test data, etc.: 35 810 No. of bytes in distributed program, including test data, etc.: 436 613 Distribution format: tar.gz Programming language: GNU-C++, GNU-C, GNU Fortran 77 Computer: The tool is designed to be portable in all systems running the GNU C++ compiler Operating system: The tool is designed to be portable in all systems running the GNU C++ compiler RAM: 200 KB Word size: 32 bits Classification: 4.9 Nature of problem: A multitude of problems in science and engineering are often reduced to minimizing a function of many variables. There are instances that a local optimum does not correspond to the desired physical solution and hence the search for a better solution is required. Local optimization techniques are frequently trapped in local minima. Global optimization is hence the appropriate tool. For example, solving a nonlinear system of equations via optimization, employing a least squares type of objective, one may encounter many local minima that do not correspond to solutions (i.e. they are far from zero). Solution method: Grammatical evolution and a stopping rule. Running time: Depending on the objective function. The test example given takes only a few seconds to run.
Resistive Network Optimal Power Flow: Uniqueness and Algorithms
Tan, Chee Wei
Resistive Network Optimal Power Flow: Uniqueness and Algorithms Chee Wei Tan, Senior Member, IEEE, Desmond W. H. Cai, Student Member, IEEE and Xin Lou Abstract--The optimal power flow (OPF) problem and asynchronous settings in practical power grids. I. INTRODUCTION The Optimal Power Flow (OPF) problem
A PARALLEL ALGORITHM FOR TOPOLOGY OPTIMIZATION Roy Johanson*
Papalambros, Panos
applied thermo-mechanical loads. In shape optimization, the optimum shape of a structure is sought by structural engineers. If topological changes are not allowed, size and shape optimization procedures canA PARALLEL ALGORITHM FOR TOPOLOGY OPTIMIZATION Roy Johanson* PanosPapalambros Newton Mack Noboru
AN SLP ALGORITHM AND ITS APPLICATION TO TOPOLOGY OPTIMIZATION
Gomes, Francisco A. M.
of topology optimization is the design of compliant mechanisms. A compliant mechanism is a structureAN SLP ALGORITHM AND ITS APPLICATION TO TOPOLOGY OPTIMIZATION FRANCISCO A. M. GOMES AND THADEU A-859, Campinas, SP, Brazil. E-mails: chico@ime.unicamp.br / tsenne@ime.unicamp.br Abstract. Topology optimization
A hybrid decoupled approach to optimal power flow
Yan, Z.; Xiang, N.D.; Zhang, B.M.; Wang, S.Y.; Chung, T.S.
1996-05-01
This paper presents a hybrid decoupled optimal power flow approach aimed at implementing an optimal power flow program for on-line applications. By taking advantage of the partial duality principle of mathematical programming theory and the inherent weak coupling characteristics between real and reactive power flows in a power system, the solution to the original optimization problem can be approximated iteratively by solving a real and a reactive subproblem alternately. Two special deliberately customized techniques, one based-on linear programming and the other based-on quadratic programming, are adopted to solve the two subproblems respectively. This hybrid feature makes the approach very flexible while preserving the efficiency of decoupling. The approach has been successfully implemented for on-line usage in the Control Center of Northeast China Electric Power Administration.
Islam, Md Rabiul
2009-01-01
In this paper, an improved strategy for automated text dependent speaker identification system has been proposed in noisy environment. The identification process incorporates the Neuro- Genetic hybrid algorithm with cepstral based features. To remove the background noise from the source utterance, wiener filter has been used. Different speech pre-processing techniques such as start-end point detection algorithm, pre-emphasis filtering, frame blocking and windowing have been used to process the speech utterances. RCC, MFCC, MFCC, MFCC, LPC and LPCC have been used to extract the features. After feature extraction of the speech, Neuro-Genetic hybrid algorithm has been used in the learning and identification purposes. Features are extracted by using different techniques to optimize the performance of the identification. According to the VALID speech database, the highest speaker identification rate of 100.000 percent for studio environment and 82.33 percent for office environmental conditions have been achieved i...
Genetic Algorithms Applied to Multi-Objective Aerodynamic Shape Optimization
NASA Technical Reports Server (NTRS)
Holst, Terry L.
2005-01-01
A genetic algorithm approach suitable for solving multi-objective problems is described and evaluated using a series of aerodynamic shape optimization problems. Several new features including two variations of a binning selection algorithm and a gene-space transformation procedure are included. The genetic algorithm is suitable for finding Pareto optimal solutions in search spaces that are defined by any number of genes and that contain any number of local extrema. A new masking array capability is included allowing any gene or gene subset to be eliminated as decision variables from the design space. This allows determination of the effect of a single gene or gene subset on the Pareto optimal solution. Results indicate that the genetic algorithm optimization approach is flexible in application and reliable. The binning selection algorithms generally provide Pareto front quality enhancements and moderate convergence efficiency improvements for most of the problems solved.
A Hybrid CPU/GPU Pattern-Matching Algorithm for Deep Packet Inspection
Chen, Yaw-Chung
2015-01-01
The large quantities of data now being transferred via high-speed networks have made deep packet inspection indispensable for security purposes. Scalable and low-cost signature-based network intrusion detection systems have been developed for deep packet inspection for various software platforms. Traditional approaches that only involve central processing units (CPUs) are now considered inadequate in terms of inspection speed. Graphic processing units (GPUs) have superior parallel processing power, but transmission bottlenecks can reduce optimal GPU efficiency. In this paper we describe our proposal for a hybrid CPU/GPU pattern-matching algorithm (HPMA) that divides and distributes the packet-inspecting workload between a CPU and GPU. All packets are initially inspected by the CPU and filtered using a simple pre-filtering algorithm, and packets that might contain malicious content are sent to the GPU for further inspection. Test results indicate that in terms of random payload traffic, the matching speed of our proposed algorithm was 3.4 times and 2.7 times faster than those of the AC-CPU and AC-GPU algorithms, respectively. Further, HPMA achieved higher energy efficiency than the other tested algorithms. PMID:26437335
A Novel Hybrid PSO-BP Algorithm for Neural Network Training
Jun Liu; Xiaohong Qiu
2009-01-01
In order to search better solution in the high dimension space, the novel hybrid PSO-BP algorithm which combines the PSO mechanism with the Levenberg-Marquardt algorithm or the conjugate gradient algorithm is proposed. The main idea employs BP algorithm with numeric technology to find the local optimum, and takes the weights and biases trained as particles, and harnesses swarm motion to
An enhanced DWBA algorithm in hybrid WDM/TDM EPON networks with heterogeneous propagation delays
NASA Astrophysics Data System (ADS)
Li, Chengjun; Guo, Wei; Jin, Yaohui; Sun, Weiqiang; Hu, Weisheng
2011-12-01
An enhanced dynamic wavelength and bandwidth allocation (DWBA) algorithm in hybrid WDM/TDM PON is proposed and experimentally demonstrated. In addition to the fairness of bandwidth allocation, this algorithm also considers the varying propagation delays between ONUs and OLT. The simulation based on MATLAB indicates that the improved algorithm has a better performance compared with some other algorithms.
Delay-area trade-off for MPRM circuits based on hybrid discrete particle swarm optimization
NASA Astrophysics Data System (ADS)
Zhidi, Jiang; Zhenhai, Wang; Pengjun, Wang
2013-06-01
Polarity optimization for mixed polarity Reed—Muller (MPRM) circuits is a combinatorial issue. Based on the study on discrete particle swarm optimization (DPSO) and mixed polarity, the corresponding relation between particle and mixed polarity is established, and the delay-area trade-off of large-scale MPRM circuits is proposed. Firstly, mutation operation and elitist strategy in genetic algorithm are incorporated into DPSO to further develop a hybrid DPSO (HDPSO). Then the best polarity for delay and area trade-off is searched for large-scale MPRM circuits by combining the HDPSO and a delay estimation model. Finally, the proposed algorithm is testified by MCNC Benchmarks. Experimental results show that HDPSO achieves a better convergence than DPSO in terms of search capability for large-scale MPRM circuits.
Decoherence in optimized quantum random-walk search algorithm
NASA Astrophysics Data System (ADS)
Zhang, Yu-Chao; Bao, Wan-Su; Wang, Xiang; Fu, Xiang-Qun
2015-08-01
This paper investigates the effects of decoherence generated by broken-link-type noise in the hypercube on an optimized quantum random-walk search algorithm. When the hypercube occurs with random broken links, the optimized quantum random-walk search algorithm with decoherence is depicted through defining the shift operator which includes the possibility of broken links. For a given database size, we obtain the maximum success rate of the algorithm and the required number of iterations through numerical simulations and analysis when the algorithm is in the presence of decoherence. Then the computational complexity of the algorithm with decoherence is obtained. The results show that the ultimate effect of broken-link-type decoherence on the optimized quantum random-walk search algorithm is negative. Project supported by the National Basic Research Program of China (Grant No. 2013CB338002).
Hybrid intelligent control scheme for air heating system using fuzzy logic and genetic algorithm
Thyagarajan, T.; Shanmugam, J.; Ponnavaikko, M.; Panda, R.C.
2000-01-01
Fuzzy logic provides a means for converting a linguistic control strategy, based on expert knowledge, into an automatic control strategy. Its performance depends on membership function and rule sets. In the traditional Fuzzy Logic Control (FLC) approach, the optimal membership is formed by trial-and-error method. In this paper, Genetic Algorithm (GA) is applied to generate the optimal membership function of FLC. The membership function thus obtained is utilized in the design of the Hybrid Intelligent Control (HIC) scheme. The investigation is carried out for an Air Heat System (AHS), an important component of drying process. The knowledge of the optimum PID controller designed, is used to develop the traditional FLC scheme. The computational difficulties in finding optimal membership function of traditional FLC is alleviated using GA In the design of HIC scheme. The qualitative performance indices are evaluated for the three control strategies, namely, PID, FLC and HIC. The comparison reveals that the HIC scheme designed based on the hybridization of FLC with GA performs better. Moreover, GA is found to be an effective tool for designing the FLC, eliminating the human interface required to generate the membership functions.
A hybrid search algorithm for swarm robots searching in an unknown environment.
Li, Shoutao; Li, Lina; Lee, Gordon; Zhang, Hao
2014-01-01
This paper proposes a novel method to improve the efficiency of a swarm of robots searching in an unknown environment. The approach focuses on the process of feeding and individual coordination characteristics inspired by the foraging behavior in nature. A predatory strategy was used for searching; hence, this hybrid approach integrated a random search technique with a dynamic particle swarm optimization (DPSO) search algorithm. If a search robot could not find any target information, it used a random search algorithm for a global search. If the robot found any target information in a region, the DPSO search algorithm was used for a local search. This particle swarm optimization search algorithm is dynamic as all the parameters in the algorithm are refreshed synchronously through a communication mechanism until the robots find the target position, after which, the robots fall back to a random searching mode. Thus, in this searching strategy, the robots alternated between two searching algorithms until the whole area was covered. During the searching process, the robots used a local communication mechanism to share map information and DPSO parameters to reduce the communication burden and overcome hardware limitations. If the search area is very large, search efficiency may be greatly reduced if only one robot searches an entire region given the limited resources available and time constraints. In this research we divided the entire search area into several subregions, selected a target utility function to determine which subregion should be initially searched and thereby reduced the residence time of the target to improve search efficiency. PMID:25386855
A Hybrid Search Algorithm for Swarm Robots Searching in an Unknown Environment
Li, Shoutao; Li, Lina; Lee, Gordon; Zhang, Hao
2014-01-01
This paper proposes a novel method to improve the efficiency of a swarm of robots searching in an unknown environment. The approach focuses on the process of feeding and individual coordination characteristics inspired by the foraging behavior in nature. A predatory strategy was used for searching; hence, this hybrid approach integrated a random search technique with a dynamic particle swarm optimization (DPSO) search algorithm. If a search robot could not find any target information, it used a random search algorithm for a global search. If the robot found any target information in a region, the DPSO search algorithm was used for a local search. This particle swarm optimization search algorithm is dynamic as all the parameters in the algorithm are refreshed synchronously through a communication mechanism until the robots find the target position, after which, the robots fall back to a random searching mode. Thus, in this searching strategy, the robots alternated between two searching algorithms until the whole area was covered. During the searching process, the robots used a local communication mechanism to share map information and DPSO parameters to reduce the communication burden and overcome hardware limitations. If the search area is very large, search efficiency may be greatly reduced if only one robot searches an entire region given the limited resources available and time constraints. In this research we divided the entire search area into several subregions, selected a target utility function to determine which subregion should be initially searched and thereby reduced the residence time of the target to improve search efficiency. PMID:25386855
NASA Astrophysics Data System (ADS)
Zhou, Mandi; Shu, Jiong; Chen, Zhigang; Ji, Minhe
2012-11-01
Hyperspectral imagery has been widely used in terrain classification for its high resolution. Urban vegetation, known as an essential part of the urban ecosystem, can be difficult to discern due to high similarity of spectral signatures among some land-cover classes. In this paper, we investigate a hybrid approach of the genetic-algorithm tuned fuzzy support vector machine (GA-FSVM) technique and apply it to urban vegetation classification from aerial hyperspectral urban imagery. The approach adopts the genetic algorithm to optimize parameters of support vector machine, and employs the K-nearest neighbor algorithm to calculate the membership function for each fuzzy parameter, aiming to reduce the effects of the isolated and noisy samples. Test data come from push-broom hyperspectral imager (PHI) hyperspectral remote sensing image which partially covers a corner of the Shanghai World Exposition Park, while PHI is a hyper-spectral sensor developed by Shanghai Institute of Technical Physics. Experimental results show the GA-FSVM model generates overall accuracy of 71.2%, outperforming the maximum likelihood classifier with 49.4% accuracy and the artificial neural network method with 60.8% accuracy. It indicates GA-FSVM is a promising model for vegetation classification from hyperspectral urban data, and has good advantage in the application of classification involving abundant mixed pixels and small samples problem.
Low-thrust orbit transfer optimization with refined Q-law and multi-objective genetic algorithm
NASA Technical Reports Server (NTRS)
Lee, Seungwon; Petropoulos, Anastassios E.; von Allmen, Paul
2005-01-01
An optimization method for low-thrust orbit transfers around a central body is developed using the Q-law and a multi-objective genetic algorithm. in the hybrid method, the Q-law generates candidate orbit transfers, and the multi-objective genetic algorithm optimizes the Q-law control parameters in order to simultaneously minimize both the consumed propellant mass and flight time of the orbit tranfer. This paper addresses the problem of finding optimal orbit transfers for low-thrust spacecraft.
Hybrid Reduced Order Modeling Algorithms for Reactor Physics Calculations
NASA Astrophysics Data System (ADS)
Bang, Youngsuk
Reduced order modeling (ROM) has been recognized as an indispensable approach when the engineering analysis requires many executions of high fidelity simulation codes. Examples of such engineering analyses in nuclear reactor core calculations, representing the focus of this dissertation, include the functionalization of the homogenized few-group cross-sections in terms of the various core conditions, e.g. burn-up, fuel enrichment, temperature, etc. This is done via assembly calculations which are executed many times to generate the required functionalization for use in the downstream core calculations. Other examples are sensitivity analysis used to determine important core attribute variations due to input parameter variations, and uncertainty quantification employed to estimate core attribute uncertainties originating from input parameter uncertainties. ROM constructs a surrogate model with quantifiable accuracy which can replace the original code for subsequent engineering analysis calculations. This is achieved by reducing the effective dimensionality of the input parameter, the state variable, or the output response spaces, by projection onto the so-called active subspaces. Confining the variations to the active subspace allows one to construct an ROM model of reduced complexity which can be solved more efficiently. This dissertation introduces a new algorithm to render reduction with the reduction errors bounded based on a user-defined error tolerance which represents the main challenge of existing ROM techniques. Bounding the error is the key to ensuring that the constructed ROM models are robust for all possible applications. Providing such error bounds represents one of the algorithmic contributions of this dissertation to the ROM state-of-the-art. Recognizing that ROM techniques have been developed to render reduction at different levels, e.g. the input parameter space, the state space, and the response space, this dissertation offers a set of novel hybrid ROM algorithms which can be readily integrated into existing methods and offer higher computational efficiency and defendable accuracy of the reduced models. For example, the snapshots ROM algorithm is hybridized with the range finding algorithm to render reduction in the state space, e.g. the flux in reactor calculations. In another implementation, the perturbation theory used to calculate first order derivatives of responses with respect to parameters is hybridized with a forward sensitivity analysis approach to render reduction in the parameter space. Reduction at the state and parameter spaces can be combined to render further reduction at the interface between different physics codes in a multi-physics model with the accuracy quantified in a similar manner to the single physics case. Although the proposed algorithms are generic in nature, we focus here on radiation transport models used in support of the design and analysis of nuclear reactor cores. In particular, we focus on replacing the traditional assembly calculations by ROM models to facilitate the generation of homogenized cross-sections for downstream core calculations. The implication is that assembly calculations could be done instantaneously therefore precluding the need for the expensive evaluation of the few-group cross-sections for all possible core conditions. Given the generic natures of the algorithms, we make an effort to introduce the material in a general form to allow non-nuclear engineers to benefit from this work.
Evaluation of hybrids algorithms for mass detection in digitalized mammograms
NASA Astrophysics Data System (ADS)
Cordero, José; Garzón Reyes, Johnson
2011-01-01
The breast cancer remains being a significant public health problem, the early detection of the lesions can increase the success possibilities of the medical treatments. The mammography is an image modality effective to early diagnosis of abnormalities, where the medical image is obtained of the mammary gland with X-rays of low radiation, this allows detect a tumor or circumscribed mass between two to three years before that it was clinically palpable, and is the only method that until now achieved reducing the mortality by breast cancer. In this paper three hybrids algorithms for circumscribed mass detection on digitalized mammograms are evaluated. In the first stage correspond to a review of the enhancement and segmentation techniques used in the processing of the mammographic images. After a shape filtering was applied to the resulting regions. By mean of a Bayesian filter the survivors regions were processed, where the characteristics vector for the classifier was constructed with few measurements. Later, the implemented algorithms were evaluated by ROC curves, where 40 images were taken for the test, 20 normal images and 20 images with circumscribed lesions. Finally, the advantages and disadvantages in the correct detection of a lesion of every algorithm are discussed.
A fast hybrid algorithm for exoplanetary transit searches
Cameron, A C; Street, R A; Lister, T A; West, R G; Wilson, D M; Pont, F; Christian, D J; Clarkson, W I; Enoch, B; Evans, A; Fitzsimmons, A; Haswell, C A; Hellier, C; Hodgkin, S T; Horne, K; Irwin, J; Kane, S R; Keenan, F P; Norton, A J; Parley, N R; Osborne, J; Ryans, R; Skillen, I; Wheatley, P J
2006-01-01
We present a fast and efficient hybrid algorithm for selecting exoplanetary candidates from wide-field transit surveys. Our method is based on the widely-used SysRem and Box Least-Squares (BLS) algorithms. Patterns of systematic error that are common to all stars on the frame are mapped and eliminated using the SysRem algorithm. The remaining systematic errors caused by spatially localised flat-fielding and other errors are quantified using a boxcar-smoothing method. We show that the dimensions of the search-parameter space can be reduced greatly by carrying out an initial BLS search on a coarse grid of reduced dimensions, followed by Newton-Raphson refinement of the transit parameters in the vicinity of the most significant solutions. We illustrate the method's operation by applying it to data from one field of the SuperWASP survey, comprising 2300 observations of 7840 stars brighter than V=13.0. We identify 11 likely transit candidates. We reject stars that exhibit significant ellipsoidal variations indicat...
An Adaptive Unified Differential Evolution Algorithm for Global Optimization
Qiang, Ji; Mitchell, Chad
2014-11-03
In this paper, we propose a new adaptive unified differential evolution algorithm for single-objective global optimization. Instead of the multiple mutation strate- gies proposed in conventional differential evolution algorithms, this algorithm employs a single equation unifying multiple strategies into one expression. It has the virtue of mathematical simplicity and also provides users the flexibility for broader exploration of the space of mutation operators. By making all control parameters in the proposed algorithm self-adaptively evolve during the process of optimization, it frees the application users from the burden of choosing appro- priate control parameters and also improves the performance of the algorithm. In numerical tests using thirteen basic unimodal and multimodal functions, the proposed adaptive unified algorithm shows promising performance in compari- son to several conventional differential evolution algorithms.
Improved PSO algorithms for electromagnetic optimization
L. Matekovits; M. Mussetta; P. Pirinoli; S. Selleri; R. E. Zich
2005-01-01
Some variations over the basic particle swarm algorithm are here proposed, aimed at a more efficient search over the solution space and exhibiting a negligible overhead in complexity and speed. The proposed algorithms are then applied to the test case of a microwave filter to show their superior capabilities with respect to the conventional algorithm.
Optimal design of passive linear suspension using genetic algorithm
R Alkhatib; G Nakhaie Jazar; M. F Golnaraghi
2004-01-01
In this paper the genetic algorithm (GA) method is applied to the optimization problem of a linear one-degree-of-freedom (1-DOF) vibration isolator mount and the method is extended to the optimization of a linear quarter car suspension model. A novel criterion for selecting optimal suspension parameters is presented. An optimal relationship between the root mean square (RMS) of the absolute acceleration
Optimal combination of nested clusters by a greedy approximation algorithm.
Dang, Edward K F; Luk, Robert W P; Lee, D L; Ho, K S; Chan, Stephen C F
2009-11-01
Given a set of clusters, we consider an optimization problem which seeks a subset of clusters that maximizes the microaverage F-measure. This optimal value can be used as an evaluation measure of the goodness of clustering. For arbitrarily overlapping clusters, finding the optimal value is NP-hard. We claim that a greedy approximation algorithm yields the global optimal solution for clusters that overlap only by nesting. We present a mathematical proof of this claim by induction. For a family of n clusters containing a total of N objects, this algorithm has an {\\rm O}(n;{2}) time complexity and O(N) space complexity. PMID:19762933
An efficient hybrid approach for multiobjective optimization of water distribution systems
NASA Astrophysics Data System (ADS)
Zheng, Feifei; Simpson, Angus R.; Zecchin, Aaron C.
2014-05-01
An efficient hybrid approach for the design of water distribution systems (WDSs) with multiple objectives is described in this paper. The objectives are the minimization of the network cost and maximization of the network resilience. A self-adaptive multiobjective differential evolution (SAMODE) algorithm has been developed, in which control parameters are automatically adapted by means of evolution instead of the presetting of fine-tuned parameter values. In the proposed method, a graph algorithm is first used to decompose a looped WDS into a shortest-distance tree (T) or forest, and chords (?). The original two-objective optimization problem is then approximated by a series of single-objective optimization problems of the T to be solved by nonlinear programming (NLP), thereby providing an approximate Pareto optimal front for the original whole network. Finally, the solutions at the approximate front are used to seed the SAMODE algorithm to find an improved front for the original entire network. The proposed approach is compared with two other conventional full-search optimization methods (the SAMODE algorithm and the NSGA-II) that seed the initial population with purely random solutions based on three case studies: a benchmark network and two real-world networks with multiple demand loading cases. Results show that (i) the proposed NLP-SAMODE method consistently generates better-quality Pareto fronts than the full-search methods with significantly improved efficiency; and (ii) the proposed SAMODE algorithm (no parameter tuning) exhibits better performance than the NSGA-II with calibrated parameter values in efficiently offering optimal fronts.
Optimization of hybrid passive-active solar residences
Geni, L.R.
1980-01-01
A computer program to be used in the optimal design of hybrid passive-active solar residences is developed in two stages. First, the annual energy loads of an array of hybrid solar residence models are generated using the TRNSYS transient simulation program. The three parameters in the solar homes which are allowed to vary are the insulation thickness, the area of passive direct gain windows and the size of an active air solar system. Data are generated for approximately four hundred distinct hybrid solar configurations as well as a base case house to serve as a reference for increased costs and energy savings. The second stage consists of developing a user-oriented computer program to perform a life-cycle cost analysis of each of the configurations and determine those configurations with the greatest economic viability.
Design of underwater robot lines based on a hybrid automatic optimization strategy
NASA Astrophysics Data System (ADS)
Lyu, Wenjing; Luo, Weilin
2014-09-01
In this paper, a hybrid automatic optimization strategy is proposed for the design of underwater robot lines. Isight is introduced as an integration platform. The construction of this platform is based on the user programming and several commercial software including UG6.0, GAMBIT2.4.6 and FLUENT12.0. An intelligent parameter optimization method, the particle swarm optimization, is incorporated into the platform. To verify the strategy proposed, a simulation is conducted on the underwater robot model 5470, which originates from the DTRC SUBOFF project. With the automatic optimization platform, the minimal resistance is taken as the optimization goal; the wet surface area as the constraint condition; the length of the fore-body, maximum body radius and after-body's minimum radius as the design variables. With the CFD calculation, the RANS equations and the standard turbulence model are used for direct numerical simulation. By analyses of the simulation results, it is concluded that the platform is of high efficiency and feasibility. Through the platform, a variety of schemes for the design of the lines are generated and the optimal solution is achieved. The combination of the intelligent optimization algorithm and the numerical simulation ensures a global optimal solution and improves the efficiency of the searching solutions.
PCB Drill Path Optimization by Combinatorial Cuckoo Search Algorithm
Lim, Wei Chen Esmonde; Kanagaraj, G.; Ponnambalam, S. G.
2014-01-01
Optimization of drill path can lead to significant reduction in machining time which directly improves productivity of manufacturing systems. In a batch production of a large number of items to be drilled such as printed circuit boards (PCB), the travel time of the drilling device is a significant portion of the overall manufacturing process. To increase PCB manufacturing productivity and to reduce production costs, a good option is to minimize the drill path route using an optimization algorithm. This paper reports a combinatorial cuckoo search algorithm for solving drill path optimization problem. The performance of the proposed algorithm is tested and verified with three case studies from the literature. The computational experience conducted in this research indicates that the proposed algorithm is capable of efficiently finding the optimal path for PCB holes drilling process. PMID:24707198
PCB drill path optimization by combinatorial cuckoo search algorithm.
Lim, Wei Chen Esmonde; Kanagaraj, G; Ponnambalam, S G
2014-01-01
Optimization of drill path can lead to significant reduction in machining time which directly improves productivity of manufacturing systems. In a batch production of a large number of items to be drilled such as printed circuit boards (PCB), the travel time of the drilling device is a significant portion of the overall manufacturing process. To increase PCB manufacturing productivity and to reduce production costs, a good option is to minimize the drill path route using an optimization algorithm. This paper reports a combinatorial cuckoo search algorithm for solving drill path optimization problem. The performance of the proposed algorithm is tested and verified with three case studies from the literature. The computational experience conducted in this research indicates that the proposed algorithm is capable of efficiently finding the optimal path for PCB holes drilling process. PMID:24707198
Development of hybrid genetic algorithms for product line designs.
Balakrishnan, P V Sundar; Gupta, Rakesh; Jacob, Varghese S
2004-02-01
In this paper, we investigate the efficacy of artificial intelligence (AI) based meta-heuristic techniques namely genetic algorithms (GAs), for the product line design problem. This work extends previously developed methods for the single product design problem. We conduct a large scale simulation study to determine the effectiveness of such an AI based technique for providing good solutions and bench mark the performance of this against the current dominant approach of beam search (BS). We investigate the potential advantages of pursuing the avenue of developing hybrid models and then implement and study such hybrid models using two very distinct approaches: namely, seeding the initial GA population with the BS solution, and employing the BS solution as part of the GA operator's process. We go on to examine the impact of two alternate string representation formats on the quality of the solutions obtained by the above proposed techniques. We also explicitly investigate a critical managerial factor of attribute importance in terms of its impact on the solutions obtained by the alternate modeling procedures. The alternate techniques are then evaluated, using statistical analysis of variance, on a fairy large number of data sets, as to the quality of the solutions obtained with respect to the state-of-the-art benchmark and in terms of their ability to provide multiple, unique product line options. PMID:15372718
Tian, GuangJian; Xia, Yong; Zhang, Yanning; Feng, Dagan
2011-05-01
The expectation-maximization (EM) algorithm has been widely applied to the estimation of gaussian mixture model (GMM) in brain MR image segmentation. However, the EM algorithm is deterministic and intrinsically prone to overfitting the training data and being trapped in local optima. In this paper, we propose a hybrid genetic and variational EM (GA-VEM) algorithm for brain MR image segmentation. In this approach, the VEM algorithm is performed to estimate the GMM, and the GA is employed to initialize the hyperparameters of the conjugate prior distributions of GMM parameters involved in the VEM algorithm. Since GA has the potential to achieve global optimization and VEM can steadily avoid overfitting, the hybrid GA-VEM algorithm is capable of overcoming the drawbacks of traditional EM-based methods. We compared our approach to the EM-based, VEM-based, and GA-EM based segmentation algorithms, and the segmentation routines used in the statistical parametric mapping package and FMRIB Software Library in 20 low-resolution and 17 high-resolution brain MR studies. Our results show that the proposed approach can improve substantially the performance of brain MR image segmentation. PMID:21233052
A Unified Differential Evolution Algorithm for Global Optimization
Qiang, Ji; Mitchell, Chad
2014-06-24
Abstract?In this paper, we propose a new unified differential evolution (uDE) algorithm for single objective global optimization. Instead of selecting among multiple mutation strategies as in the conventional differential evolution algorithm, this algorithm employs a single equation as the mutation strategy. It has the virtue of mathematical simplicity and also provides users the flexbility for broader exploration of different mutation strategies. Numerical tests using twelve basic unimodal and multimodal functions show promising performance of the proposed algorithm in comparison to convential differential evolution algorithms.
The LFOPC leap-frog algorithm for constrained optimization
J. A. Snyman
2000-01-01
This paper describes an accurate and reliable new algorithm (LFOPC) for solving constrained optimization problems, through a three-phase application of the well-established leap-frog method for unconstrained optimization, to penalty function formulations of the original constrained problems. The algorithm represents a considerable improvement over an earlier version (LFOPCON) which requires the judicious choice of parameter settings for efficient use. The current
A computer algorithm to optimize the scheduling of strategic sealift
Lambert, Garrett Randall
1995-01-01
A COMPUTER ALGORITHM TO OPTIMIZE THE SCHEDULING OF STRATEGIC SEALIFT A Thesis by GARRETT RANDALL LAMBERT Submitted to the OI5ce of Graduate Studies of Texas A&M University in partial fulfillment of the requirements for the degree of MASTER... OF SCIENCE May 1995 Major Subject: Industrial Engineering A COMPUTER ALGORITHM TO OPTIMIZE THE SCHEDULING OF STRATEGIC SEALIFT A Thesis by GARRETT RANDALL LAMBERT Submitted to Texas A&M University in partial fulfillment of the requirements...
Genetic algorithms - What fitness scaling is optimal?
NASA Technical Reports Server (NTRS)
Kreinovich, Vladik; Quintana, Chris; Fuentes, Olac
1993-01-01
A problem of choosing the best scaling function as a mathematical optimization problem is formulated and solved under different optimality criteria. A list of functions which are optimal under different criteria is presented which includes both the best functions empirically proved and new functions that may be worth trying.
Aerodynamic Shape Design of Transonic Airfoils Using Hybrid Optimization Techniques and CFD
Xing, X.Q.
This paper will analyze the effects of using hybrid optimization methods for optimizing objective functions that are determined by computational fluid dynamics solvers for compressible viscous flow for optimal design of ...
Wave Algorithms: Optimal Database Search and Catalysis
Apoorva D. Patel
2006-12-20
Grover's database search algorithm, although discovered in the context of quantum computation, can be implemented using any physical system that allows superposition of states. A physical realization of this algorithm is described using coupled simple harmonic oscillators, which can be exactly solved in both classical and quantum domains. Classical wave algorithms are far more stable against decoherence compared to their quantum counterparts. In addition to providing convenient demonstration models, they may have a role in practical situations, such as catalysis.
The optimization design of truss based on Ant Colony optimal Algorithm
Xiaojia Chen; Shuguang Liu; Shaohong He
2010-01-01
An optimization method for a truss structure design using the Ant Colony Algorithm is presented in this paper. With the constraints of the truss stiffness, strength, allowing displacement and other design conditions, the Ant Colony Algorithm is implanted to the optimizing process of the truss structure using the program languages of MATLAB. Two typical trusses are used as examples to
Dominance Learning in Diploid Genetic Algorithms for Dynamic Optimization Problems
Yang, Shengxiang
Dominance Learning in Diploid Genetic Algorithms for Dynamic Optimization Problems Shengxiang Yang.yang@mcs.le.ac.uk ABSTRACT This paper proposes an adaptive dominance mechanism for diploidy genetic algorithms in dynamic environments. In this scheme, the genotype to phenotype mapping in each gene locus is controlled by a dominance
An algebraic grid optimization algorithm using condition numbers
Costanza Conti; Rossana Morandi; Rosa Maria Spitaleri
2006-01-01
In this paper we present an algorithm able to provide geometrically optimal algebraic grids by using condition numbers as quality measures. In fact, the solution of partial differential equations (PDEs) to model complex problems needs an efficient algorithm to generate a good quality grid since better geometrical grid quality is gained, faster accuracy of the numerical solution can be kept.
Frankenstein's PSO: A Composite Particle Swarm Optimization Algorithm
Marco Antonio Montes de Oca; Thomas Stützle; Mauro Birattari; Marco Dorigo
2009-01-01
During the last decade, many variants of the original particle swarm optimization (PSO) algorithm have been proposed. In many cases, the difference between two variants can be seen as an algorithmic component being present in one variant but not in the other. In the first part of the paper, we present the results and insights obtained from a detailed empirical
Genetic Algorithms in Optimization: Better than Random Search? \\Lambda
Amaral, José Nelson
Genetic Algorithms in Optimization: Better than Random Search? \\Lambda Jos'e Nelson Amaral, Ph'osGradua¸c~ao em Engenharia El'etrica Pontif'icia Universidade Cat'olica do Rio Grande do Sul 90619900 Porto of choice for individu als in Genetic Algorithms and that genetic op erators must be tailored to each
Hardware oriented optimization of Smith-Waterman algorithm
A. Milik; A. Pulka
2010-01-01
The work presented within the paper concerns very important problem of searching for string alignments. The problem originates from modern computation biology. Hardware based implementations have been driving out software solutions in the field recently. The complex programmable devices have become very commonly applied. The paper introduces a new, optimized approach based on Smith-Waterman dynamic programming algorithm. The original algorithm
Parallel optimization algorithms and their implementation in VLSI design
NASA Technical Reports Server (NTRS)
Lee, G.; Feeley, J. J.
1991-01-01
Two new parallel optimization algorithms based on the simplex method are described. They may be executed by a SIMD parallel processor architecture and be implemented in VLSI design. Several VLSI design implementations are introduced. An application example is reported to demonstrate that the algorithms are effective.
Designing Stochastic Optimization Algorithms for Real-world Applications
NASA Astrophysics Data System (ADS)
Someya, Hiroshi; Handa, Hisashi; Koakutsu, Seiichi
This article presents a review of recent advances in stochastic optimization algorithms. Novel algorithms achieving highly adaptive and efficient searches, theoretical analyses to deepen our understanding of search behavior, successful implementation on parallel computers, attempts to build benchmark suites for industrial use, and techniques applied to real-world problems are included. A list of resources is provided.
Motivation Defect correction The algorithm Summary Defect correction in optimization
Hemker, P.W.
Motivation Defect correction The algorithm Summary Defect correction in optimization "Manifold Mapping" P.W. Hemker IPIR/CWI/UvA June 11, 2010 Manifold Mapping P.W. Hemker #12;Motivation Defect correction The algorithm Summary Motivation Motivation determine x1, x2, x1, x3, x4, x5, x6, x7 Manifold
An Optimized Multiple Hypothesis RAIM Algorithm forVertical Guidance
Stanford University
An Optimized Multiple Hypothesis RAIM Algorithm forVertical Guidance Juan Blanch, Alex Ene, Todd or Cat. I, for example), and perhaps ultimately replacing integrity providers such as SBAS and GBAS Multiple Hypothesis Solution Separation algorithm for RAIM. There are several advantages in the Multiple
A novel optimization sizing model for hybrid solar-wind power generation system
Hongxing Yang; Lin Lu; Wei Zhou
2007-01-01
This paper develops the Hybrid Solar-Wind System Optimization Sizing (HSWSO) model, to optimize the capacity sizes of different components of hybrid solar-wind power generation systems employing a battery bank. The HSWSO model consists of three parts: the model of the hybrid system, the model of Loss of Power Supply Probability (LPSP) and the model of the Levelised Cost of Energy
Horizontal Well Placement Optimization in Gas Reservoirs Using Genetic Algorithms
Gibbs, Trevor Howard
2011-08-08
since they modify several solutions simultaneously. All of these properties make genetic algorithms the logical choice to be the basis in answering the well location determination problem in a gas reservoir. LITERATURE SURVEY ?Genetic algorithms... PLACEMENT OPTIMIZATION IN GAS RESERVOIRS USING GENETIC ALGORITHMS A Thesis by TREVOR HOWARD GIBBS Submitted to the Office of Graduate Studies of Texas A&M University in partial fulfillment of the requirements for the degree of MASTER...
Global Optimization of Plug-In Hybrid Vehicle Design and Allocation to
McGaughey, Alan
Global Optimization of Plug-In Hybrid Vehicle Design and Allocation to Minimize Life Cycle- tion of conventional (CV), hybrid electric (HEV), and plug-in hybrid electric (PHEV) vehicles to obtain assessment 1 Introduction Plug-in hybrid electric vehicles (PHEVs) offer a potentially promising technology
Communication-Optimal Eigenvalue/SVD Algorithms
California at Berkeley, University of
Algorithm Goal of divide and conquer step is to divide the spectrum along a curve in the com- plex plane blocks as subproblems Algorithm 1: Splitting the spectrum of a matrix A along unit circle 1: Implicit¨obius transformations · In order to split the spectrum along any line or circle, we can use transformations of the form
A comparison of optimal and sub-optimal MAP decoding algorithms operating in the log domain
P. Robertson; E. Villebrun; P. Hoeher
1995-01-01
For estimating the states or outputs of a Markov process, the symbol-by-symbol MAP algorithm is optimal. However, this algorithm, even in its recursive form, poses technical difficulties because of numerical representation problems, the necessity of nonlinear functions and a high number of additions and multiplications. MAP like algorithms operating in the logarithmic domain presented in the past solve the numerical
NASA Astrophysics Data System (ADS)
Nourelfath, M.; Nahas, N.; Montreuil, B.
2007-12-01
This article uses a hybrid optimization approach to solve the discrete facility layout problem (FLP), modelled as a quadratic assignment problem (QAP). The idea of this approach design is inspired by the ant colony meta-heuristic optimization method, combined with the extended great deluge (EGD) local search technique. Comparative computational experiments are carried out on benchmarks taken from the QAP-library and from real life problems. The performance of the proposed algorithm is compared to construction and improvement heuristics such as H63, HC63-66, CRAFT and Bubble Search, as well as other existing meta-heuristics developed in the literature based on simulated annealing (SA), tabu search and genetic algorithms (GAs). This algorithm is compared also to other ant colony implementations for QAP. The experimental results show that the proposed ant colony optimization/extended great deluge (ACO/EGD) performs significantly better than the existing construction and improvement algorithms. The experimental results indicate also that the ACO/EGD heuristic methodology offers advantages over other algorithms based on meta-heuristics in terms of solution quality.
GLOBAL OPTIMIZATION AND APPROXIMATION ALGORITHMS IN COMPUTER VISION
Lunds Universitet
Vision Abstract Computer Vision is today a wide research area including topics like robot vision, image relaxations, computer vision, binary quadratic optimization. Classification system and/or index terms (if anyGLOBAL OPTIMIZATION AND APPROXIMATION ALGORITHMS IN COMPUTER VISION CARL OLSSON Faculty
A parallel Particle swarm optimization algorithm for option pricing
Hari Prasain; Girish Kumar Jha; Parimala Thulasiraman; Ruppa K. Thulasiram
2010-01-01
Option pricing is one of the challenging problems of computational finance. Nature-inspired algorithms have gained prominence in real world optimization problems such as in mobile ad hoc networks. The option pricing problem fits very well into this category of problems due to the ad hoc nature of the market. Particle swarm optimization (PSO) is one of the novel global search
Using modifications to Grover's Search algorithm for quantum global optimization
Yipeng Liu; Gary J. Koehler
2010-01-01
We study the problem of finding a global optimal solution to discrete optimization problems using a heuristic based on quantum computing methods. (Knowledge of quantum computing ideas is not necessary to read this paper.) We focus on a successful quantum computing method introduced by Baritompa, Bulger, and Wood, that we refer to as the BBW algorithm, and develop two modifications.
Monotonic convergence of a general algorithm for computing optimal designs
Yu, Yaming
2010-01-01
Monotonic convergence is established for a general class of multiplicative algorithms introduced by Silvey, Titterington and Torsney [Comm. Statist. Theory Methods 14 (1978) 1379--1389] for computing optimal designs. A conjecture of Titterington [Appl. Stat. 27 (1978) 227--234] is confirmed as a consequence. Optimal designs for logistic regression are used as an illustration.
An Ant Colony Optimization Competition Routing Algorithm for WSN
Zhicheng Zhong; Zhizhong Tian; Li Zhe; Peihua Xu
2008-01-01
This paper develops Ant Colony optimization (ACO) algorithm and applies it to energy control and congestion control on wireless sensor network route. The pheromone and the energy of the node are combined to affect the pheromone consent ration in optimization path, which can avoid network congestion and fast consume of energy of individual node. Then it can prolong the lifecycle
Seminar on Algorithms and Models for Railway Optimization
Brandes, Ulrik
Seminar on Algorithms and Models for Railway Optimization Crew scheduling Jasper MÂ¨oller mailto part of the railway optimization process. Unfortunately it is also a difficult one to solve, due railroad companies, it has become increasingly important to reduce the overall cost of operation. Personnel
Design optimization of electrical machines using genetic algorithms
G. F. Uler; O. A. Mohammed; Chang-Seop Koh
1995-01-01
The application of genetic algorithms (GAs) to the design optimization of electromagnetic devices is presented in detail. The method is demonstrated on a magnetizer by optimizing its pole face to obtain the desired magnetic flux density distribution. The shape of the pole face is constructed from the control points by means of uniform nonrational b-splines
Imperialist competitive algorithm combined with chaos for global optimization
NASA Astrophysics Data System (ADS)
Talatahari, S.; Farahmand Azar, B.; Sheikholeslami, R.; Gandomi, A. H.
2012-03-01
A novel chaotic improved imperialist competitive algorithm (CICA) is presented for global optimization. The ICA is a new meta-heuristic optimization developed based on a socio-politically motivated strategy and contains two main steps: the movement of the colonies and the imperialistic competition. Here different chaotic maps are utilized to improve the movement step of the algorithm. Seven different chaotic maps are investigated and the Logistic and Sinusoidal maps are found as the best choices. Comparing the new algorithm with the other ICA-based methods demonstrates the superiority of the CICA for the benchmark functions.
Using genetic algorithm to solve a new multi-period stochastic optimization model
NASA Astrophysics Data System (ADS)
Zhang, Xin-Li; Zhang, Ke-Cun
2009-09-01
This paper presents a new asset allocation model based on the CVaR risk measure and transaction costs. Institutional investors manage their strategic asset mix over time to achieve favorable returns subject to various uncertainties, policy and legal constraints, and other requirements. One may use a multi-period portfolio optimization model in order to determine an optimal asset mix. Recently, an alternative stochastic programming model with simulated paths was proposed by Hibiki [N. Hibiki, A hybrid simulation/tree multi-period stochastic programming model for optimal asset allocation, in: H. Takahashi, (Ed.) The Japanese Association of Financial Econometrics and Engineering, JAFFE Journal (2001) 89-119 (in Japanese); N. Hibiki A hybrid simulation/tree stochastic optimization model for dynamic asset allocation, in: B. Scherer (Ed.), Asset and Liability Management Tools: A Handbook for Best Practice, Risk Books, 2003, pp. 269-294], which was called a hybrid model. However, the transaction costs weren't considered in that paper. In this paper, we improve Hibiki's model in the following aspects: (1) The risk measure CVaR is introduced to control the wealth loss risk while maximizing the expected utility; (2) Typical market imperfections such as short sale constraints, proportional transaction costs are considered simultaneously. (3) Applying a genetic algorithm to solve the resulting model is discussed in detail. Numerical results show the suitability and feasibility of our methodology.
Optimization of a CNG series hybrid concept vehicle
Aceves, S.M.; Smith, J.R.; Perkins, L.J.; Haney, S.W.; Flowers, D.L.
1995-09-22
Compressed Natural Gas (CNG) has favorable characteristics as a vehicular fuel, in terms of fuel economy as well as emissions. Using CNG as a fuel in a series hybrid vehicle has the potential of resulting in very high fuel economy (between 26 and 30 km/liter, 60 to 70 mpg) and very low emissions (substantially lower than Federal Tier II or CARB ULEV). This paper uses a vehicle evaluation code and an optimizer to find a set of vehicle parameters that result in optimum vehicle fuel economy. The vehicle evaluation code used in this analysis estimates vehicle power performance, including engine efficiency and power, generator efficiency, energy storage device efficiency and state-of-charge, and motor and transmission efficiencies. Eight vehicle parameters are selected as free variables for the optimization. The optimum vehicle must also meet two perfect requirements: accelerate to 97 km/h in less than 10 s, and climb an infinitely long hill with a 6% slope at 97 km/h with a 272 kg (600 lb.) payload. The optimizer used in this work was originally developed in the magnetic fusion energy program, and has been used to optimize complex systems, such as magnetic and inertial fusion devices, neutron sources, and mil guns. The optimizer consists of two parts: an optimization package for minimizing non-linear functions of many variables subject to several non-linear equality and/or inequality constraints and a programmable shell that allows interactive configuration and execution of the optimizer. The results of the analysis indicate that the CNG series hybrid vehicle has a high efficiency and low emissions. These results emphasize the advantages of CNG as a near-term alternative fuel for vehicles.
Hybrid Honey Bees Mating Optimisation algorithm to assign terminals to concentrators
Eugénia M. Bernardino; Anabela M. Bernardino; Juan Manuel Sánchez-Pérez; J. A. Go?mez-Pulido; Miguel Angel Vega-Rodríguez
2010-01-01
In this paper we propose a new approach to assign terminals to concentrators using a Hybrid Honey Bees Mating Optimisation algorithm. Honey Bees Mating Optimisation (HBMO) algorithm is a swarm-based optimisation algorithm, which simulates the mating process of real honey bees. We apply a hybridisation of HBMO to solve a combinatorial optimisation problem known as Terminal Assignment Problem (TAP). The
Hybrid Genetic Algorithms for Minimization of a Polypeptide Specific Energy Model
Laurence D. Merkle; Gary B. Lamont; George H. Gates Jr.; Ruth Pachter
1996-01-01
A hybrid genetic algorithm for polypeptide structure prediction is proposed which incorporates efficient gradient-based minimization directly in the fitness evaluation. Fitness is based on a polypeptide specific potential energy model. The algorithm includes a replacement frequency parameter which specifies the probability with which an individual is replaced by its minimized counterpart. Thus, the algorithm can implement either Baldwinian, Lamarckian, or
An evolutionary game based particle swarm optimization algorithm
NASA Astrophysics Data System (ADS)
Liu, Wei-Bing; Wang, Xian-Jia
2008-04-01
Particle swarm optimization (PSO) is an evolutionary algorithm used extensively. This paper presented a new particle swarm optimizer based on evolutionary game (EGPSO). We map particles' finding optimal solution in PSO algorithm to players' pursuing maximum utility by choosing strategies in evolutionary games, using replicator dynamics to model the behavior of particlesE And in order to overcome premature convergence a multi-start technique was introduced. Experimental results show that EGPSO can overcome premature convergence and has great performance of convergence property over traditional PSO.
A Hybrid Optimization Framework with POD-based Order Reduction and Design-Space Evolution Scheme
NASA Astrophysics Data System (ADS)
Ghoman, Satyajit S.
The main objective of this research is to develop an innovative multi-fidelity multi-disciplinary design, analysis and optimization suite that integrates certain solution generation codes and newly developed innovative tools to improve the overall optimization process. The research performed herein is divided into two parts: (1) the development of an MDAO framework by integration of variable fidelity physics-based computational codes, and (2) enhancements to such a framework by incorporating innovative features extending its robustness. The first part of this dissertation describes the development of a conceptual Multi-Fidelity Multi-Strategy and Multi-Disciplinary Design Optimization Environment (M3 DOE), in context of aircraft wing optimization. M 3 DOE provides the user a capability to optimize configurations with a choice of (i) the level of fidelity desired, (ii) the use of a single-step or multi-step optimization strategy, and (iii) combination of a series of structural and aerodynamic analyses. The modularity of M3 DOE allows it to be a part of other inclusive optimization frameworks. The M 3 DOE is demonstrated within the context of shape and sizing optimization of the wing of a Generic Business Jet aircraft. Two different optimization objectives, viz. dry weight minimization, and cruise range maximization are studied by conducting one low-fidelity and two high-fidelity optimization runs to demonstrate the application scope of M3 DOE. The second part of this dissertation describes the development of an innovative hybrid optimization framework that extends the robustness of M 3 DOE by employing a proper orthogonal decomposition-based design-space order reduction scheme combined with the evolutionary algorithm technique. The POD method of extracting dominant modes from an ensemble of candidate configurations is used for the design-space order reduction. The snapshot of candidate population is updated iteratively using evolutionary algorithm technique of fitness-driven retention. This strategy capitalizes on the advantages of evolutionary algorithm as well as POD-based reduced order modeling, while overcoming the shortcomings inherent with these techniques. When linked with M3 DOE, this strategy offers a computationally efficient methodology for problems with high level of complexity and a challenging design-space. This newly developed framework is demonstrated for its robustness on a nonconventional supersonic tailless air vehicle wing shape optimization problem.
A parallel variable metric optimization algorithm
NASA Technical Reports Server (NTRS)
Straeter, T. A.
1973-01-01
An algorithm, designed to exploit the parallel computing or vector streaming (pipeline) capabilities of computers is presented. When p is the degree of parallelism, then one cycle of the parallel variable metric algorithm is defined as follows: first, the function and its gradient are computed in parallel at p different values of the independent variable; then the metric is modified by p rank-one corrections; and finally, a single univariant minimization is carried out in the Newton-like direction. Several properties of this algorithm are established. The convergence of the iterates to the solution is proved for a quadratic functional on a real separable Hilbert space. For a finite-dimensional space the convergence is in one cycle when p equals the dimension of the space. Results of numerical experiments indicate that the new algorithm will exploit parallel or pipeline computing capabilities to effect faster convergence than serial techniques.
Optimization of the Solovay-Kitaev algorithm
NASA Astrophysics Data System (ADS)
Pham, Tien Trung; Van Meter, Rodney; Horsman, Clare
2013-05-01
The Solovay-Kitaev algorithm is the standard method used for approximating arbitrary single-qubit gates for fault-tolerant quantum computation. In this paper we introduce a technique called search space expansion, which modifies the initial stage of the Solovay-Kitaev algorithm, increasing the length of the possible approximating sequences but without requiring an exhaustive search over all possible sequences. This technique is combined with an efficient space search method called geometric nearest-neighbor access trees, modified for the unitary matrix lookup problem, in order to reduce significantly the algorithm run time. We show that, with low time cost, our techniques output gate sequences that are almost an order of magnitude smaller for the same level of accuracy. This therefore reduces the error correction requirements for quantum algorithms on encoded fault-tolerant hardware.
Artificial bee colony algorithm for solving optimal power flow problem.
Le Dinh, Luong; Vo Ngoc, Dieu; Vasant, Pandian
2013-01-01
This paper proposes an artificial bee colony (ABC) algorithm for solving optimal power flow (OPF) problem. The objective of the OPF problem is to minimize total cost of thermal units while satisfying the unit and system constraints such as generator capacity limits, power balance, line flow limits, bus voltages limits, and transformer tap settings limits. The ABC algorithm is an optimization method inspired from the foraging behavior of honey bees. The proposed algorithm has been tested on the IEEE 30-bus, 57-bus, and 118-bus systems. The numerical results have indicated that the proposed algorithm can find high quality solution for the problem in a fast manner via the result comparisons with other methods in the literature. Therefore, the proposed ABC algorithm can be a favorable method for solving the OPF problem. PMID:24470790
Artificial Bee Colony Algorithm for Solving Optimal Power Flow Problem
Le Dinh, Luong; Vo Ngoc, Dieu
2013-01-01
This paper proposes an artificial bee colony (ABC) algorithm for solving optimal power flow (OPF) problem. The objective of the OPF problem is to minimize total cost of thermal units while satisfying the unit and system constraints such as generator capacity limits, power balance, line flow limits, bus voltages limits, and transformer tap settings limits. The ABC algorithm is an optimization method inspired from the foraging behavior of honey bees. The proposed algorithm has been tested on the IEEE 30-bus, 57-bus, and 118-bus systems. The numerical results have indicated that the proposed algorithm can find high quality solution for the problem in a fast manner via the result comparisons with other methods in the literature. Therefore, the proposed ABC algorithm can be a favorable method for solving the OPF problem. PMID:24470790
Design optimization of gas generator hybrid propulsion boosters
NASA Technical Reports Server (NTRS)
Weldon, Vincent; Phillips, Dwight U.; Fink, Lawrence E.
1990-01-01
A methodology used in support of a contract study for NASA/MSFC to optimize the design of gas generator hybrid propulsion booster for uprating the National Space Transportation System (NSTS) is presented. The objective was to compare alternative configurations for this booster approach, optimizing each candidate concept on different bases, in order to develop data for a trade table on which a final decision was based. The methodology is capable of processing a large number of independent and dependent variables, adjusting the overall subsystems characteristics to arrive at a best compromise integrated design to meet various specified optimization criteria subject to selected constraints. For each system considered, a detailed weight statement was generated along with preliminary cost and reliability estimates.
Design Optimization of Gas Generator Hybrid Propulsion Boosters
NASA Technical Reports Server (NTRS)
Weldon, Vincent; Phillips, Dwight; Fink, Larry
1990-01-01
A methodology used in support of a study for NASA/MSFC to optimize the design of gas generator hybrid propulsion booster for uprating the National Space Transportation System (NSTS) is presented. The objective was to compare alternative configurations for this booster approach, optimizing each candidate concept on different bases, in order to develop data for a trade table on which a final decision was based. The methodology is capable of processing a large number of independent and dependent variables, adjusting the overall subsystems characteristics to arrive at a best compromise integrated design to meet various specific optimization criteria subject to selected constraints. For each system considered, a detailed weight statement was generated along with preliminary cost and reliability estimates.
Efficient integer optimization algorithms for optimal coordination of capacitors and regulators
Baldick, R.; Wu, F.F. (Univ. of California, Berkeley, CA (US))
1990-08-01
The optimal coordination of switched capacitors and tap-changing transformers in a radial distribution system is considered. The formulation incorporates voltage constraints. The coordination problem is approximated by a constrained discrete quadratic optimization using the results from the corresponding unconstrained continuous problem. The discrepancy between the actual and approximating problem is discussed. Two algorithms are proposed to seek solutions to the approximating optimization problem. The first is a randomized algorithm that runs fast but for which there is no guarantee of optimality. The second is a deterministic algorithm, the run time of which is polynomially bounded in the problem size. For large systems the run times of these algorithms may be significantly less than the run times of explicit search or branch and bound algorithms.
Adabor, Emmanuel S; Acquaah-Mensah, George K; Oduro, Francis T
2015-02-01
Bayesian Networks have been used for the inference of transcriptional regulatory relationships among genes, and are valuable for obtaining biological insights. However, finding optimal Bayesian Network (BN) is NP-hard. Thus, heuristic approaches have sought to effectively solve this problem. In this work, we develop a hybrid search method combining Simulated Annealing with a Greedy Algorithm (SAGA). SAGA explores most of the search space by undergoing a two-phase search: first with a Simulated Annealing search and then with a Greedy search. Three sets of background-corrected and normalized microarray datasets were used to test the algorithm. BN structure learning was also conducted using the datasets, and other established search methods as implemented in BANJO (Bayesian Network Inference with Java Objects). The Bayesian Dirichlet Equivalence (BDe) metric was used to score the networks produced with SAGA. SAGA predicted transcriptional regulatory relationships among genes in networks that evaluated to higher BDe scores with high sensitivities and specificities. Thus, the proposed method competes well with existing search algorithms for Bayesian Network structure learning of transcriptional regulatory networks. PMID:25181467
NASA Astrophysics Data System (ADS)
Maslov, Igor V.
2003-08-01
Image registration is formulated as a nonlinear optimization problem of finding an affine transformation minimizing the difference between images. A particular scheme of the hybrid evolutionary algorithm is used to solve the problem. The reproduction phase of the algorithm is enhanced with a two-phase operation of local search and correction performed on the subset of the best chromosomes in the reproduction pool. In order to reduce the computational cost of the correction, a mechanism of the adaptive control of the local search is designed, based on a micro model of the local image response. The mechanism correlates the step size of the search with the local properties of the gray level surface at different points of an image. To reduce the number of evaluations of the local image response required by the control mechanism, all participating image points are evaluated and classified at once in the preprocessing stage. A self-organizing neural network is employed to classify different points according to their response values, and to build an adaptive, compact response map of the entire image. During the execution of the main algorithm, this map is used as a lookup table, to retrieve the appropriate response values for the points participating in the local search.
Zeng, Chen
Reference Energy Extremal Optimization: A Stochastic Search Algorithm Applied to Computational optimization (EO), for the search problem in computational protein design. This algorithm takes advantage and present an improved method, which we call reference energy extremal optimization (REEO). REEO uses
Fixed structure compensator design using a constrained hybrid evolutionary optimization approach.
Ghosh, Subhojit; Samanta, Susovon
2014-07-01
This paper presents an efficient technique for designing a fixed order compensator for compensating current mode control architecture of DC-DC converters. The compensator design is formulated as an optimization problem, which seeks to attain a set of frequency domain specifications. The highly nonlinear nature of the optimization problem demands the use of an initial parameterization independent global search technique. In this regard, the optimization problem is solved using a hybrid evolutionary optimization approach, because of its simple structure, faster execution time and greater probability in achieving the global solution. The proposed algorithm involves the combination of a population search based optimization approach i.e. Particle Swarm Optimization (PSO) and local search based method. The op-amp dynamics have been incorporated during the design process. Considering the limitations of fixed structure compensator in achieving loop bandwidth higher than a certain threshold, the proposed approach also determines the op-amp bandwidth, which would be able to achieve the same. The effectiveness of the proposed approach in meeting the desired frequency domain specifications is experimentally tested on a peak current mode control dc-dc buck converter. PMID:24768082
Single-objective optimization of thermo-electric coolers using genetic algorithm
NASA Astrophysics Data System (ADS)
Khanh, Doan V. K.; Vasant, P.; Elamvazuthi, Irraivan; Dieu, Vo N.
2014-10-01
Thermo-electric Coolers (TECs) nowadays is applied in a wide range of thermal energy systems. This is due to its superior features where no refrigerant and dynamic parts are needed. TECs generate no electrical or acoustical noise and are environment friendly. Over the past decades, many researches were employed to improve the efficiency of TECs by enhancing the material parameters and design parameters. The material parameters are restricted by currently available materials and module fabricating technologies. Therefore, the main objective of TECs design is to determine a set of design parameters such as leg area, leg length and the number of legs. Two elements that play an important role when considering the suitability of TECs in applications are rated of refrigeration (ROR) and coefficient of performance (COP). In this paper, the review of some previous researches will be conducted to see the diversity of optimization in the design of TECs in enhancing the performance and efficiency. After that, single objective optimization problems (SOP) will be tested first by using Genetic Algorithm (GA) to optimize geometry properties so that TECs will operate at near optimal conditions. In the future works, multi-objective optimization problems (MOP) using hybrid GA with another optimization technique will be considered to give a better results and compare with previous research such as Non-Dominated Sorting Genetic Algorithm (NSGA-II) to see the advantages and disadvantages.
High Field Plasma Experiments with Optimal Temperature Hybrid Magnets
NASA Astrophysics Data System (ADS)
Grasso, G.; Coppi, B.
2012-10-01
Developments in the technology of MgB2 superconducting magnets operating at temperatures around 10 K has led to envision their use for fields up to 10 T and as components of hybrid (copper for the highest field + MgB2) magnets. A hybrid magnet of this kind is being constructed at Grenoble. The common coolant is He-gas, the optimal temperature for Cu being about 30 K. The goal of this solution is to construct machines producing plasmas with values of the poloidal field close to those considered for the design of the Ignitor machine but with longer burning times and higher duty cycles. A perspective of this program is to produce devices to be used as neutron sources or material testing systems. Since the relevant machines will have to be larger than Ignitor, they will have to be able to sustain higher plasma currents under macroscopically stable conditions.
Comparative Evaluation of Different Optimization Algorithms for Structural Design Applications
NASA Technical Reports Server (NTRS)
Patnaik, Surya N.; Coroneos, Rula M.; Guptill, James D.; Hopkins, Dale A.
1996-01-01
Non-linear programming algorithms play an important role in structural design optimization. Fortunately, several algorithms with computer codes are available. At NASA Lewis Research Centre, a project was initiated to assess the performance of eight different optimizers through the development of a computer code CometBoards. This paper summarizes the conclusions of that research. CometBoards was employed to solve sets of small, medium and large structural problems, using the eight different optimizers on a Cray-YMP8E/8128 computer. The reliability and efficiency of the optimizers were determined from the performance of these problems. For small problems, the performance of most of the optimizers could be considered adequate. For large problems, however, three optimizers (two sequential quadratic programming routines, DNCONG of IMSL and SQP of IDESIGN, along with Sequential Unconstrained Minimizations Technique SUMT) outperformed others. At optimum, most optimizers captured an identical number of active displacement and frequency constraints but the number of active stress constraints differed among the optimizers. This discrepancy can be attributed to singularity conditions in the optimization and the alleviation of this discrepancy can improve the efficiency of optimizers.
Performance Trend of Different Algorithms for Structural Design Optimization
NASA Technical Reports Server (NTRS)
Patnaik, Surya N.; Coroneos, Rula M.; Guptill, James D.; Hopkins, Dale A.
1996-01-01
Nonlinear programming algorithms play an important role in structural design optimization. Fortunately, several algorithms with computer codes are available. At NASA Lewis Research Center, a project was initiated to assess performance of different optimizers through the development of a computer code CometBoards. This paper summarizes the conclusions of that research. CometBoards was employed to solve sets of small, medium and large structural problems, using different optimizers on a Cray-YMP8E/8128 computer. The reliability and efficiency of the optimizers were determined from the performance of these problems. For small problems, the performance of most of the optimizers could be considered adequate. For large problems however, three optimizers (two sequential quadratic programming routines, DNCONG of IMSL and SQP of IDESIGN, along with the sequential unconstrained minimizations technique SUMT) outperformed others. At optimum, most optimizers captured an identical number of active displacement and frequency constraints but the number of active stress constraints differed among the optimizers. This discrepancy can be attributed to singularity conditions in the optimization and the alleviation of this discrepancy can improve the efficiency of optimizers.
Wang, Peng; Zhu, Zhouquan; Huang, Shuai
2013-01-01
This paper presents a novel biologically inspired metaheuristic algorithm called seven-spot ladybird optimization (SLO). The SLO is inspired by recent discoveries on the foraging behavior of a seven-spot ladybird. In this paper, the performance of the SLO is compared with that of the genetic algorithm, particle swarm optimization, and artificial bee colony algorithms by using five numerical benchmark functions with multimodality. The results show that SLO has the ability to find the best solution with a comparatively small population size and is suitable for solving optimization problems with lower dimensions. PMID:24385879
NASA Astrophysics Data System (ADS)
Feng, Zhilin; Yin, Jianwei; Xiong, Naixue
2012-07-01
Simultaneous denoizing and segmentation of ink-jet printing images are challenging tasks for ink-jet printing pattern analysis. Thus, a unified denoizing-segmentation algorithm is proposed for ink-jet printing images. The energy functional for the proposed algorithm consists of three terms: the Beltrami flow, the phase field, and the shape prior. The Beltrami flow is used to enhance image features while preserving natural fine structures and the phase field model is introduced to extract specific pattern structures within ink-jet printing images. The shape prior term for the deformable framework through a nonlinear energy representation is designed to attract different shapes towards the Beltrami flow and the phase field at given directions. A fuzzy optimization method, called multi-start fuzzy optimization method (MSFOM), is also proposed to numerically solve the unified denoizing-segmentation model. MSFOM is a hybrid algorithm, combining fuzzy logic and genetic optimization, which is able to find the suboptimal global minimum with a low computational cost. Experimental results demonstrate that the proposed algorithm is better than the existing schemes, and it offers effective noise removal in noisy ink-jet printing images while maintaining fine structures of patterns.
Modeling and optimization of a hybrid solar combined cycle (HYCS)
NASA Astrophysics Data System (ADS)
Eter, Ahmad Adel
2011-12-01
The main objective of this thesis is to investigate the feasibility of integrating concentrated solar power (CSP) technology with the conventional combined cycle technology for electric generation in Saudi Arabia. The generated electricity can be used locally to meet the annual increasing demand. Specifically, it can be utilized to meet the demand during the hours 10 am-3 pm and prevent blackout hours, of some industrial sectors. The proposed CSP design gives flexibility in the operation system. Since, it works as a conventional combined cycle during night time and it switches to work as a hybrid solar combined cycle during day time. The first objective of the thesis is to develop a thermo-economical mathematical model that can simulate the performance of a hybrid solar-fossil fuel combined cycle. The second objective is to develop a computer simulation code that can solve the thermo-economical mathematical model using available software such as E.E.S. The developed simulation code is used to analyze the thermo-economic performance of different configurations of integrating the CSP with the conventional fossil fuel combined cycle to achieve the optimal integration configuration. This optimal integration configuration has been investigated further to achieve the optimal design of the solar field that gives the optimal solar share. Thermo-economical performance metrics which are available in the literature have been used in the present work to assess the thermo-economic performance of the investigated configurations. The economical and environmental impact of integration CSP with the conventional fossil fuel combined cycle are estimated and discussed. Finally, the optimal integration configuration is found to be solarization steam side in conventional combined cycle with solar multiple 0.38 which needs 29 hectare and LEC of HYCS is 63.17 $/MWh under Dhahran weather conditions.
A novel hybrid algorithm for the design of the phase diffractive optical elements for beam shaping
NASA Astrophysics Data System (ADS)
Jiang, Wenbo; Wang, Jun; Dong, Xiucheng
2013-02-01
In this paper, a novel hybrid algorithm for the design of a phase diffractive optical elements (PDOE) is proposed. It combines the genetic algorithm (GA) with the transformable scale BFGS (Broyden, Fletcher, Goldfarb, Shanno) algorithm, the penalty function was used in the cost function definition. The novel hybrid algorithm has the global merits of the genetic algorithm as well as the local improvement capabilities of the transformable scale BFGS algorithm. We designed the PDOE using the conventional simulated annealing algorithm and the novel hybrid algorithm. To compare the performance of two algorithms, three indexes of the diffractive efficiency, uniformity error and the signal-to-noise ratio are considered in numerical simulation. The results show that the novel hybrid algorithm has good convergence property and good stability. As an application example, the PDOE was used for the Gaussian beam shaping; high diffractive efficiency, low uniformity error and high signal-to-noise were obtained. The PDOE can be used for high quality beam shaping such as inertial confinement fusion (ICF), excimer laser lithography, fiber coupling laser diode array, laser welding, etc. It shows wide application value.
Series hybrid vehicles and optimized hydrogen engine design
NASA Astrophysics Data System (ADS)
Smith, J. R.; Aceves, S.; Vanblarigan, P.
1995-05-01
Lawrence Livermore, Sandia Livermore and Los Alamos National Laboratories have a joint project to develop an optimized hydrogen fueled engine for series hybrid automobiles. The major divisions of responsibility are: system analysis, engine design and kinetics modeling by LLNL; performance and emission testing, and friction reduction by SNL; computational fluid mechanics and combustion modeling by LANL. This project is a component of the Department of Energy, Office of Utility Technology, National Hydrogen Program. We report here on the progress on system analysis and preliminary engine testing. We have done system studies of series hybrid automobiles that approach the PNGV design goal of 34 km/liter (80 mpg), for 384 km (240 mi) and 608 km (380 mi) ranges. Our results indicate that such a vehicle appears feasible using an optimized hydrogen engine. The impact of various on-board storage options on fuel economy are evaluated. Experiments with an available engine at the Sandia Combustion Research Facility demonstrated NO(x) emissions of 10 to 20 ppm at an equivalence ratio of 0.4, rising to about 500 ppm at 0.5 equivalence ratio using neat hydrogen. Hybrid vehicle simulation studies indicate that exhaust NO(x) concentrations must be less than 180 ppm to meet the 0.2 g/mile California Air Resources Board ULEV or Federal Tier-2 emissions regulations. We have designed and fabricated a first generation optimized hydrogen engine head for use on an existing single cylinder Onan engine. This head currently features 14.8:1 compression ratio, dual ignition, water cooling, two valves and open quiescent combustion chamber to minimize heat transfer losses.
A training algorithm for optimal margin classifiers
Bernhard E. Boser; Isabelle M. Guyon; Vladimir N. Vapnik
1992-01-01
A training algorithm that maximizes the margin between the training patterns and the decision boundary is presented. The technique is applicable to a wide variety of the classification functions, including Perceptrons, polynomials, and Radial Basis Functions. The effective number of parameters is adjusted automatically to match the complexity of the problem. The solution is expressed as a linear combination of
Communication-Optimal Eigenvalue/SVD Algorithms
California at Berkeley, University of
the spectrum along a curve in the com- plex plane and orthogonally transform the matrix to block triangular the spectrum of a matrix A along unit circle 1: Implicit Repeated Squaring of A-1 2: Compute invariant subspace algorithm works for generalized eigenproblem M¨obius transformations · In order to split the spectrum along
Optimization of a hybrid solar energy collector system
Shinkman, Alan M.
1981-01-01
variables for four representative farms. The primary crop in the Southern Texas High Plains is cotton. In recent years, close to 100X of the cultivated land has been planted in cotton. The second leading crop of the area is grain sorghum. Much...OPTIMIZATION OF A HYBRID SOLAR ENERGY COLLECTOR SYSTEM A Thesis by ALAN M. SHI NEMAN Submitted to the Graduate College of Texas A&N University in partial fulfillment of the requirement for the degree MASTER OF SCIENCE May 1981 Major Subject...
Hybrid photoneutron source optimization for electron accelerator-based BNCT
NASA Astrophysics Data System (ADS)
Rahmani, F.; Shahriari, M.
2010-06-01
Boron Neutron Capture Therapy (BNCT) is being studied as a possible radiotherapic treatment for some cancer types. Neutron energy for penetrating into tissue should be in the epithermal range. Different methods are used for neutron production. Electron accelerators are an alternative way for producing neutrons in electron-photon-neutron processes. Optimization of electron/photon and photoneutron targets calculations with respect to electron energy, dimension (radius and thickness) and neutron yield were done by MCNPX Monte Carlo code. According to the results, a hybrid photoneutron source including BeD 2 and Tungsten has been introduced.
Jackson, Daniel
2009-07-03
This paper presents a new general-purpose algorithm for exact solving of combinatorial many-objective optimization problems. We call this new algorithm the guided improvement algorithm. The algorithm is implemented on top ...
A local stability supported parallel distributed constraint optimization algorithm.
Peibo, Duan; Changsheng, Zhang; Bin, Zhang
2014-01-01
This paper presents a new distributed constraint optimization algorithm called LSPA, which can be used to solve large scale distributed constraint optimization problem (DCOP). Different from the access of local information in the existing algorithms, a new criterion called local stability is defined and used to evaluate which is the next agent whose value needs to be changed. The propose of local stability opens a new research direction of refining initial solution by finding key agents which can seriously effect global solution once they modify assignments. In addition, the construction of initial solution could be received more quickly without repeated assignment and conflict. In order to execute parallel search, LSPA finds final solution by constantly computing local stability of compatible agents. Experimental evaluation shows that LSPA outperforms some of the state-of-the-art incomplete distributed constraint optimization algorithms, guaranteeing better solutions received within ideal time. PMID:25105166
Study of genetic direct search algorithms for function optimization
NASA Technical Reports Server (NTRS)
Zeigler, B. P.
1974-01-01
The results are presented of a study to determine the performance of genetic direct search algorithms in solving function optimization problems arising in the optimal and adaptive control areas. The findings indicate that: (1) genetic algorithms can outperform standard algorithms in multimodal and/or noisy optimization situations, but suffer from lack of gradient exploitation facilities when gradient information can be utilized to guide the search. (2) For large populations, or low dimensional function spaces, mutation is a sufficient operator. However for small populations or high dimensional functions, crossover applied in about equal frequency with mutation is an optimum combination. (3) Complexity, in terms of storage space and running time, is significantly increased when population size is increased or the inversion operator, or the second level adaptation routine is added to the basic structure.
Comparing a Coevolutionary Genetic Algorithm for Multiobjective Optimization
NASA Technical Reports Server (NTRS)
Lohn, Jason D.; Kraus, William F.; Haith, Gary L.; Clancy, Daniel (Technical Monitor)
2002-01-01
We present results from a study comparing a recently developed coevolutionary genetic algorithm (CGA) against a set of evolutionary algorithms using a suite of multiobjective optimization benchmarks. The CGA embodies competitive coevolution and employs a simple, straightforward target population representation and fitness calculation based on developmental theory of learning. Because of these properties, setting up the additional population is trivial making implementation no more difficult than using a standard GA. Empirical results using a suite of two-objective test functions indicate that this CGA performs well at finding solutions on convex, nonconvex, discrete, and deceptive Pareto-optimal fronts, while giving respectable results on a nonuniform optimization. On a multimodal Pareto front, the CGA finds a solution that dominates solutions produced by eight other algorithms, yet the CGA has poor coverage across the Pareto front.
A Compositional Algorithm for Parallel Model Checking of Polygonal Hybrid Systems
Pace, Gordon J.
A Compositional Algorithm for Parallel Model Checking of Polygonal Hybrid Systems Gordon Pace1. The reachability problem as well as the computation of the phase portrait for the class of planar hybrid systems generally unfeasible. In this paper we present a compositional parallel al- gorithm for reachability
One-Restart Algorithm for Scheduling and Offloading in a Hybrid Cloud
Liang, Ben
,liang}@comm.utoronto.ca Abstract--The hybrid cloud architecture utilizes both privately owned cloud servers and rented instances computing. The task scheduler at a hybrid cloud decides both the selection of tasks to be offloaded offline algorithms. I. INTRODUCTION Cloud computing has emerged as a vital technology used by many
Hybrid robust predictive optimization method of power system dispatch
Chandra, Ramu Sharat (Niskayuna, NY); Liu, Yan (Ballston Lake, NY); Bose, Sumit (Niskayuna, NY); de Bedout, Juan Manuel (West Glenville, NY)
2011-08-02
A method of power system dispatch control solves power system dispatch problems by integrating a larger variety of generation, load and storage assets, including without limitation, combined heat and power (CHP) units, renewable generation with forecasting, controllable loads, electric, thermal and water energy storage. The method employs a predictive algorithm to dynamically schedule different assets in order to achieve global optimization and maintain the system normal operation.
Genetic Algorithm Optimizes Q-LAW Control Parameters
NASA Technical Reports Server (NTRS)
Lee, Seungwon; von Allmen, Paul; Petropoulos, Anastassios; Terrile, Richard
2008-01-01
A document discusses a multi-objective, genetic algorithm designed to optimize Lyapunov feedback control law (Q-law) parameters in order to efficiently find Pareto-optimal solutions for low-thrust trajectories for electronic propulsion systems. These would be propellant-optimal solutions for a given flight time, or flight time optimal solutions for a given propellant requirement. The approximate solutions are used as good initial solutions for high-fidelity optimization tools. When the good initial solutions are used, the high-fidelity optimization tools quickly converge to a locally optimal solution near the initial solution. Q-law control parameters are represented as real-valued genes in the genetic algorithm. The performances of the Q-law control parameters are evaluated in the multi-objective space (flight time vs. propellant mass) and sorted by the non-dominated sorting method that assigns a better fitness value to the solutions that are dominated by a fewer number of other solutions. With the ranking result, the genetic algorithm encourages the solutions with higher fitness values to participate in the reproduction process, improving the solutions in the evolution process. The population of solutions converges to the Pareto front that is permitted within the Q-law control parameter space.
NASA Astrophysics Data System (ADS)
Ge, Xinmin; Fan, Yiren; Cao, Yingchang; Wang, Yang; Cong, Yunhai; Liu, Lailei
2015-06-01
To allow peak searching and parameter estimation for geological and geophysical data with multi-peak distributions, we explore a hybrid method based on a combination of the particle swarm optimization (PSO) and generalized reduced gradient (GRG) algorithms. After characterizing peaks using the additive Gaussian function, a nonlinear objective function is established, which transforms our task into a search for optimal solutions. In this process, PSO is used to obtain the initial values, aiming for global convergence, while GRG is subsequently implemented for higher stability. Iterations are stopped when the convergence criteria are satisfied. Finally, grayscale histograms of backscattering electron images of sandstone show that the proposed algorithm performs much better than other methods such as PSO, GRG, simulated annealing and differential evolution, achieving a faster convergence speed and minimal variances.
NASA Astrophysics Data System (ADS)
Chen, Zheng; Mi, Chris Chunting; Xiong, Rui; Xu, Jun; You, Chenwen
2014-02-01
This paper introduces an online and intelligent energy management controller to improve the fuel economy of a power-split plug-in hybrid electric vehicle (PHEV). Based on analytic analysis between fuel-rate and battery current at different driveline power and vehicle speed, quadratic equations are applied to simulate the relationship between battery current and vehicle fuel-rate. The power threshold at which engine is turned on is optimized by genetic algorithm (GA) based on vehicle fuel-rate, battery state of charge (SOC) and driveline power demand. The optimal battery current when the engine is on is calculated using quadratic programming (QP) method. The proposed algorithm can control the battery current effectively, which makes the engine work more efficiently and thus reduce the fuel-consumption. Moreover, the controller is still applicable when the battery is unhealthy. Numerical simulations validated the feasibility of the proposed controller.
Optimization of computer-generated binary holograms using genetic algorithms
NASA Astrophysics Data System (ADS)
Cojoc, Dan; Alexandrescu, Adrian
1999-11-01
The aim of this paper is to compare genetic algorithms against direct point oriented coding in the design of binary phase Fourier holograms, computer generated. These are used as fan-out elements for free space optical interconnection. Genetic algorithms are optimization methods which model the natural process of genetic evolution. The configuration of the hologram is encoded to form a chromosome. To start the optimization, a population of different chromosomes randomly generated is considered. The chromosomes compete, mate and mutate until the best chromosome is obtained according to a cost function. After explaining the operators that are used by genetic algorithms, this paper presents two examples with 32 X 32 genes in a chromosome. The crossover type and the number of mutations are shown to be important factors which influence the convergence of the algorithm. GA is demonstrated to be a useful tool to design namely binary phase holograms of complicate structures.
MOGA algorithm for multi-objective optimization of aircraft detection
NASA Astrophysics Data System (ADS)
Sun, Hongguang; Pan, Yuxue; Zhang, Jingbo
2006-01-01
This paper presents effective multi-objective genetic algorithms (MOGA) method, whose character lies in that evolutionary population is preference ranked based on concordance model, which was applied to a multi-objective optimization of aircraft, measure of fitness degree was discussed as an emphasis. The solutions were analyzed and compares with original BP neural networks algorithm, which is better than the network trained only on alternating momentum, which can performed well neural networks and have shown the superiority to the network structure. Based the pareto optimal approaches are equipped with a fast identifying ability in capturing the learned objects, and in the meantime it can adapt the new objects. The experiments with variety of image show that the method proposed is efficient and useful, the result demonstrates that convergence speed is faster than traditional algorithm; target was recognized by this algorithm and can increase recognition precision.
Efficient Optimally Lazy Algorithms for Minimal-Interval Semantics
Vigna, Sebastiano
2007-01-01
Minimal-interval semantics associates with each query over a document a set of intervals, called witnesses, that are incomparable with respect to inclusion (i.e., they form an antichain): witnesses define the minimal regions of the document satisfying the query. Minimal-interval semantics makes it easy to define and compute several sophisticated proximity operators, provides snippets for user presentation, and can be used to rank documents. In this paper we provide algorithms for computing conjunction and disjunction that are linear in the number of intervals and logarithmic in the number of operands; for additional operators, such as ordered conjunction and Brouwerian difference, we provide linear algorithms. In all cases, space is linear in the number of operands. More importantly, we define a formal notion of optimal laziness, and either prove it, or prove its impossibility, for each algorithm. Optimal laziness implies that the algorithms do not assume random access to the input intervals, and read as litt...
NASA Astrophysics Data System (ADS)
Lin, Wei-Song; Zheng, Chen-Hong
2011-03-01
Energy management of a fuel cell/ultracapacitor hybrid power system aims to optimize energy efficiency while satisfying the operational constraints. The current challenges include ensuring that the non-linear dynamics and energy management of a hybrid power system are consistent with state and input constraints imposed by operational limitations. This paper formulates the requirements for energy management of the hybrid power system as a constrained optimal-control problem, and then transforms the problem into an unconstrained form using the penalty-function method. Radial-basis-function networks are organized in an adaptive optimal-control algorithm to synthesize an optimal strategy for energy management. The obtained optimal strategy was verified in an electric vehicle powered by combining a fuel-cell system and an ultracapacitor bank. Driving-cycle tests were conducted to investigate the fuel consumption, fuel-cell peak power, and instantaneous rate of change in fuel-cell power. The results show that the energy efficiency of the electric vehicle is significantly improved relative to that without using the optimal strategy.
EFFICIENT OPTIMIZATION ALGORITHMS FOR LEARNING Ruslan Salakhutdinov
Roweis, Sam
& To my dear sister Olya and brother-in-law Steve for their invaluable advice and support & To my adoring Whye Teh. Their discussions and presentations during weekly group meet- ings have been one of my best optimization, that possess superior convergence over standard existing methods. ii #12;Dedication To my loving
NASA Astrophysics Data System (ADS)
Sun, Dongye; Lin, Xinyou; Qin, Datong; Deng, Tao
2012-11-01
Energy management(EM) is a core technique of hybrid electric bus(HEB) in order to advance fuel economy performance optimization and is unique for the corresponding configuration. There are existing algorithms of control strategy seldom take battery power management into account with international combustion engine power management. In this paper, a type of power-balancing instantaneous optimization(PBIO) energy management control strategy is proposed for a novel series-parallel hybrid electric bus. According to the characteristic of the novel series-parallel architecture, the switching boundary condition between series and parallel mode as well as the control rules of the power-balancing strategy are developed. The equivalent fuel model of battery is implemented and combined with the fuel of engine to constitute the objective function which is to minimize the fuel consumption at each sampled time and to coordinate the power distribution in real-time between the engine and battery. To validate the proposed strategy effective and reasonable, a forward model is built based on Matlab/Simulink for the simulation and the dSPACE autobox is applied to act as a controller for hardware in-the-loop integrated with bench test. Both the results of simulation and hardware-in-the-loop demonstrate that the proposed strategy not only enable to sustain the battery SOC within its operational range and keep the engine operation point locating the peak efficiency region, but also the fuel economy of series-parallel hybrid electric bus(SPHEB) dramatically advanced up to 30.73% via comparing with the prototype bus and a similar improvement for PBIO strategy relative to rule-based strategy, the reduction of fuel consumption is up to 12.38%. The proposed research ensures the algorithm of PBIO is real-time applicability, improves the efficiency of SPHEB system, as well as suite to complicated configuration perfectly.
A new efficient optimal path planner for mobile robot based on Invasive Weed Optimization algorithm
NASA Astrophysics Data System (ADS)
Mohanty, Prases K.; Parhi, Dayal R.
2014-12-01
Planning of the shortest/optimal route is essential for efficient operation of autonomous mobile robot or vehicle. In this paper Invasive Weed Optimization (IWO), a new meta-heuristic algorithm, has been implemented for solving the path planning problem of mobile robot in partially or totally unknown environments. This meta-heuristic optimization is based on the colonizing property of weeds. First we have framed an objective function that satisfied the conditions of obstacle avoidance and target seeking behavior of robot in partially or completely unknown environments. Depending upon the value of objective function of each weed in colony, the robot avoids obstacles and proceeds towards destination. The optimal trajectory is generated with this navigational algorithm when robot reaches its destination. The effectiveness, feasibility, and robustness of the proposed algorithm has been demonstrated through series of simulation and experimental results. Finally, it has been found that the developed path planning algorithm can be effectively applied to any kinds of complex situation.
A hybrid approach to near-optimal launch vehicle guidance
NASA Astrophysics Data System (ADS)
Leung, Martin S. K.; Calise, Anthony J.
1992-08-01
This paper evaluates a proposed hybrid analytical/numerical approach to launch-vehicle guidance for ascent to orbit injection. The feedback-guidance approach is based on a piecewise nearly analytic zero-order solution evaluated using a collocation method. The zero-order solution is then improved through a regular perturbation analysis, wherein the neglected dynamics are corrected in the first-order term. For real-time implementation, the guidance approach requires solving a set of small dimension nonlinear algebraic equations and performing quadrature. Assessment of performance and reliability are carried out through closed-loop simulation for a vertically launched 2-stage heavy-lift capacity vehicle to a low earth orbit. The solutions are compared with optimal solutions generated from a multiple shooting code. In the example the guidance approach delivers over 99.9 percent of optimal performance and terminal constraint accuracy.
Environmental Optimization: Applications of Genetic Algorithms
Sue Ellen Haupt
The genetic algorithm (GA) has found wide acceptance in many fields, ranging from economics through engineering. In the environmental\\u000a sciences, some disciplines are using GAs regularly as a tool to solve typical problems; while in other areas, they have hardly\\u000a been assessed for use in research projects. The key to using GAs in environmental sciences is to pose the problem
New near-optimal feedback guidance algorithms for space missions
NASA Astrophysics Data System (ADS)
Hawkins, Matthew Jay
This dissertation describes several different spacecraft guidance algorithms, with applications including asteroid intercept and rendezvous, planetary landing, and orbital transfer. A comprehensive review of spacecraft guidance algorithms for asteroid intercept and rendezvous. Zero-Effort-Miss/Zero-Effort-Velocity (ZEM/ZEV) guidance is introduced and applied to asteroid intercept and rendezvous, and to a wealth of different example problems, including missile intercept, planetary landing, and orbital transfer. It is seen that the ZEM/ZEV guidance law can be used in many different scenarios, and that it provides near-optimal performance where an analytical optimal guidance law does not exist, such as in a non-linear gravity field.
A Parallel Particle Swarm Optimization Algorithm Accelerated by Asynchronous Evaluations
NASA Technical Reports Server (NTRS)
Venter, Gerhard; Sobieszczanski-Sobieski, Jaroslaw
2005-01-01
A parallel Particle Swarm Optimization (PSO) algorithm is presented. Particle swarm optimization is a fairly recent addition to the family of non-gradient based, probabilistic search algorithms that is based on a simplified social model and is closely tied to swarming theory. Although PSO algorithms present several attractive properties to the designer, they are plagued by high computational cost as measured by elapsed time. One approach to reduce the elapsed time is to make use of coarse-grained parallelization to evaluate the design points. Previous parallel PSO algorithms were mostly implemented in a synchronous manner, where all design points within a design iteration are evaluated before the next iteration is started. This approach leads to poor parallel speedup in cases where a heterogeneous parallel environment is used and/or where the analysis time depends on the design point being analyzed. This paper introduces an asynchronous parallel PSO algorithm that greatly improves the parallel e ciency. The asynchronous algorithm is benchmarked on a cluster assembled of Apple Macintosh G5 desktop computers, using the multi-disciplinary optimization of a typical transport aircraft wing as an example.
On the optimality of the neighbor-joining algorithm
Eickmeyer, Kord; Huggins, Peter; Pachter, Lior; Yoshida, Ruriko
2008-01-01
The popular neighbor-joining (NJ) algorithm used in phylogenetics is a greedy algorithm for finding the balanced minimum evolution (BME) tree associated to a dissimilarity map. From this point of view, NJ is "optimal" when the algorithm outputs the tree which minimizes the balanced minimum evolution criterion. We use the fact that the NJ tree topology and the BME tree topology are determined by polyhedral subdivisions of the spaces of dissimilarity maps R+(n2) to study the optimality of the neighbor-joining algorithm. In particular, we investigate and compare the polyhedral subdivisions for n ? 8. This requires the measurement of volumes of spherical polytopes in high dimension, which we obtain using a combination of Monte Carlo methods and polyhedral algorithms. Our results include a demonstration that highly unrelated trees can be co-optimal in BME reconstruction, and that NJ regions are not convex. We obtain the l2 radius for neighbor-joining for n = 5 and we conjecture that the ability of the neighbor-joining algorithm to recover the BME tree depends on the diameter of the BME tree. PMID:18447942
Chaos time series prediction based on membrane optimization algorithms.
Li, Meng; Yi, Liangzhong; Pei, Zheng; Gao, Zhisheng; Peng, Hong
2015-01-01
This paper puts forward a prediction model based on membrane computing optimization algorithm for chaos time series; the model optimizes simultaneously the parameters of phase space reconstruction (?, m) and least squares support vector machine (LS-SVM) (?, ?) by using membrane computing optimization algorithm. It is an important basis for spectrum management to predict accurately the change trend of parameters in the electromagnetic environment, which can help decision makers to adopt an optimal action. Then, the model presented in this paper is used to forecast band occupancy rate of frequency modulation (FM) broadcasting band and interphone band. To show the applicability and superiority of the proposed model, this paper will compare the forecast model presented in it with conventional similar models. The experimental results show that whether single-step prediction or multistep prediction, the proposed model performs best based on three error measures, namely, normalized mean square error (NMSE), root mean square error (RMSE), and mean absolute percentage error (MAPE). PMID:25874249
Optimization Algorithm for the Generation of ONCV Pseudopotentials
Schlipf, Martin
2015-01-01
We present an optimization algorithm to construct pseudopotentials and use it to generate a set of Optimized Norm-Conserving Vanderbilt (ONCV) pseudopotentials for elements up to Z=83 (Bi) (excluding Lanthanides). We introduce a quality function that assesses the agreement of a pseudopotential calculation with all-electron FLAPW results, and the necessary plane-wave energy cutoff. This quality function allows us to use a Nelder-Mead optimization algorithm on a training set of materials to optimize the input parameters of the pseudopotential construction for most of the periodic table. We control the accuracy of the resulting pseudopotentials on a test set of materials independent of the training set. We find that the automatically constructed pseudopotentials provide a good agreement with the all-electron results obtained using the FLEUR code with a plane-wave energy cutoff of approximately 60 Ry.
Optimization Algorithm for the Generation of ONCV Pseudopotentials
NASA Astrophysics Data System (ADS)
Schlipf, Martin; Gygi, Francois
2015-03-01
We present an optimization algorithm to construct pseudopotentials and use it to generate a set of Optimized Norm-Conserving Vanderbilt (ONCV) pseudopotentials for elements up to Z=83 (Bi) (excluding Lanthanides). We introduce a quality function that assesses the agreement of a pseudopotential calculation with all-electron FLAPW results, and the necessary plane-wave energy cutoff. This quality function allows us to use a Nelder-Mead optimization algorithm on a training set of materials to optimize the input parameters of the pseudopotential construction for most of the periodic table. We control the accuracy of the resulting pseudopotentials on a test set of materials independent of the training set. We find that the automatically constructed pseudopotentials provide a good agreement with the all-electron results obtained using the FLEUR code with a plane-wave energy cutoff of approximately 60 Ry. Supported by DOE/BES Grant DE-SC0008938.
Flow Control Optimization Using Neural Networks and Genetic Algorithms
Raymond P. LeBeau; Narendra K. Beliganur; Thomas Hauser
Evolutionary algorithms have now been used as tool to optimize complex design spaces in aerospace applications, notably in\\u000a the areas of Multidisciplinary Design Optimization (MDO) [4, 2] and flow control [9]. However, in the latter area a limiting\\u000a factor has been the cost of evaluating the performance of each tested flow control configuration. This process is conventionally\\u000a accomplished through computational
An efficient cuckoo search algorithm for numerical function optimization
NASA Astrophysics Data System (ADS)
Ong, Pauline; Zainuddin, Zarita
2013-04-01
Cuckoo search algorithm which reproduces the breeding strategy of the best known brood parasitic bird, the cuckoos has demonstrated its superiority in obtaining the global solution for numerical optimization problems. However, the involvement of fixed step approach in its exploration and exploitation behavior might slow down the search process considerably. In this regards, an improved cuckoo search algorithm with adaptive step size adjustment is introduced and its feasibility on a variety of benchmarks is validated. The obtained results show that the proposed scheme outperforms the standard cuckoo search algorithm in terms of convergence characteristic while preserving the fascinating features of the original method.
NASA Astrophysics Data System (ADS)
Hassan, Md. Rakib; Islam, Md. Monirul; Murase, Kazuyuki
Ant Colony Optimization (ACO) algorithms are a new branch of swarm intelligence. They have been applied to solve different combinatorial optimization problems successfully. Their performance is very promising when they solve small problem instances. However, the algorithms' time complexity increase and solution quality decrease for large problem instances. So, it is crucial to reduce the time requirement and at the same time to increase the solution quality for solving large combinatorial optimization problems by the ACO algorithms. This paper introduces a Local Search based ACO algorithm (LSACO), a new algorithm to solve large combinatorial optimization problems. The basis of LSACO is to apply an adaptive local search method to improve the solution quality. This local search automatically determines the number of edges to exchange during the execution of the algorithm. LSACO also applies pheromone updating rule and constructs solutions in a new way so as to decrease the convergence time. The performance of LSACO has been evaluated on a number of benchmark combinatorial optimization problems and results are compared with several existing ACO algorithms. Experimental results show that LSACO is able to produce good quality solutions with a higher rate of convergence for most of the problems.
Nonconvex network optimization: Algorithms and software
Lamar, B.
1994-12-31
Although very efficient solution methods exist for linear and convex network optimization problems, minimum cost network flow problems with concave arc cost functions are challenging because the determination of the optimal solution requires, in the worst case, an evaluation of all the extreme points in the feasible region. Even more challenging, are network flow problems whose arc costs are neither concave nor convex as is the case for problems with price breaks or all-unit discounting. Yet, such situations arise frequently in many real-world problems. In this talk, solution methods for concave cost network flow problems will be reviewed and a computer software package will be presented. In addition, a method for converting networks with arbitrary arc costs into a pure concave cost network will be described.
Optimal Placement Algorithms for Virtual Machines
Bellur, Umesh; SD, Madhu Kumar
2010-01-01
Cloud computing provides a computing platform for the users to meet their demands in an efficient, cost-effective way. Virtualization technologies are used in the clouds to aid the efficient usage of hardware. Virtual machines (VMs) are utilized to satisfy the user needs and are placed on physical machines (PMs) of the cloud for effective usage of hardware resources and electricity in the cloud. Optimizing the number of PMs used helps in cutting down the power consumption by a substantial amount. In this paper, we present an optimal technique to map virtual machines to physical machines (nodes) such that the number of required nodes is minimized. We provide two approaches based on linear programming and quadratic programming techniques that significantly improve over the existing theoretical bounds and efficiently solve the problem of virtual machine (VM) placement in data centers.
A social learning particle swarm optimization algorithm for scalable optimization
Jin, Yaochu
into particle swarm optimization (PSO) to develop a social learning PSO (SL-PSO). Unlike classical PSO variants in the proposed SL-PSO learns from any better particles (termed demonstrators) in the current swarm. In addition, to ease the burden of parameter settings, the proposed SL-PSO adopts a dimension-dependent parameter
Towards a Hybrid Energy Efficient Multi-Tree-Based Optimized Routing Protocol for Wireless Networks
Mitton, Nathalie; Razafindralambo, Tahiry; Simplot-Ryl, David; Stojmenovic, Ivan
2012-01-01
This paper considers the problem of designing power efficient routing with guaranteed delivery for sensor networks with unknown geographic locations. We propose HECTOR, a hybrid energy efficient tree-based optimized routing protocol, based on two sets of virtual coordinates. One set is based on rooted tree coordinates, and the other is based on hop distances toward several landmarks. In HECTOR, the node currently holding the packet forwards it to its neighbor that optimizes ratio of power cost over distance progress with landmark coordinates, among nodes that reduce landmark coordinates and do not increase distance in tree coordinates. If such a node does not exist, then forwarding is made to the neighbor that reduces tree-based distance only and optimizes power cost over tree distance progress ratio. We theoretically prove the packet delivery and propose an extension based on the use of multiple trees. Our simulations show the superiority of our algorithm over existing alternatives while guaranteeing delivery, and only up to 30% additional power compared to centralized shortest weighted path algorithm. PMID:23443398
Stefan Wagner; Gabriel Kronberger
2012-01-01
This tutorial demonstrates how to apply and analyze metaheuristic optimization algorithms using the HeuristicLab open source optimization environment. It is shown how to parameterize and execute evolutionary algorithms to solve combinatorial optimization problems (traveling salesman, vehicle routing) as well as data analysis problems (regression, classification). The attendees learn how to assemble different algorithms and parameter settings to large scale optimization
Optimal Design of Geodetic Network Using Genetic Algorithms
NASA Astrophysics Data System (ADS)
Vajedian, Sanaz; Bagheri, Hosein
2010-05-01
A geodetic network is a network which is measured exactly by techniques of terrestrial surveying based on measurement of angles and distances and can control stability of dams, towers and their around lands and can monitor deformation of surfaces. The main goals of an optimal geodetic network design process include finding proper location of control station (First order Design) as well as proper weight of observations (second order observation) in a way that satisfy all the criteria considered for quality of the network with itself is evaluated by the network's accuracy, reliability (internal and external), sensitivity and cost. The first-order design problem, can be dealt with as a numeric optimization problem. In this designing finding unknown coordinates of network stations is an important issue. For finding these unknown values, network geodetic observations that are angle and distance measurements must be entered in an adjustment method. In this regard, using inverse problem algorithms is needed. Inverse problem algorithms are methods to find optimal solutions for given problems and include classical and evolutionary computations. The classical approaches are analytical methods and are useful in finding the optimum solution of a continuous and differentiable function. Least squares (LS) method is one of the classical techniques that derive estimates for stochastic variables and their distribution parameters from observed samples. The evolutionary algorithms are adaptive procedures of optimization and search that find solutions to problems inspired by the mechanisms of natural evolution. These methods generate new points in the search space by applying operators to current points and statistically moving toward more optimal places in the search space. Genetic algorithm (GA) is an evolutionary algorithm considered in this paper. This algorithm starts with definition of initial population, and then the operators of selection, replication and variation are applied to obtain the solution of problem. In this research, the first step is to design a geodetic network and do the observations of the distances and angles between network's stations. The second step is to use the optimization algorithms to estimate unknown values of stations' coordinates, with regards to calculation equations of length and angle. The result indicates that The Genetic algorithms have been successfully employed for solving inverse problems in engineering disciplines. And it seems that many complex problems can be better solved using genetic algorithms than those of using conventional methods.
NASA Astrophysics Data System (ADS)
Davendra, Donald; Zelinka, Ivan; Senkerik, Roman; Jasek, Roman; Bialic-Davendra, Magdalena
2012-11-01
One of the new emerging application strategies for optimization is the hybridization of existing metaheuristics. The research combines the unique paradigms of solution space sampling of SOMA and memory retention capabilities of Scatter Search for the task of capacitated vehicle routing problem. The new hybrid heuristic is tested on the Taillard sets and obtains good results.
Delay-aware Cost Optimization for Dynamic Resource Provisioning in Hybrid Clouds
Fu, Xiaoming
--Hybrid cloud computing paradigm has recently be widely advocated, where Software-as-a-Service (SaaS) providers for the SaaS providers to adopt such a hybrid cloud computing paradigm. However, this critical problem remains for the hybrid cloud model by theoretically analyzing the problem with a Lyapunov optimization framework
RQSG-I: An optimized real time scheduling algorithm for tasks allocation in grid environments
Vahe Aghazarian; Arash Ghorbannia Delavar; Nima Ghazanfari Motlagh; Mohsen Khajeh Naeini
2011-01-01
In this paper we introduce an optimized real time scheduling algorithm for tasks allocation in grid environment. The modified RQSG algorithm is an optimized algorithm that is being improved using processing node and processing power parameters. The proposed algorithm allocates group jobs with prioritization rather than RQSG algorithm and also has dependent nodes in which the tasks are being done
NASA Astrophysics Data System (ADS)
Matott, L. S.; Gray, G. A.
2011-12-01
Pump-and-treat systems are a common strategy for groundwater remediation, wherein a system of extraction wells is installed at an affected site to address pollutant migration. In this context, the likely performance of candidate remedial systems is often assessed using groundwater flow modeling. When linked with an optimizer, these models can be utilized to identify a least-cost system design that nonetheless satisfies remediation goals. Moreover, the resulting design problems serve as important tools in the development and testing of optimization algorithms. For example, consider EAGLS (Evolutionary Algorithm Guiding Local Search), a recently developed derivative-free simulation-optimization code that seeks to efficiently solve nonlinear problems by hybridizing local and global search techniques. The EAGLS package was designed to specifically target mixed variable problems and has a limited ability to intelligently adapt its behavior to given problem characteristics. For instance, to solve problems in which there are no discrete or integer variables, the EAGLS code defaults to a multi-start asynchronous parallel pattern search. Therefore, to better understand the behavior of EAGLS, the algorithm was applied to a representative dual-plume pump-and-treat containment problem. A series of numerical experiments were performed involving four different formulations of the underlying pump-and-treat optimization problem, namely: (1) optimization of pumping rates, given fixed number of wells at fixed locations; (2) optimization of pumping rates and locations of a fixed number of wells; (3) optimization of pumping rates and number of wells at fixed locations; and (4) optimization of pumping rates, locations, and number of wells. Comparison of the performance of the EAGLS software with alternative search algorithms across different problem formulations yielded new insights for improving the EAGLS algorithm and enhancing its adaptive behavior.
Quad Search and Hybrid Genetic Algorithms Darrell Whitley, Deon Garrett, and JeanPaul Watson
Whitley, Darrell
Search using a Gray code representation con verges after at most 2L+ 2 evaluations on classesQuad Search and Hybrid Genetic Algorithms Darrell Whitley, Deon Garrett, and JeanPaul Watson constructed an algorithm we call Quad Search. Quad Search converges to a local optimum on unimodal 1D
Quad Search and Hybrid Genetic Algorithms Darrell Whitley, Deon Garrett, and Jean-Paul Watson
Whitley, Darrell
Search using a Gray code representation con- verges after at most ¡ evaluations on classesQuad Search and Hybrid Genetic Algorithms Darrell Whitley, Deon Garrett, and Jean-Paul Watson constructed an algorithm we call Quad Search. Quad Search converges to a local optimum on unimodal 1-D
A Hybrid Factored Frontier Algorithm for Dynamic Bayesian Networks with a
Thiagarajan, P.S.
1 A Hybrid Factored Frontier Algorithm for Dynamic Bayesian Networks with a Biopathways Application--Probability and Statistics, Symbolic and Algebraic Manipulation - Algorithms, Life and Medical Sciences - Biology and Genetics ! 1 INTRODUCTION Biological pathways are usually described by a network of biochemical reactions
A Mesh-based Robust Topology Discovery Algorithm for Hybrid Wireless Networks
Birman, Kenneth P.
A Mesh-based Robust Topology Discovery Algorithm for Hybrid Wireless Networks Ranveer CHANDRA by a wireline network. Topology information of the wireless network at these powerful nodes can be used data. In this paper we propose an algorithm for topology discovery in wireless networks with slow
Optimization of reinforced soil embankments by genetic algorithm
NASA Astrophysics Data System (ADS)
Ponterosso, P.; Fox, D. St. J.
2000-04-01
A Genetic Algorithm (GA) is described, which produces solutions to the cost optimization problem of reinforcement layout for reinforced soil slopes. These solutions incorporate different types of reinforcement within a single slope. The GA described is implemented with the aim of optimizing the cost of materials for the preliminary layout of reinforced soil embankments. The slope design method chosen is the U.K. Department of Transport HA 68/94 Design Methods for the Reinforcement of Highway Slopes by Reinforced Soil and Soil Nailing Techniques. The results confirm that there is a role for the GA in optimization of reinforced soil design.
Optimal brushless DC motor design using genetic algorithms
NASA Astrophysics Data System (ADS)
Rahideh, A.; Korakianitis, T.; Ruiz, P.; Keeble, T.; Rothman, M. T.
2010-11-01
This paper presents a method for the optimal design of a slotless permanent magnet brushless DC (BLDC) motor with surface mounted magnets using a genetic algorithm. Characteristics of the motor are expressed as functions of motor geometries. The objective function is a combination of losses, volume and cost to be minimized simultaneously. Electrical and mechanical requirements (i.e. voltage, torque and speed) and other limitations (e.g. upper and lower limits of the motor geometries) are cast into constraints of the optimization problem. One sample case is used to illustrate the design and optimization technique.
A Global Optimization Algorithm Based on Plant Growth Theory: Plant Growth Optimization
Wei Cai; Weiwei Yang; Xiaoqian Chen
2008-01-01
A novel optimization algorithm, plant growth optimization (PGO), is proposed in this paper. According to the plant growth characteristics, an artificial plant growth model is built including leaf growth, branching, phototropism and spatial occupancy. Afterward, two mechanisms are introduced and the basic process of PGO is presented in details. Three classical test problems are adopted to test the performance of
Optimal Step Nonrigid ICP Algorithms for Surface Registration Brian Amberg
Vetter, Thomas
Optimal Step Nonrigid ICP Algorithms for Surface Registration Brian Amberg University of Basel University of Basel thomas.vetter@uni-basel.ch Abstract We show how to extend the ICP framework to nonrigid nonrigid ICP framework allows the use of different regularisations, as long as they have an adjustable
BP neural network optimization based on an improved genetic algorithm
Bo Yang; Xiao-Hong Su; Ya-Dong Wang
2002-01-01
An improved genetic algorithm based on evolutionarily stable strategy is proposed to optimize the initial weights of backpropagation (BP) network in this paper. The improvement of GA lies in the introducing of a new mutation operator under control of a stable factor, which is found to be a very simple and effective searching operator. The experimental results in BP neural
DE\\/EDA: A new evolutionary algorithm for global optimization
Jianyong Sun; Qingfu Zhang; Edward P. K. Tsang
2005-01-01
Differential evolution (DE) was very successful in solving the global continuous optimization problem. It mainly uses the distance and direction information from the current population to guide its further search. Estimation of distribution algorithm (EDA) samples new solutions from a probability model which characterizes the distribution of promising solutions. This paper proposes a combination of DE and EDA (DE\\/EDA) for
Algorithmic Strategies for Optimizing the Parallel Reduction Primitive in CUDA
Moreno Maza, Marc
of well-known data- parallel primitives. Those primitives are usually invoked from the host many times, so primitive. Nevertheless, this should be done many times (one for each segment), and some of the segments mayAlgorithmic Strategies for Optimizing the Parallel Reduction Primitive in CUDA Pedro J. MartÃn
Truss topology optimization by a modified genetic algorithm
H. Kawamura; H. Ohmori; N. Kito
2002-01-01
This paper describes the use of a stochastic search procedure based on genetic algorithms for developing near-optimal topologies of load-bearing truss structures. Most existing cases these publications express the truss topology as a combination of members. These methods, however, have the disadvantage that the resulting topology may include needless members or those which overlap other members. In addition to these
The Cache Performance and Optimizations of Blocked Algorithms
Monica S. Lam; Edward E. Rothberg; Michael E. Wolf
1991-01-01
Blocking is a well-known optimization technique for improving the effectiveness of memory hierarchies. Instead of operating on entire rows or columns of an array, blocked algorithms operate on submatrices or blocks, so that data loaded into the faster levels of the memory hierarchy are reused. This paper presents cache performance data for blocked programs and evaluates several op- timizations to
GENETIC ALGORITHMS AND OPTIMIZING CHEMICAL OXYGEN-IODINE LASERS
David L. Carroll
1996-01-01
This paper presents results from the first known application of the genetic algorithm (GA) technique for optimizing the performance of a laser system (chemical, solid-state, or gaseous). The effects of elitism, single point and uniform crossover, creep mutation, different random number seeds, population size, niching and the number of children per pair of parents on the performance of the GA
Optimal Sampling Algorithms for Frequency Estimation in Distributed Data
Yi, Ke "Kevin"
Optimal Sampling Algorithms for Frequency Estimation in Distributed Data Zengfeng Huang Ke Yi to estimate the global frequency of any item with a standard deviation of N, where N denotes the total. In this paper, we study the problem of estimating the global frequencies of the items where an item's global
Environmental Optimization Using the WAste Reduction Algorithm (WAR)
Traditionally chemical process designs were optimized using purely economic measures such as rate of return. EPA scientists developed the WAste Reduction algorithm (WAR) so that environmental impacts of designs could easily be evaluated. The goal of WAR is to reduce environme...
Optimization flow control—I: basic algorithm and convergence
Steven H. Low; David E. Lapsley
1999-01-01
We propose an optimization approach to o w control where the objective is to maximize the aggregate source utility over their transmission rates. We view net- work links and sources as processors of a distributed com- putation system to solve the dual problem using gradient projection algorithm. In this system sources select trans- mission rates that maximize their own benets,
Optimization Algorithms for Site-directed Protein Recombination Experiment Planning
of Graduate Studies Dartmouth Computer Science Technical Report TR2010-672 #12;Copyright by Wei Zheng 2010 #12Optimization Algorithms for Site-directed Protein Recombination Experiment Planning A Thesis characteristics. In order to increase the "hit rate" of good variants, this thesis develops experiment planning
Optimal Sleep-Wakeup Algorithms for Barriers of Wireless Sensors
Kumar, Santosh
Optimal Sleep-Wakeup Algorithms for Barriers of Wireless Sensors Santosh Kumar Ten H. Lai Marc E,posner.1,sinha.43}@osu.edu Abstract-- The problem of sleep wakeup has been extensively studied the sleep- wakeup problem is NP-Hard for this model, several heuristics ex- ist. For the model of barrier
Numerical Optimization Algorithms and Software for Systems Biology
Saunders, Michael
2013-02-02
The basic aims of this work are: to develop reliable algorithms for solving optimization problems involving large stoi- chiometric matrices; to investigate cyclic dependency between metabolic and macromolecular biosynthetic networks; and to quantify the significance of thermodynamic constraints on prokaryotic metabolism.
Parallel evolutionary algorithms for optimization problems in aerospace engineering
J. F. Wang; J. Periaux; M. Sefrioui
2002-01-01
This paper presents the recent developments in hierarchical genetic algorithms (HGAs) to speed up the optimization of aerodynamic shapes. It first introduces HGAs, a particular instance of parallel GAs based on the notion of interconnected sub-populations evolving independently. Previous studies have shown the advantages of introducing a multi-layered hierarchical topology in parallel GAs. Such a topology allows the use of
Asymptotically Optimal Algorithms For Weather Applications of Smart Dust
Kreinovich, Vladik
Asymptotically Optimal Algorithms For Weather Applications of Smart Dust Edward Vidal Luc Longpr 79968, USA Hong Kong vladik@cs.utep.edu yyam@mae.cunh.edu.hk Abstract Smart Dust is a collection What Is ``Smart Dust'' Smart Dust is a project developed by the UniverÂ sity of California at Berkeley
Deque-Free Work-Optimal Parallel STL Algorithms
Daouda Traoré; Jean-louis Roch; Nicolas Maillard; Thierry Gautier; Julien Bernard
2008-01-01
This paper presents provable work-optimal parallelizations of STL (Standard Template Library) algorithms based on the work- stealing technique. Unlike previous approaches where a deque for each processor is typically used to locally store ready tasks and where a proces- sor that runs out of work steals a ready task from the deque of a randomly selected processor, the current paper
Extended Semantics and Optimization Algorithms for CP-Networks
Dimopoulos, Yannis
Extended Semantics and Optimization Algorithms for CP-Networks Ronen I. Brafman Dept. of Computer;cation tasks. CP-nets were designed to make the process of preference elicitation simpler and more intuitive for lay users by graphically structuring a set of Ceteris Paribus (CP) preference statements
A scaled BFGS preconditioned conjugate gradient algorithm for unconstrained optimization
Neculai Andrei
2007-01-01
This letter presents a scaled memoryless BFGS preconditioned conjugate gradient algorithm for solving unconstrained optimization problems. The basic idea is to combine the scaled memoryless BFGS method and the preconditioning technique in the frame of the conjugate gradient method. The preconditioner, which is also a scaled memoryless BFGS matrix, is reset when the Powell restart criterion holds. The parameter scaling
Scaled memoryless BFGS preconditioned conjugate gradient algorithm for unconstrained optimization
Neculai Andrei
2007-01-01
A scaled memoryless BFGS preconditioned conjugate gradient algorithm for solving unconstrained optimization problems is presented. The basic idea is to combine the scaled memoryless BFGS method and the preconditioning technique in the frame of the conjugate gradient method. The preconditioner, which is also a scaled memoryless BFGS matrix, is reset when the Beale–Powell restart criterion holds. The parameter scaling the
Sujit Ghosal; Sudipto Chaki
2010-01-01
The paper presents an artificial neural network-optimization hybrid model to predict and optimize penetration depth of CO2 LASER-MIG hybrid welding used for 5005 Al–Mg alloy. The input welding parameters are power, focal distance from the work\\u000a piece surface, torch angle, and the distance between the laser and the welding torch. The model combines single hidden layer\\u000a back propagation artificial neural
A hybrid reconstruction algorithm for fast and accurate 4D cone-beam CT imaginga)
Yan, Hao; Zhen, Xin; Folkerts, Michael; Li, Yongbao; Pan, Tinsu; Cervino, Laura; Jiang, Steve B.; Jia, Xun
2014-01-01
Purpose: 4D cone beam CT (4D-CBCT) has been utilized in radiation therapy to provide 4D image guidance in lung and upper abdomen area. However, clinical application of 4D-CBCT is currently limited due to the long scan time and low image quality. The purpose of this paper is to develop a new 4D-CBCT reconstruction method that restores volumetric images based on the 1-min scan data acquired with a standard 3D-CBCT protocol. Methods: The model optimizes a deformation vector field that deforms a patient-specific planning CT (p-CT), so that the calculated 4D-CBCT projections match measurements. A forward-backward splitting (FBS) method is invented to solve the optimization problem. It splits the original problem into two well-studied subproblems, i.e., image reconstruction and deformable image registration. By iteratively solving the two subproblems, FBS gradually yields correct deformation information, while maintaining high image quality. The whole workflow is implemented on a graphic-processing-unit to improve efficiency. Comprehensive evaluations have been conducted on a moving phantom and three real patient cases regarding the accuracy and quality of the reconstructed images, as well as the algorithm robustness and efficiency. Results: The proposed algorithm reconstructs 4D-CBCT images from highly under-sampled projection data acquired with 1-min scans. Regarding the anatomical structure location accuracy, 0.204 mm average differences and 0.484 mm maximum difference are found for the phantom case, and the maximum differences of 0.3–0.5 mm for patients 1–3 are observed. As for the image quality, intensity errors below 5 and 20 HU compared to the planning CT are achieved for the phantom and the patient cases, respectively. Signal-noise-ratio values are improved by 12.74 and 5.12 times compared to results from FDK algorithm using the 1-min data and 4-min data, respectively. The computation time of the algorithm on a NVIDIA GTX590 card is 1–1.5 min per phase. Conclusions: High-quality 4D-CBCT imaging based on the clinically standard 1-min 3D CBCT scanning protocol is feasible via the proposed hybrid reconstruction algorithm. PMID:24989381
Optimal Green Energy Utilization in MIMO Systems with Hybrid Energy Supplies
, Ericsson has developed a wind-powered tower for wireless BSs [3]. The renewable energy provides a promising1 Optimal Green Energy Utilization in MIMO Systems with Hybrid Energy Supplies Congshi Hu, Jie Gong link with hybrid energy harvesting and grid power supply. We optimize the power allocation to minimize
Supervisory Power Management Control Algorithms for Hybrid Electric Vehicles: A Survey
Malikopoulos, Andreas
2014-01-01
The growing necessity for environmentally benign hybrid propulsion systems has led to the development of advanced power management control algorithms to maximize fuel economy and minimize pollutant emissions. This paper surveys the control algorithms for hybrid electric vehicles (HEVs) and plug-in HEVs (PHEVs) that have been reported in the literature to date. The exposition ranges from parallel, series, and power split HEVs and PHEVs and includes a classification of the algorithms in terms of their implementation and the chronological order of their appearance. Remaining challenges and potential future research directions are also discussed.
Elsheikh, Ahmed H., E-mail: aelsheikh@ices.utexas.edu [Institute for Computational Engineering and Sciences (ICES), University of Texas at Austin, TX (United States); Institute of Petroleum Engineering, Heriot-Watt University, Edinburgh EH14 4AS (United Kingdom); Wheeler, Mary F. [Institute for Computational Engineering and Sciences (ICES), University of Texas at Austin, TX (United States)] [Institute for Computational Engineering and Sciences (ICES), University of Texas at Austin, TX (United States); Hoteit, Ibrahim [Department of Earth Sciences and Engineering, King Abdullah University of Science and Technology (KAUST), Thuwal (Saudi Arabia)] [Department of Earth Sciences and Engineering, King Abdullah University of Science and Technology (KAUST), Thuwal (Saudi Arabia)
2014-02-01
A Hybrid Nested Sampling (HNS) algorithm is proposed for efficient Bayesian model calibration and prior model selection. The proposed algorithm combines, Nested Sampling (NS) algorithm, Hybrid Monte Carlo (HMC) sampling and gradient estimation using Stochastic Ensemble Method (SEM). NS is an efficient sampling algorithm that can be used for Bayesian calibration and estimating the Bayesian evidence for prior model selection. Nested sampling has the advantage of computational feasibility. Within the nested sampling algorithm, a constrained sampling step is performed. For this step, we utilize HMC to reduce the correlation between successive sampled states. HMC relies on the gradient of the logarithm of the posterior distribution, which we estimate using a stochastic ensemble method based on an ensemble of directional derivatives. SEM only requires forward model runs and the simulator is then used as a black box and no adjoint code is needed. The developed HNS algorithm is successfully applied for Bayesian calibration and prior model selection of several nonlinear subsurface flow problems.
RCQ-GA: RDF Chain Query Optimization Using Genetic Algorithms
NASA Astrophysics Data System (ADS)
Hogenboom, Alexander; Milea, Viorel; Frasincar, Flavius; Kaymak, Uzay
The application of Semantic Web technologies in an Electronic Commerce environment implies a need for good support tools. Fast query engines are needed for efficient querying of large amounts of data, usually represented using RDF. We focus on optimizing a special class of SPARQL queries, the so-called RDF chain queries. For this purpose, we devise a genetic algorithm called RCQ-GA that determines the order in which joins need to be performed for an efficient evaluation of RDF chain queries. The approach is benchmarked against a two-phase optimization algorithm, previously proposed in literature. The more complex a query is, the more RCQ-GA outperforms the benchmark in solution quality, execution time needed, and consistency of solution quality. When the algorithms are constrained by a time limit, the overall performance of RCQ-GA compared to the benchmark further improves.
The optimization algorithm based knot and control point automatic adjustment
NASA Astrophysics Data System (ADS)
Jia, Xingyue; Zhao, Xiuyang
2015-03-01
Aiming at the issue of point cloud or mesh model, which can be approximated using cubic B-spline surfaces, an algorithm of optimizing the knot vector based on Gaussian Mixture Model(GMM) is proposed in this paper. In addition, the control points of sub-corner points are searched by the Particle Swarm Optimization (PSO) in the process of stitching two B-spline surfaces with different knot vectors. Compared with conventional B-spline surface skinning, the proposed algorithms have two advantages. First, the global optimum is easy to be found by statistically learning and sampling in accordance with the probability distribution of the best individuals. Second, the stitching surface obtained is much smoother and the precise of approximate surface is also higher. The effectiveness of the proposed algorithm have been demonstrated according to experimental examples.
A genetic algorithm approach in interface and surface structure optimization
Zhang, Jian
2010-05-16
The thesis is divided into two parts. In the first part a global optimization method is developed for the interface and surface structures optimization. Two prototype systems are chosen to be studied. One is Si[001] symmetric tilted grain boundaries and the other is Ag/Au induced Si(111) surface. It is found that Genetic Algorithm is very efficient in finding lowest energy structures in both cases. Not only existing structures in the experiments can be reproduced, but also many new structures can be predicted using Genetic Algorithm. Thus it is shown that Genetic Algorithm is a extremely powerful tool for the material structures predictions. The second part of the thesis is devoted to the explanation of an experimental observation of thermal radiation from three-dimensional tungsten photonic crystal structures. The experimental results seems astounding and confusing, yet the theoretical models in the paper revealed the physics insight behind the phenomena and can well reproduced the experimental results.
Evolutionary algorithms for multiobjective and multimodal optimization of diagnostic schemes.
de Toro, Francisco; Ros, Eduardo; Mota, Sonia; Ortega, Julio
2006-02-01
This paper addresses the optimization of noninvasive diagnostic schemes using evolutionary algorithms in medical applications based on the interpretation of biosignals. A general diagnostic methodology using a set of definable characteristics extracted from the biosignal source followed by the specific diagnostic scheme is presented. In this framework, multiobjective evolutionary algorithms are used to meet not only classification accuracy but also other objectives of medical interest, which can be conflicting. Furthermore, the use of both multimodal and multiobjective evolutionary optimization algorithms provides the medical specialist with different alternatives for configuring the diagnostic scheme. Some application examples of this methodology are described in the diagnosis of a specific cardiac disorder-paroxysmal atrial fibrillation. PMID:16485746
Multiobjective Optimization of Rocket Engine Pumps Using Evolutionary Algorithm
NASA Technical Reports Server (NTRS)
Oyama, Akira; Liou, Meng-Sing
2001-01-01
A design optimization method for turbopumps of cryogenic rocket engines has been developed. Multiobjective Evolutionary Algorithm (MOEA) is used for multiobjective pump design optimizations. Performances of design candidates are evaluated by using the meanline pump flow modeling method based on the Euler turbine equation coupled with empirical correlations for rotor efficiency. To demonstrate the feasibility of the present approach, a single stage centrifugal pump design and multistage pump design optimizations are presented. In both cases, the present method obtains very reasonable Pareto-optimal solutions that include some designs outperforming the original design in total head while reducing input power by one percent. Detailed observation of the design results also reveals some important design criteria for turbopumps in cryogenic rocket engines. These results demonstrate the feasibility of the EA-based design optimization method in this field.
Optimal reservoir operation policies using novel nested algorithms
NASA Astrophysics Data System (ADS)
Delipetrev, Blagoj; Jonoski, Andreja; Solomatine, Dimitri
2015-04-01
Historically, the two most widely practiced methods for optimal reservoir operation have been dynamic programming (DP) and stochastic dynamic programming (SDP). These two methods suffer from the so called "dual curse" which prevents them to be used in reasonably complex water systems. The first one is the "curse of dimensionality" that denotes an exponential growth of the computational complexity with the state - decision space dimension. The second one is the "curse of modelling" that requires an explicit model of each component of the water system to anticipate the effect of each system's transition. We address the problem of optimal reservoir operation concerning multiple objectives that are related to 1) reservoir releases to satisfy several downstream users competing for water with dynamically varying demands, 2) deviations from the target minimum and maximum reservoir water levels and 3) hydropower production that is a combination of the reservoir water level and the reservoir releases. Addressing such a problem with classical methods (DP and SDP) requires a reasonably high level of discretization of the reservoir storage volume, which in combination with the required releases discretization for meeting the demands of downstream users leads to computationally expensive formulations and causes the curse of dimensionality. We present a novel approach, named "nested" that is implemented in DP, SDP and reinforcement learning (RL) and correspondingly three new algorithms are developed named nested DP (nDP), nested SDP (nSDP) and nested RL (nRL). The nested algorithms are composed from two algorithms: 1) DP, SDP or RL and 2) nested optimization algorithm. Depending on the way we formulate the objective function related to deficits in the allocation problem in the nested optimization, two methods are implemented: 1) Simplex for linear allocation problems, and 2) quadratic Knapsack method in the case of nonlinear problems. The novel idea is to include the nested optimization algorithm into the state transition that lowers the starting problem dimension and alleviates the curse of dimensionality. The algorithms can solve multi-objective optimization problems, without significantly increasing the complexity and the computational expenses. The algorithms can handle dense and irregular variable discretization, and are coded in Java as prototype applications. The three algorithms were tested at the multipurpose reservoir Knezevo of the Zletovica hydro-system located in the Republic of Macedonia, with eight objectives, including urban water supply, agriculture, ensuring ecological flow, and generation of hydropower. Because the Zletovica hydro-system is relatively complex, the novel algorithms were pushed to their limits, demonstrating their capabilities and limitations. The nSDP and nRL derived/learned the optimal reservoir policy using 45 (1951-1995) years historical data. The nSDP and nRL optimal reservoir policy was tested on 10 (1995-2005) years historical data, and compared with nDP optimal reservoir operation in the same period. The nested algorithms and optimal reservoir operation results are analysed and explained.
Fast Optimal Load Balancing Algorithms for 1D Partitioning
Pinar, Ali; Aykanat, Cevdet
2002-12-09
One-dimensional decomposition of nonuniform workload arrays for optimal load balancing is investigated. The problem has been studied in the literature as ''chains-on-chains partitioning'' problem. Despite extensive research efforts, heuristics are still used in parallel computing community with the ''hope'' of good decompositions and the ''myth'' of exact algorithms being hard to implement and not runtime efficient. The main objective of this paper is to show that using exact algorithms instead of heuristics yields significant load balance improvements with negligible increase in preprocessing time. We provide detailed pseudocodes of our algorithms so that our results can be easily reproduced. We start with a review of literature on chains-on-chains partitioning problem. We propose improvements on these algorithms as well as efficient implementation tips. We also introduce novel algorithms, which are asymptotically and runtime efficient. We experimented with data sets from two different applications: Sparse matrix computations and Direct volume rendering. Experiments showed that the proposed algorithms are 100 times faster than a single sparse-matrix vector multiplication for 64-way decompositions on average. Experiments also verify that load balance can be significantly improved by using exact algorithms instead of heuristics. These two findings show that exact algorithms with efficient implementations discussed in this paper can effectively replace heuristics.
Optimal control of switched linear systems based on Migrant Particle Swarm Optimization algorithm
NASA Astrophysics Data System (ADS)
Xie, Fuqiang; Wang, Yongji; Zheng, Zongzhun; Li, Chuanfeng
2009-10-01
The optimal control problem for switched linear systems with internally forced switching has more constraints than with externally forced switching. Heavy computations and slow convergence in solving this problem is a major obstacle. In this paper we describe a new approach for solving this problem, which is called Migrant Particle Swarm Optimization (Migrant PSO). Imitating the behavior of a flock of migrant birds, the Migrant PSO applies naturally to both continuous and discrete spaces, in which definitive optimization algorithm and stochastic search method are combined. The efficacy of the proposed algorithm is illustrated via a numerical example.
An Efficient Hybrid Classification Algorithm -an Example from Palliative Care
Aamodt, Agnar
an efficient hybrid classification al- gorithm based on combining case-based reasoning and random decision computational cost. Keywords: hybrid reasoning systems, classifier combination, case-based reasoning, random decision trees 1 Introduction Case-based reasoning (CBR), including instance-based methods, represents
Genetic algorithms for the construction of D-optimal designs
Heredia-Langner, Alejandro; Carlyle, W M.; Montgomery, D C.; Borror, Connie M.; Runger, George C.
2003-01-01
Computer-generated designs are useful for situations where standard factorial, fractional factorial or response surface designs cannot be easily employed. Alphabetically-optimal designs are the most widely used type of computer-generated designs, and of these, the D-optimal (or D-efficient) class of designs are extremely popular. D-optimal designs are usually constructed by algorithms that sequentially add and delete points from a potential design based using a candidate set of points spaced over the region of interest. We present a technique to generate D-efficient designs using genetic algorithms (GA). This approach eliminates the need to explicitly consider a candidate set of experimental points and it can handle highly constrained regions while maintaining a level of performance comparable to more traditional design construction techniques.
Research on Optimization of Encoding Algorithm of PDF417 Barcodes
NASA Astrophysics Data System (ADS)
Sun, Ming; Fu, Longsheng; Han, Shuqing
The purpose of this research is to develop software to optimize the data compression of a PDF417 barcode using VC++6.0. According to the different compression mode and the particularities of Chinese, the relevant approaches which optimize the encoding algorithm of data compression such as spillage and the Chinese characters encoding are proposed, a simple approach to compute complex polynomial is introduced. After the whole data compression is finished, the number of the codeword is reduced and then the encoding algorithm is optimized. The developed encoding system of PDF 417 barcodes will be applied in the logistics management of fruits, therefore also will promote the fast development of the two-dimensional bar codes.
Identifying damage in spherical laminate shells by using a hybrid real-parameter genetic algorithm
Rong-Song He; Shun-Fa Hwang
2007-01-01
A hybrid algorithm combining an adaptive real-parameter genetic algorithm with simulated annealing is proposed to detect damage in a simply supported equal-sided sector of a spherical laminate shell. The proposed algorithm uses the data of natural frequencies and mode shapes. A single damage example and a two-damage example are investigated. To simulate the error on the measured data, three error
NASA Astrophysics Data System (ADS)
Tsekouras, Georgios; Ioannou, Christos; Efstratiadis, Andreas; Koutsoyiannis, Demetris
2013-04-01
The drawbacks of conventional energy sources including their negative environmental impacts emphasize the need to integrate renewable energy sources into energy balance. However, the renewable sources strongly depend on time varying and uncertain hydrometeorological processes, including wind speed, sunshine duration and solar radiation. To study the design and management of hybrid energy systems we investigate the stochastic properties of these natural processes, including possible long-term persistence. We use wind speed and sunshine duration time series retrieved from a European database of daily records and we estimate representative values of the Hurst coefficient for both variables. We conduct simultaneous generation of synthetic time series of wind speed and sunshine duration, on yearly, monthly and daily scale. To this we use the Castalia software system which performs multivariate stochastic simulation. Using these time series as input, we perform stochastic simulation of an autonomous hypothetical hybrid renewable energy system and optimize its performance using genetic algorithms. For the system design we optimize the sizing of the system in order to satisfy the energy demand with high reliability also minimizing the cost. While the simulation scale is the daily, a simple method allows utilizing the subdaily distribution of the produced wind power. Various scenarios are assumed in order to examine the influence of input parameters, such as the Hurst coefficient, and design parameters such as the photovoltaic panel angle.
Optimization with Fuzzy Data via Evolutionary Algorithms
NASA Astrophysics Data System (ADS)
Kosi?ski, Witold
2010-09-01
Order fuzzy numbers (OFN) that make possible to deal with fuzzy inputs quantitatively, exactly in the same way as with real numbers, have been recently defined by the author and his 2 coworkers. The set of OFN forms a normed space and is a partially ordered ring. The case when the numbers are presented in the form of step functions, with finite resolution, simplifies all operations and the representation of defuzzification functionals. A general optimization problem with fuzzy data is formulated. Its fitness function attains fuzzy values. Since the adjoint space to the space of OFN is finite dimensional, a convex combination of all linear defuzzification functionals may be used to introduce a total order and a real-valued fitness function. Genetic operations on individuals representing fuzzy data are defined.
Dervis Karaboga; Bahriye Basturk
2007-01-01
Swarm intelligence is a research branch that models the population of interacting agents or swarms that are able to self-organize.\\u000a An ant colony, a flock of birds or an immune system is a typical example of a swarm system. Bees’ swarming around their hive\\u000a is another example of swarm intelligence. Artificial Bee Colony (ABC) Algorithm is an optimization algorithm based
Optimal design of passive linear suspension using genetic algorithm
NASA Astrophysics Data System (ADS)
Alkhatib, R.; Nakhaie Jazar, G.; Golnaraghi, M. F.
2004-08-01
In this paper the genetic algorithm (GA) method is applied to the optimization problem of a linear one-degree-of-freedom (1-DOF) vibration isolator mount and the method is extended to the optimization of a linear quarter car suspension model. A novel criterion for selecting optimal suspension parameters is presented. An optimal relationship between the root mean square (RMS) of the absolute acceleration and the RMS of the relative displacement is found. Although the systems are linear, it is difficult to find such optimal relation analytically. The optimum solution is obtained numerically by utilizing GA and employing a cost function that seeks minimizing absolute acceleration RMS sensitivity to changes in relative displacement RMS. The combination of RMS and absolute acceleration sensitivity minimization produces optimal suspension that is robust to broadband frequency excitation. The GA method increases the probability of finding the global optimum solution and avoids convergence to a local minimum which is a drawback of gradient-based methods. Given allowable mount relative displacement (working space), designers can use the results to specify the optimal mount and suspension. The cost function employed can be extended to optimize multi-DOF (MDOF) and non-linear vibrating mechanical systems in frequency domain. Applying the method to a linear quarter car model illustrates the applicability of the method to MDOF systems. An example is given to demonstrate the optimality of the solution obtained by the GA technique.
Double-layer evolutionary algorithm for distributed optimization of particle detection on the Grid
NASA Astrophysics Data System (ADS)
Padée, Adam; Kurek, Krzysztof; Zaremba, Krzysztof
2013-08-01
Reconstruction of particle tracks from information collected by position-sensitive detectors is an important procedure in HEP experiments. It is usually controlled by a set of numerical parameters which have to be manually optimized. This paper proposes an automatic approach to this task by utilizing evolutionary algorithm (EA) operating on both real-valued and binary representations. Because of computational complexity of the task a special distributed architecture of the algorithm is proposed, designed to be run in grid environment. It is two-level hierarchical hybrid utilizing asynchronous master-slave EA on the level of clusters and island model EA on the level of the grid. The technical aspects of usage of production grid infrastructure are covered, including communication protocols on both levels. The paper deals also with the problem of heterogeneity of the resources, presenting efficiency tests on a benchmark function. These tests confirm that even relatively small islands (clusters) can be beneficial to the optimization process when connected to the larger ones. Finally a real-life usage example is presented, which is an optimization of track reconstruction in Large Angle Spectrometer of NA-58 COMPASS experiment held at CERN, using a sample of Monte Carlo simulated data. The overall reconstruction efficiency gain, achieved by the proposed method, is more than 4%, compared to the manually optimized parameters.
Algorithmic Aspects of Optimal Channel Coding
Siddharth Barman; Omar Fawzi
2015-08-17
A central question in information theory is to determine the maximum success probability that can be achieved in sending a fixed number of messages over a noisy channel. This was first studied in the pioneering work of Shannon who established a simple expression characterizing this quantity in the limit of multiple independent uses of the channel. Here we consider the general setting with only one use of the channel. We observe that the maximum success probability can be expressed as the maximum value of a submodular function. Using this connection, we establish the following results: 1. There is a simple greedy polynomial-time algorithm that computes a code achieving a (1-1/e)-approximation of the maximum success probability. Moreover, for this problem it is NP-hard to obtain an approximation ratio strictly better than (1-1/e). 2. Shared quantum entanglement between the sender and the receiver can increase the success probability by a factor of at most 1/(1-1/e). In addition, this factor is tight if one allows an arbitrary non-signaling box between the sender and the receiver. 3. We give tight bounds on the one-shot performance of the meta-converse of Polyanskiy-Poor-Verdu.
Tolbert, Leon M.
-bridge converters in series as shown in Fig. 1 for a 7-level inverter. Each converter generates a square wave; therefore they have reduced harmonics compared to a square wave inverter. To reduce the harmonics furtherHarmonic Optimization of Multilevel Converters Using Genetic Algorithms Abstract-- In this paper
Using genetic algorithms to search for an optimal investment strategy
NASA Astrophysics Data System (ADS)
Mandere, Edward; Xi, Haowen
2007-10-01
In this experiment we used genetic algorithms to search for an investment strategy by dividing capital among different stocks with varying returns. The algorithm involves having a ``manager'' who divides his capital among various ``experts'' each of whom has a simple investment strategy. The expert strategies act like genes, experiencing mutation and crossover, in a selection process using previous returns as the fitness function. When algorithm was run with test data where the optimal strategy favored non-uniform investment in one stock it consistently beat a simple buy hold. However when the algorithm was run on actual stock data the system overwhelmingly stabilized at a population that closely resembled a simple buy hold portfolio, that is, evenly distribute the capital among all stocks.
Multidisciplinary Multiobjective Optimal Design for Turbomachinery Using Evolutionary Algorithm
NASA Technical Reports Server (NTRS)
2005-01-01
This report summarizes Dr. Lian s efforts toward developing a robust and efficient tool for multidisciplinary and multi-objective optimal design for turbomachinery using evolutionary algorithms. This work consisted of two stages. The first stage (from July 2003 to June 2004) Dr. Lian focused on building essential capabilities required for the project. More specifically, Dr. Lian worked on two subjects: an enhanced genetic algorithm (GA) and an integrated optimization system with a GA and a surrogate model. The second stage (from July 2004 to February 2005) Dr. Lian formulated aerodynamic optimization and structural optimization into a multi-objective optimization problem and performed multidisciplinary and multi-objective optimizations on a transonic compressor blade based on the proposed model. Dr. Lian s numerical results showed that the proposed approach can effectively reduce the blade weight and increase the stage pressure ratio in an efficient manner. In addition, the new design was structurally safer than the original design. Five conference papers and three journal papers were published on this topic by Dr. Lian.
NASA Astrophysics Data System (ADS)
Borhan, Hoseinali
Modern hybrid electric vehicles and many stationary renewable power generation systems combine multiple power generating and energy storage devices to achieve an overall system-level efficiency and flexibility which is higher than their individual components. The power or energy management control, "brain" of these "hybrid" systems, determines adaptively and based on the power demand the power split between multiple subsystems and plays a critical role in overall system-level efficiency. This dissertation proposes that a receding horizon optimal control (aka Model Predictive Control) approach can be a natural and systematic framework for formulating this type of power management controls. More importantly the dissertation develops new results based on the classical theory of optimal control that allow solving the resulting optimal control problem in real-time, in spite of the complexities that arise due to several system nonlinearities and constraints. The dissertation focus is on two classes of hybrid systems: hybrid electric vehicles in the first part and wind farms with battery storage in the second part. The first part of the dissertation proposes and fully develops a real-time optimization-based power management strategy for hybrid electric vehicles. Current industry practice uses rule-based control techniques with "else-then-if" logic and look-up maps and tables in the power management of production hybrid vehicles. These algorithms are not guaranteed to result in the best possible fuel economy and there exists a gap between their performance and a minimum possible fuel economy benchmark. Furthermore, considerable time and effort are spent calibrating the control system in the vehicle development phase, and there is little flexibility in real-time handling of constraints and re-optimization of the system operation in the event of changing operating conditions and varying parameters. In addition, a proliferation of different powertrain configurations may result in the need for repeated control system redesign. To address these shortcomings, we formulate the power management problem as a nonlinear and constrained optimal control problem. Solution of this optimal control problem in real-time on chronometric- and memory-constrained automotive microcontrollers is quite challenging; this computational complexity is due to the highly nonlinear dynamics of the powertrain subsystems, mixed-integer switching modes of their operation, and time-varying and nonlinear hard constraints that system variables should satisfy. The main contribution of the first part of the dissertation is that it establishes methods for systematic and step-by step improvements in fuel economy while maintaining the algorithmic computational requirements in a real-time implementable framework. More specifically a linear time-varying model predictive control approach is employed first which uses sequential quadratic programming to find sub-optimal solutions to the power management problem. Next the objective function is further refined and broken into a short and a long horizon segments; the latter approximated as a function of the state using the connection between the Pontryagin minimum principle and Hamilton-Jacobi-Bellman equations. The power management problem is then solved using a nonlinear MPC framework with a dynamic programming solver and the fuel economy is further improved. Typical simplifying academic assumptions are minimal throughout this work, thanks to close collaboration with research scientists at Ford research labs and their stringent requirement that the proposed solutions be tested on high-fidelity production models. Simulation results on a high-fidelity model of a hybrid electric vehicle over multiple standard driving cycles reveal the potential for substantial fuel economy gains. To address the control calibration challenges, we also present a novel and fast calibration technique utilizing parallel computing techniques. ^ The second part of this dissertation presents an optimization-based control str
Vincylloyd, F.; Anand, B.
2015-01-01
In wireless communication systems, mobility tracking deals with determining a mobile subscriber (MS) covering the area serviced by the wireless network. Tracking a mobile subscriber is governed by the two fundamental components called location updating (LU) and paging. This paper presents a novel hybrid method using a krill herd algorithm designed to optimize the location area (LA) within available spectrum such that total network cost, comprising location update (LU) cost and cost for paging, is minimized without compromise. Based on various mobility patterns of users and network architecture, the design of the LR area is formulated as a combinatorial optimization problem. Numerical results indicate that the proposed model provides a more accurate update boundary in real environment than that derived from a hexagonal cell configuration with a random walk movement pattern. The proposed model allows the network to maintain a better balance between the processing incurred due to location update and the radio bandwidth utilized for paging between call arrivals. PMID:25879061
Vincylloyd, F; Anand, B
2015-01-01
In wireless communication systems, mobility tracking deals with determining a mobile subscriber (MS) covering the area serviced by the wireless network. Tracking a mobile subscriber is governed by the two fundamental components called location updating (LU) and paging. This paper presents a novel hybrid method using a krill herd algorithm designed to optimize the location area (LA) within available spectrum such that total network cost, comprising location update (LU) cost and cost for paging, is minimized without compromise. Based on various mobility patterns of users and network architecture, the design of the LR area is formulated as a combinatorial optimization problem. Numerical results indicate that the proposed model provides a more accurate update boundary in real environment than that derived from a hexagonal cell configuration with a random walk movement pattern. The proposed model allows the network to maintain a better balance between the processing incurred due to location update and the radio bandwidth utilized for paging between call arrivals. PMID:25879061
Hybrid Neural Network and Support Vector Machine Method for Optimization
NASA Technical Reports Server (NTRS)
Rai, Man Mohan (Inventor)
2007-01-01
System and method for optimization of a design associated with a response function, using a hybrid neural net and support vector machine (NN/SVM) analysis to minimize or maximize an objective function, optionally subject to one or more constraints. As a first example, the NN/SVM analysis is applied iteratively to design of an aerodynamic component, such as an airfoil shape, where the objective function measures deviation from a target pressure distribution on the perimeter of the aerodynamic component. As a second example, the NN/SVM analysis is applied to data classification of a sequence of data points in a multidimensional space. The NN/SVM analysis is also applied to data regression.
HYBRID NEURAL NETWORK AND SUPPORT VECTOR MACHINE METHOD FOR OPTIMIZATION
NASA Technical Reports Server (NTRS)
Rai, Man Mohan (Inventor)
2005-01-01
System and method for optimization of a design associated with a response function, using a hybrid neural net and support vector machine (NN/SVM) analysis to minimize or maximize an objective function, optionally subject to one or more constraints. As a first example, the NN/SVM analysis is applied iteratively to design of an aerodynamic component, such as an airfoil shape, where the objective function measures deviation from a target pressure distribution on the perimeter of the aerodynamic component. As a second example, the NN/SVM analysis is applied to data classification of a sequence of data points in a multidimensional space. The NN/SVM analysis is also applied to data regression.
Optimal on-line algorithms to minimize makespan on two machines with resource augmentation
Epstein, Leah
where the on-line algorithm has resources different from those of the off-line algorithm. We consider. This is allowed to both the on-line algorithm and the off-line algorithm. The measure. A schedule is measured() are the makespans of the schedules created by the on-line algorithm and an optimal off-line algorithm for that list
Computational experiments for local search algorithms for binary and mixed integer optimization
Zhou, Jingting, S.M. Massachusetts Institute of Technology
2010-01-01
In this thesis, we implement and test two algorithms for binary optimization and mixed integer optimization, respectively. We fine tune the parameters of these two algorithms and achieve satisfactory performance. We also ...
OPTIMIZATION OF TURBOMACHINERY AIRFOILS WITH A GENETIC/SEQUENTIAL QUADRATIC PROGRAMMING ALGORITHM
Dennis, Brian
OPTIMIZATION OF TURBOMACHINERY AIRFOILS WITH A GENETIC/SEQUENTIAL QUADRATIC PROGRAMMING ALGORITHM words: shape optimization, aerodynamic design, turbomachinery, aerodynamics, genetic algorithms-magneto- gasdynamic effects. In the case of a turbomachinery aerodynamics, sources of entropy production other than
A Multi-Objective Ant Colony Optimization Algorithm for Infrastructure Routing
McDonald, Walter
2012-07-16
An algorithm is presented that is capable of producing Pareto-optimal solutions for multi-objective infrastructure routing problems: the Multi-Objective Ant Colony Optimization (MOACO). This algorithm offers a constructive search technique...
Designing Artificial Neural Networks Using Particle Swarm Optimization Algorithms.
Garro, Beatriz A; Vázquez, Roberto A
2015-01-01
Artificial Neural Network (ANN) design is a complex task because its performance depends on the architecture, the selected transfer function, and the learning algorithm used to train the set of synaptic weights. In this paper we present a methodology that automatically designs an ANN using particle swarm optimization algorithms such as Basic Particle Swarm Optimization (PSO), Second Generation of Particle Swarm Optimization (SGPSO), and a New Model of PSO called NMPSO. The aim of these algorithms is to evolve, at the same time, the three principal components of an ANN: the set of synaptic weights, the connections or architecture, and the transfer functions for each neuron. Eight different fitness functions were proposed to evaluate the fitness of each solution and find the best design. These functions are based on the mean square error (MSE) and the classification error (CER) and implement a strategy to avoid overtraining and to reduce the number of connections in the ANN. In addition, the ANN designed with the proposed methodology is compared with those designed manually using the well-known Back-Propagation and Levenberg-Marquardt Learning Algorithms. Finally, the accuracy of the method is tested with different nonlinear pattern classification problems. PMID:26221132
A Hybrid Memetic Framework for Coverage Optimization in Wireless Sensor Networks.
Chen, Chia-Pang; Mukhopadhyay, Subhas Chandra; Chuang, Cheng-Long; Lin, Tzu-Shiang; Liao, Min-Sheng; Wang, Yung-Chung; Jiang, Joe-Air
2015-10-01
One of the critical concerns in wireless sensor networks (WSNs) is the continuous maintenance of sensing coverage. Many particular applications, such as battlefield intrusion detection and object tracking, require a full-coverage at any time, which is typically resolved by adding redundant sensor nodes. With abundant energy, previous studies suggested that the network lifetime can be maximized while maintaining full coverage through organizing sensor nodes into a maximum number of disjoint sets and alternately turning them on. Since the power of sensor nodes is unevenly consumed over time, and early failure of sensor nodes leads to coverage loss, WSNs require dynamic coverage maintenance. Thus, the task of permanently sustaining full coverage is particularly formulated as a hybrid of disjoint set covers and dynamic-coverage-maintenance problems, and both have been proven to be nondeterministic polynomial-complete. In this paper, a hybrid memetic framework for coverage optimization (Hy-MFCO) is presented to cope with the hybrid problem using two major components: 1) a memetic algorithm (MA)-based scheduling strategy and 2) a heuristic recursive algorithm (HRA). First, the MA-based scheduling strategy adopts a dynamic chromosome structure to create disjoint sets, and then the HRA is utilized to compensate the loss of coverage by awaking some of the hibernated nodes in local regions when a disjoint set fails to maintain full coverage. The results obtained from real-world experiments using a WSN test-bed and computer simulations indicate that the proposed Hy-MFCO is able to maximize sensing coverage while achieving energy efficiency at the same time. Moreover, the results also show that the Hy-MFCO significantly outperforms the existing methods with respect to coverage preservation and energy efficiency. PMID:25532143
Hybrid vehicle system studies and optimized hydrogen engine design
Smith, J.R.; Aceves, S.
1995-04-26
We have done system studies of series hydrogen hybrid automobiles that approach the PNGV design goal of 34 km/liter (80 mpg), for 384 km (240 mi) and 608 km (380 mi) ranges. Our results indicate that such a vehicle appears feasible using an optimized hydrogen engine. We have evaluated the impact of various on-board storage options on fuel economy. Experiments in an available engine at the Sandia CRF demonstrated NO{sub x} emissions of 10 to 20 ppM at an equivalence ratio of 0.4, rising to about 500 ppm at 0.5 equivalence ratio using neat hydrogen. Hybrid simulation studies indicate that exhaust NO{sub x} concentrations must be less than 180 ppM to meet the 0.2 g/mile ULEV or Federal Tier II emissions regulations. LLNL has designed and fabricated a first generation optimized hydrogen engine head for use on an existing Onan engine. This head features 15:1 compression ratio, dual ignition, water cooling, two valves and open quiescent combustion chamber to minimize heat transfer losses. Initial testing shows promise of achieving an indicated efficiency of nearly 50% and emissions of less than 100 ppM NO{sub x}. Hydrocarbons and CO are to be measured, but are expected to be very low since their only source is engine lubricating oil. A successful friction reduction program on the Onan engine should result in a brake thermal efficiency of about 42% compared to today`s gasoline engines of 32%. Based on system studies requirements, the next generation engine will be about 2 liter displacement and is projected to achieve 46% brake thermal efficiency with outputs of 15 kW for cruise and 40 kW for hill climb.
Optimization of Multiple Vehicle Routing Problems using Approximation Algorithms
Nallusamy, R; Dhanalaksmi, R; Parthiban, P
2010-01-01
This paper deals with generating of an optimized route for multiple Vehicle routing Problems (mVRP). We used a methodology of clustering the given cities depending upon the number of vehicles and each cluster is allotted to a vehicle. k- Means clustering algorithm has been used for easy clustering of the cities. In this way the mVRP has been converted into VRP which is simple in computation compared to mVRP. After clustering, an optimized route is generated for each vehicle in its allotted cluster. Once the clustering had been done and after the cities were allocated to the various vehicles, each cluster/tour was taken as an individual Vehicle Routing problem and the steps of Genetic Algorithm were applied to the cluster and iterated to obtain the most optimal value of the distance after convergence takes place. After the application of the various heuristic techniques, it was found that the Genetic algorithm gave a better result and a more optimal tour for mVRPs in short computational time than other Algorit...
Using Heuristic Algorithms to Optimize Observing Target Sequences
NASA Astrophysics Data System (ADS)
Sosnowska, D.; Ouadahi, A.; Buchschacher, N.; Weber, L.; Pepe, F.
2014-05-01
The preparation of observations is normally carried out at the telescope by the visiting observer. In order to help the observer, we propose several algorithms to automatically optimize the sequence of targets. The optimization consists of assuring that all the chosen targets are observable within the given time interval, and to find their best execution order in terms of the observation quality and the shortest telescope displacement time. Since an exhaustive search is too expensive in time, we researched heuristic algorithms, specifically: Min-Conflict, Non-Sorting Genetic Algorithms and Simulated Annealing. Multiple metaheuristics are used in parallel to swiftly give an approximation of the best solution, with all the constraints satisfied and the total execution time minimized. The optimization process has a duration on the order of tens of seconds, allowing for quick re-adaptation in case of changing atmospheric conditions. The graphical user interface allows the user to control the parameters of the optimization process. Therefore, the search can be adjusted in real time. The module was coded in a way to allow easily the addition of new constraints, and thus ensure its compatibility with different instruments. For now, the application runs as a plug-in to the observation preparation tool called New Short Term Scheduler, which is used on three spectrographs dedicated to the exoplanets search: HARPS at the La Silla observatory, HARPS North at the La Palma observatory and SOPHIE at the Observatoire de Haute-Provence.
Control of Hidden Mode Hybrid Systems: Algorithm termination
Verma, Rajeev
We consider the problem of safety control in Hidden Mode Hybrid Systems (HMHS) that arises in the development of a semi-autonomous cooperative active safety system for collision avoidance at an intersection. We utilize the ...
A distributed PSO-SVM hybrid system with feature selection and parameter optimization
Cheng-lung Huang; Jian-fan Dun
2008-01-01
This study proposed a novel PSO–SVM model that hybridized the particle swarm optimization (PSO) and support vector machines (SVM) to improve the classification accuracy with a small and appropriate feature subset. This optimization mechanism combined the discrete PSO with the continuous-valued PSO to simultaneously optimize the input feature subset selection and the SVM kernel parameter setting. The hybrid PSO–SVM data
Preliminary flight evaluation of an engine performance optimization algorithm
NASA Technical Reports Server (NTRS)
Lambert, H. H.; Gilyard, G. B.; Chisholm, J. D.; Kerr, L. J.
1991-01-01
A performance seeking control (PSC) algorithm has undergone initial flight test evaluation in subsonic operation of a PW 1128 engined F-15. This algorithm is designed to optimize the quasi-steady performance of an engine for three primary modes: (1) minimum fuel consumption; (2) minimum fan turbine inlet temperature (FTIT); and (3) maximum thrust. The flight test results have verified a thrust specific fuel consumption reduction of 1 pct., up to 100 R decreases in FTIT, and increases of as much as 12 pct. in maximum thrust. PSC technology promises to be of value in next generation tactical and transport aircraft.