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In this paper transmission power system structure optimization is performed via a minimal spanning tree based encoded fuzzy logic self-controlled hybrid genetic algorithm (GA). During the redundancy optimization of the power system network a binary encoded GA is used for a modified transmission network expansion problem, finding the optimal power line type with respect to the net present value (NPV)

Evolutionary computation has become an important prob- lem solving methodology among the set of search and optimization tech- niques. Recently, more and more different evolutionary techniques have been developed, especially hybrid evolutionary algorithms. This paper proposes an island based hybrid evolutionary algorithm (IHEA) for op- timization, which is based on Particle swarm optimization (PSO), Fast Evolutionary Programming (FEP), and Estimation

Differential Evolution (DE) is a novel evolutionary approach capable of handling non-differentiable, non-linear and multi-modal objective functions. DE has been consistently ranked as one of the best search algorithm for solving global optimization problems in several case studies. This paper presents a simple and modified hybridized Differential Evolution algorithm for solving global optimization problems. The proposed algorithm is a hybrid

Presented in this paper is a hybridalgorithm for the design of discrete structures like trusses. The proposed algorithm called\\u000a Discrete Structures Optimization (DSO) is based on the Evolutionary Structural Optimization (ESO) [1,2]. In DSO, material\\u000a is removed from the structural elements based on the strain energy. DSO is a two stage process. First stage is the topology\\u000a optimization where

A new optimization technique based on the hybridalgorithm combining ant colony optimizationalgorithm with microgenetic algorithm is presented for the design of multilayered radar absorbing materials. During the optimization procedure the optimization constrained conditions are different in order to meet the practical requirements in the different frequency bands between 2 GHz and 18 GHz, and the multilayered radar absorbing

The development of evolutionary algorithms for optimization has always been a stimulating and growing research area with an increasing demand in using them to solve complex industrial optimization problems. A novel immunity-based hybrid evolutionary algorithm known as Hybrid Artificial Immune Systems (HAIS) for solving both unconstrained and constrained multi-objective optimization problems is developed in this research. The algorithm adopts the

Eugene Y. C. Wong; Henry S. C. Yeung; Henry Y. K. Lau

In this article, a hybrid global–local optimizationalgorithm is proposed to solve continuous engineering optimization problems. In the proposed algorithm, the harmony search (HS) algorithm is used as a global-search method and hybridized with a spreadsheet ‘Solver’ to improve the results of the HS algorithm. With this purpose, the hybrid HS–Solver algorithm has been proposed. In order to test the

M. Tamer Ayvaz; Ali Haydar Kayhan; Huseyin Ceylan; Gurhan Gurarslan

There is a lot of research in genetic algorithm about structural optimization. But as far as the large multi-goal program is concerned, it limits the application of genetic algorithm for the reason of its specialty and large calculation. In order to explore a new resolution, the author proposed a combining algorithm for structural optimization, which is based on genetic algorithm

Weijin Jiang I; Dingti Luol; Yusheng Xu; Xingming Sun

Dynamic programming, branch-and-bound, and constraint programming are the standard solution principles for finding optimal solutions to machine scheduling problems. We propose a new hybridoptimization framework that integrates all three methodologies. The hybrid framework leads to powerful solution procedures. We demonstrate our approach through the optimal solution of the single-machine total weighted completion time scheduling problem subject to release dates,

This paper proposes a hybrid rough K-means algorithm for image classification. The rough set theory is used to establish the\\u000a lower and upper bound for data clustering in the K-means algorithm. Then, the particle swarm optimization (PSO) is employed\\u000a to optimize the solutions of the rough K-means algorithm. The combined algorithm is called the Rough K-means PSO algorithm.\\u000a Experimental results

Gasoline blending is a key process in the petroleum refinery industry posed as a nonlinear optimization problem with heavily nonlinear constraints. This paper presents a DNA based hybrid genetic algorithm (DNA-HGA) to optimize such nonlinear optimization problems. In the proposed algorithm, potential solutions are represented with nucleotide bases. Based on the complementary properties of nucleotide bases, operators inspired by DNA

This paper presents a novel evolutionary algorithm (EA) for constrained optimization problems, i.e., the hybrid constrained optimization EA (HCOEA). This algorithm effectively combines multiobjective optimization with global and local search models. In performing the global search, a niching genetic algorithm based on tournament selection is proposed. Also, HCOEA has adopted a parallel local search operator that implements a clustering partition of the population and multiparent crossover to generate the offspring population. Then, nondominated individuals in the offspring population are used to replace the dominated individuals in the parent population. Meanwhile, the best infeasible individual replacement scheme is devised for the purpose of rapidly guiding the population toward the feasible region of the search space. During the evolutionary process, the global search model effectively promotes high population diversity, and the local search model remarkably accelerates the convergence speed. HCOEA is tested on 13 well-known benchmark functions, and the experimental results suggest that it is more robust and efficient than other state-of-the-art algorithms from the literature in terms of the selected performance metrics, such as the best, median, mean, and worst objective function values and the standard deviations. PMID:17550112

This study presents a hybrid harmony search algorithm (HHSA) to solve engineering optimization problems with continuous design variables. Although the harmony search algorithm (HSA) has proven its ability of finding near global regions within a reasonable time, it is comparatively inefficient in performing local search. In this study sequential quadratic programming (SQP) is employed to speed up local search and

M. Fesanghary; M. Mahdavi; M. Minaryjolandan; Y. Alizadeh

In this paper we propose an efficient algorithm for computing the solution to the finite time optimal control problem for discrete time linear hybrid systems with a quadratic performance criterion. The algorithm is based on a dynamic programming recursion and a multiparametric quadratic programming solver.

Francesco Borrelli; Mato Baotic; Alberto Bemporad; Manfred Morari

An optimum topology design algorithm based on the hybrid Big Bang–Big Crunch optimization (HBB–BC) method is developed for the Schwedler and ribbed domes. A simple procedure is defined to determine the Schwedler and ribbed dome configuration. This procedure includes calculating the joint coordinates and element constructions. The nonlinear response of the dome is considered during the optimization process. The effect

In this paper, a new formulation for optimizing the design of a photovoltaic (PV)-wind hybrid energy home system, incorporating a storage battery, is developed. This formulation is carried out with the purpose of arriving at a selection of the system economical components that can reliably satisfy the load demand. Genetic algorithm (GA) optimization technique is utilized to satisfy two purposes.

The purpose of this research was to develop a software tool for generating optimal target servicing strategies for imaging fixed ground targets with a spaceborne SAR. Given a list of targets and their corresponding geographic locations and relative priorities, this tool generates a target servicing strategy that maximizes the overall collection utility based on the number of targets successfully imaged weighted by their relative priorities. This tool is specifically designed to maximize sensor utility in the case of a target-rich environment. For small numbers of targets, a target servicing strategy is unnecessary, and the targets may be imaged in any order without paying any particular attention to geographic proximity or target priority. However, for large, geographically diverse target decks, the order in which targets are serviced is of great importance. The target servicing problem is shown to be of the class NP-hard, and thus cannot be solved to optimality in polynomial time. Therefore, global search techniques such as genetic algorithms are called for. A unique hybridalgorithm that combines genetic algorithms with simulated annealing has been developed to generate optimized target servicing strategies. The performance of this hybridalgorithm was compared against that of three different greedy algorithms in a series of 20 test cases. Preliminary results indicate consistent performance improvements over greedy algorithms for target-rich environments. Over the course of 20 trials, the hybridoptimizingalgorithm produced weighted collection scores that were on average 10% higher than the best greedy algorithm.

This paper proposes a Hybrid Genetic Algorithm (Hybrid-GA) which is suitable for a large scale multidisciplinary optimization problem such as Two-Stage-To-Orbit (TSTO) spaceplane optimal design problem. The Hybrid-GA is implemented by combining Sequential Quadratic Programming (SQP) method with GA. When constructing the Hybrid-GA, there are three problems that should be solved; 1) decision of optimized variables and discrete method, 2) how to use results of local search, and 3) selection for survival method. These problems are discussed and solved in order to effectively combine SQP method with GA. In order to demonstrate the effectiveness of the proposed Hybrid-GA, the TSTO spaceplane optimal design problem, which consists of weight, aerodynamics, propulsion, and flight trajectory analyses, is investigated using the proposed Hybrid-GA, and the optimal results of Hybrid-GA are compared with that of Simple-GA, and SQP methods. Finally, strategy to achieve the TSTO spaceplane is proposed by comparing the optimal results of changing payload weight and maximum wing load factor of booster.

A hybrid methodology is presented for the solution of the problem of the optimal allocation of reactive power sources. The technique is based upon a modified genetic algorithm, which is applied at an upper level stage, and a successive linear program at a lower level stage. The objective is the minimization of the total cost associated to the installation of

Alberto J. Urdaneta; Juan F. Gomez; Elmer Sorrentino; Luis Flores; Ricardo Diaz

Electric power demand has an increasing tendency year by year. The fluctuation of the electric power causes further increase in the cost of the electric power facility and electricity charges. The development of the electric power-leveling systems (EPLS) using energy storage technology is desired to improve the electric power quality. The EPLS with a SMES is proposed as one of the countermeasures for the electric power quality improvement. However, the SMES is very expensive and it is difficult to decide the gains of the controller. It is essential in the practical use that the reduction of SMES capacity is realized. This paper proposes a new optimization method of the EPLS. The proposed algorithm is hybrid architecture with a combination of SimE (Simulated Evolution) and GA (Genetic Algorithms). The optimization of the EPLS can be achieved by the proposed hybridalgorithm compared to the SimE and the GA.

Many real-life optimization problems often face an increased rank of nonsmoothness (many local minima) which could prevent a search algorithm from moving toward the global solution. Evolution-based algorithms try to deal with this issue. The algorithm proposed in this paper is called GAAPI and is a hybridization between two optimization techniques: a special class of ant colony optimization for continuous domains entitled API and a genetic algorithm (GA). The algorithm adopts the downhill behavior of API (a key characteristic of optimizationalgorithms) and the good spreading in the solution space of the GA. A probabilistic approach and an empirical comparison study are presented to prove the convergence of the proposed method in solving different classes of complex global continuous optimization problems. Numerical results are reported and compared to the existing results in the literature to validate the feasibility and the effectiveness of the proposed method. The proposed algorithm is shown to be effective and efficient for most of the test functions. PMID:21896393

The reactive power allocation aspect has received considerable attention in present day power system operation and control. At heavy\\/light load periods, voltage control is provided by the controllable reactive sources, which are scattered throughout the transmission network, function in co-ordination. Reactive power dispatch problem can be formulated as a nonlinear constrained optimization problem. This paper presents a Particle Swarm with

The structure and size of steering trapezoid linkage can greatly affect the steering performance of automobile, therefore it is very important to adopt optimization methods to design the steering linkage. Being satisfied with the conditions of transmission capacity and boundary constraints, considering the ideal relationship of steering angles between external and internal wheels in steering linkage of automobile to ensure

The aim of the present paper is to propose a hybrid, self adjusting, search algorithm for space trajectory optimization. By taking advantage of both direct and indirect methods, the present algorithm allows the finding of the optimal solution through the introduction of some new control parameters, whose number is smaller than that of the Lagrange multipliers, and whose range is bounded. Eventually, the optimal solution is determined by means of an iterative self-adjustment of the search domain occurring at "runtime", without any correction by an external user. This new set of parameters can be found through a reduction process of the degrees of freedom, obtained through the transversality conditions before entering the search loop. Furthermore, such a process shows that Lagrange multipliers are subject to a deep symmetry mirroring the features of the state vector. The algorithm reliability and efficiency is assessed through some test cases, and by reproducing some optimal transfer trajectories: a full three-dimensional, minimum time Mars mission, an optimal transfer to Jupiter, and finally an injection into a circular Moon orbit.

In this paper thermo-chemical simulation of the pultrusion process of a composite rod is first used as a validation case to ensure that the utilized numerical scheme is stable and converges to results given in literature. Following this validation case, a cylindrical die block with heaters is added to the pultrusion domain of a composite part and thermal contact resistance (TCR) regions at the die-part interface are defined. Two optimization case studies are performed on this new configuration. In the first one, optimal die radius and TCR values are found by using a hybrid genetic algorithm based on a sequential combination of a genetic algorithm (GA) and a local search technique to fit the centerline temperature of the composite with the one calculated in the validation case. In the second optimization study, the productivity of the process is improved by using a mixed integer genetic algorithm (MIGA) such that the total number of heaters is minimized while satisfying the constraints for the maximum composite temperature, the mean of the cure degree at the die exit and the pulling speed.

The Hybrid Projected Gradient-Evolutionary Search Algorithm (HPGES) algorithm uses a specially designed evolutionary-based global search strategy to efficiently create candidate solutions in the solution space. A local projection-based gradient search alg...

This paper presents a novel algorithm which blends optimize particle swarm optimization (PSO) algorithm and Levenberg-Marquardt (LM) algorithm according to the probability. This novel algorithm can be used for Pseudo-Voigt type of Brillouin scattering spectrum to improve the degree of fitting and precision of shift extraction. This algorithm uses PSO algorithm as the main frame. First, PSO algorithm is used in global search, after a certain number of optimization every time there generates a random probability rand (0, 1). If rand (0, 1) is less than or equal to the predetermined probability P, the optimal solution obtained by PSO algorithm will be used as the initial value of LM algorithm. Then LM algorithm is used in local depth search and the solution of LM algorithm is used to replace the previous PSO algorithm for optimal solutions. Again the PSO algorithm is used for global search. If rand (0, 1) was greater than P, PSO algorithm is still used in search, waiting the next optimization to generate random probability rand (0, 1) to judge. Two kinds of algorithms are alternatively used to obtain ideal global optimal solution. Simulation analysis and experimental results show that the new algorithm overcomes the shortcomings of single algorithm and improves the degree of fitting and precision of frequency shift extraction in Brillouin scattering spectrum, and fully prove that the new method is practical and feasible. PMID:22715752

In a hybrid renewable energy power generation system, optimization and control is a challenging task because the behaviors of the system are becoming unpredictable and more complex. After the system is built, optimization and control of its operation is important for utilizing the renewable energy efficiently and economically. In the paper, an improved genetic algorithm is developed for achieving the

In this article, a new hybrid evolutionary algorithm (HEA) is proposed to determine the optimal placement of multi-type FACTS devices for simultaneously maximizing the total transfer capability (TTC) and minimizing system real power losses of power transfers between different control areas. Multi-objective optimal power flow (OPF) with FACTS devices including TTC, system real power loss and penalty functions is used

An evolutionary recurrent network which automates the design of recurrent neural/fuzzy networks using a new evolutionary learning algorithm is proposed in this paper. This new evolutionary learning algorithm is based on a hybrid of genetic algorithm (GA) and particle swarm optimization (PSO), and is thus called HGAPSO. In HGAPSO, individuals in a new generation are created, not only by crossover and mutation operation as in GA, but also by PSO. The concept of elite strategy is adopted in HGAPSO, where the upper-half of the best-performing individuals in a population are regarded as elites. However, instead of being reproduced directly to the next generation, these elites are first enhanced. The group constituted by the elites is regarded as a swarm, and each elite corresponds to a particle within it. In this regard, the elites are enhanced by PSO, an operation which mimics the maturing phenomenon in nature. These enhanced elites constitute half of the population in the new generation, whereas the other half is generated by performing crossover and mutation operation on these enhanced elites. HGAPSO is applied to recurrent neural/fuzzy network design as follows. For recurrent neural network, a fully connected recurrent neural network is designed and applied to a temporal sequence production problem. For recurrent fuzzy network design, a Takagi-Sugeno-Kang-type recurrent fuzzy network is designed and applied to dynamic plant control. The performance of HGAPSO is compared to both GA and PSO in these recurrent networks design problems, demonstrating its superiority. PMID:15376846

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.

Mobile ad hoc network (MANET) is a group of mobile nodes which communicates with each other without any supporting infrastructure. Routing in MANET is extremely challenging because of MANETs dynamic features, its limited bandwidth and power energy. Nature-inspired algorithms (swarm intelligence) such as ant colony optimization (ACO) algorithms have shown to be a good technique for developing routing algorithms for

Jianping Wang; Eseosa Osagie; Parimala Thulasiraman; Ruppa K. Thulasiram

Multisource hybrid power generation systems are a type of representative application of the renewables' technology. In this investigation, wind turbine generators, photovoltaic panels, and storage batteries are used to build hybrid generation systems that are optimal in terms of multiple criteria including cost, reliability, and emissions. Multicriteria design facilitates the decision maker to make more rational evaluations. In this study,

This paper proposes PSACO (particle swarm ant colony optimization) algorithm for highly non-convex optimization problems. Both particle swarm optimization (PSO) and ant colony optimization (ACO) are co-operative, population-based global search swarm intelligence metaheuristics. PSO is inspired by social behavior of bird flocking or fish schooling, while ACO imitates foraging behavior of real life ants. In this study, we explore a

P. S. Shelokar; Patrick Siarry; Valadi K. Jayaraman; Bhaskar D. Kulkarni

In this paper, we present a recently developed pattern search method called Genetic Pattern Search algorithm (GPSA) for the global optimization of cost function subject to simple bounds. GPSA is a combined global optimization method using genetic algorithm (GA) and Digital Pattern Search (DPS) method, which has the digital structure represented by binary strings and guarantees convergence to stationary points from arbitrary starting points. The performance of GPSA is validated through extensive numerical experiments on a number of well known functions and on robot walking application. The optimization results confirm that GPSA is a robust and efficient global optimization method.

Kim, Nam-Geun; Park, Youngsu; Kim, Jong-Wook; Kim, Eunsu; Kim, Sang Woo

We analyze the utility and scalability of extended compact genetic algorithm (eCGA)—a genetic algorithm (GA) that automatically and adaptively mines the regularities of the fitness landscape using machine learning methods and information theoretic measures—for ground state optimization of clusters. In order to reduce the computational time requirements while retaining the high reliability of predicting near-optimal structures, we employ two efficiency-enhancement

We propose a 2-stage ILP-based design algorithm for hierarchical optical path networks that utilize hybrid-HOXCs. The hybrid-HOXC consists of an optical waveband cross-connect and an electrical cross-connect which grooms only wavelength paths. Its effectiveness is evaluated through numerical experiments. Impact of electrical/optical port cost ratio on the total network cost is also investigated.

In order to improve the performance of immune algorithm, chaos optimization is integrated into immune clone selection algorithm. Portion antibodies after decoding are mapped into Lozi's chaos field, and then collect every optical value by each of chaos iteration is added into the new antibody group to improve the antibody-antigen fitness value. This paper analyzes the importance of the selection

Daohua Liu; Xin Liu; Li Zhang; Congcong Wei; Danning Wang

A genetic algorithm aiming the optimal design of composite structures under non-linear behaviour is presented. The approach\\u000a addresses the optimal material\\/stacking sequence in laminate construction and material distribution topology in composite\\u000a structures as a multimodal optimization problem. The proposed evolutionary process is based on a sequential hierarchical relation\\u000a between subpopulations evolving in separated isolation stages followed by migration. Improvements based

Research on optimal sensor placement (OSP) has become very important due to the need to obtain effective testing results with limited testing resources in health monitoring. In this study, a new methodology is proposed to select the best sensor locations for large structures. First, a novel fitness function derived from the nearest neighbour index is proposed to overcome the drawbacks of the effective independence method for OSP for large structures. This method maximizes the contribution of each sensor to modal observability and simultaneously avoids the redundancy of information between the selected degrees of freedom. A hybridalgorithm combining the improved discrete particle swarm optimization (DPSO) with the clonal selection algorithm is then implemented to optimize the proposed fitness function effectively. Finally, the proposed method is applied to an arch dam for performance verification. The results show that the proposed hybrid swarm intelligence algorithm outperforms a genetic algorithm with decimal two-dimension array encoding and DPSO in the capability of global optimization. The new fitness function is advantageous in terms of sensor distribution and ensuring a well-conditioned information matrix and orthogonality of modes, indicating that this method may be used to provide guidance for OSP in various large structures.

The particle swarm optimization (PSO) 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 other hand, genetic algorithm is very sensitive to the initial population. In fact, the random nature of the GA operators makes the algorithm sensitive to initial population. This dependence

Multiple objectives must be considered in anatomy-based dose optimization for high-dose-rate brachytherapy and a large number of parameters must be optimized to satisfy often competing objectives. For objectives expressed solely in terms of dose variances, deterministic gradient-based algorithms can be applied and a weighted sum approach is able to produce a representative set of non-dominated solutions. As the number of objectives increases, or non-convex objectives are used, local minima can be present and deterministic or stochastic algorithms such as simulated annealing either cannot be used or are not efficient. In this case we employ a modified hybrid version of the multi-objective optimizationalgorithm NSGA-II. This, in combination with the deterministic optimizationalgorithm, produces a representative sample of the Pareto set. This algorithm can be used with any kind of objectives, including non-convex, and does not require artificial importance factors. A representation of the trade-off surface can be obtained with more than 1000 non-dominated solutions in 2-5 min. An analysis of the solutions provides information on the possibilities available using these objectives. Simple decision making tools allow the selection of a solution that provides a best fit for the clinical goals. We show an example with a prostate implant and compare results obtained by variance and dose-volume histogram (DVH) based objectives. PMID:12608615

The particle swarm optimizationalgorithm 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

Jing-ru Zhang; Jun Zhang; Tat-ming Lok; Michael R. Lyu

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-optimizationalgorithm, 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-optimizationalgorithm can identify the global optimum. The hybrid-optimizationalgorithm 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

In this research, we propose a new method, a hybrid Taguchi-genetic algorithm (HTGA), for optics and zoom optics with a diffractive optical element (DOE) in order to eliminate chromatic aberration more efficiently than traditional damped least squares (DLS) does. By researching and validating a set of optical designs using a DOE, we have derived an optimal theory for the specific elimination of chromatic aberrations. Following the advanced technology applied to microlenses and the etching process, precisely made microDOE elements may now be manufactured on a large scale. We adopted the genetic algorithm (GA) and incorporated the steady Taguchi method into it. Combining these two methods produced a new hybrid Taguchi-genetic algorithm (HTGA). Suitable glass combinations and best positions for the DOE, which could not be properly achieved with DLS, were carefully selected to minimize the chromatic aberration in the optical system. We used an optical system with a fixed-focus and complicated 4 × zoom optics with a DOE to compare the optimization results of traditional DLS for optics with a DOE. Experiments show that, whether the chromatic aberration was axial or longitudinal, the values of the measurements involving the chromatic aberration of the optical lens could be significantly reduced.

Fang, Yi Chin; Liu, Tung-Kuan; Tsai, Cheng-Mu; Chou, Jyh-Horng; Lin, Han-Ching; Lin, Wei Teng

This paper proposes an efficient design algorithm for power/ground (P/G) network synthesis with dynamic signal consideration, which is mainly caused by Ldi/dt noise and Cdv/dt decoupling capacitance (DECAP) current in the distribution network. To deal with the nonlinear global optimization under synthesis constraints directly, the genetic algorithm (GA) is introduced. The proposed GA-based synthesis method can avoid the linear transformation loss and the restraint condition complexity in current SLP, SQP, ICG, and random-walk methods. In the proposed Hybrid Grid Synthesis algorithm, the dynamic signal is simulated in the gene disturbance process, and Trapezoidal Modified Euler (TME) method is introduced to realize the precise dynamic time step process. We also use a hybrid-SLP method to reduce the genetic execute time and increase the network synthesis efficiency. Experimental results on given power distribution network show the reduction on layout area and execution time compared with current P/G network synthesis methods.

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 Optimizationalgorithm 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.

Mahmood, Zakaria N.; Mahmuddin, Massudi; Mahmood, Mohammed Nooraldeen

In a former study (F.L. de Sousa, F.M. Ramos, F.J.C.P. Soeiro, and A.J. Silva Neto, Application of the generalized extremal optimizationalgorithm to an inverse radiative transfer problem, Inverse Probl. Sci. Eng. 15 (2007), pp. 699–714), a new evolutionary optimization metaheuristic–the generalized extremal optimization (GEO) algorithm (F.L. de Sousa, F.M. Ramos, P.Paglione, and R.M. Girardi, A new stochastic algorithm for

Roberto L. Galski; Fabiano L. de Sousa; Fernando M. Ramos; Antônio J. Silva Neto

A new hybrid multi-objective, multivariable optimizer utilizing Strength Pareto Evolutionary Algorithm (SPEA), Non-dominated Sorting Differential Evolution (NSDE), and Multi-Objective Particle Swarm (MOPSO) has been created and tested. The optimizer features automatic switching among these algorithms to expedite the convergence of the optimal Pareto front in the objective function(s) space. The ultimate goal of using such a hybridoptimizer is to

George S. Dulikravich; Ramon J. Moral; Debasis Sahoo

In 1971 a hybrid computer algorithm for implementation of an optimal nonlinear one-step predictor by applying Bayes' Rule to sequentially update the conditional probability density function from the latest data was presented in a paper. Such a filter has ...

L. Basanez P. Brunet R. S. Bucy R. Huber D. S. Miller

\\u000a Profile Hidden Markov Models (Profile HMM) are well suited to modelling multiple alignment and are widely used in molecular\\u000a biology. Usually, heuristic algorithms such as Baum-Welch are used to estimate the model parameters. However, Baum-Welch has\\u000a a tendency to stagnate on local optima. A more involved approach is to use some form of stochastic search algorithm that ‘bumps’\\u000a Baum-Welch off

Hybridization of seawater reverse osmosis (S WRO), desalting technology and the multi-stage flash (MSF) has been considered to improve the performance of the latter and reduce the cost of desalted water. Coupling of the two processes could be made on different levels of integration and the resulting water cost will depend on the selected configuration: not only the plant configuration

A. M. Helal; A. M. El-Nashar; E. Al-Katheeri; S. Al-Malek

Three-dimensional braided composite has the better designable characteristic. Whereas wide application of hollow-rectangular-section three-dimensional braided composite in engineering, optimization design of the three-dimensional braided composite made by 4-step method were introduced. Firstly, the stiffness and damping characteristic analysis of the composite is presented. Then, the mathematical models for structure design of the three-dimensional braided composite were established. The objective functions are based on the specific damping capacity and stiffness of the composite. The design variables are the braiding parameters of the composites and sectional geometrical size of the composite. The optimization problem is solved by using ant colony optimization (ACO), contenting the determinate restriction. The results of numeral examples show that the better damping and stiffness characteristic could be obtained. The method proposed here is useful for the structure design of the kind of member and its engineering application.

Particle swarm optimization (PSO) is a new stochastic optimization technique based on swarm intelligence. In this paper, we introduce the basic principles of PSO firstly. Then, the research progress on PSO algorithm is summarized in several fields, such as parameter selection and design, population topology, hybrid PSO algorithm etc. Finally, some vital applications and aspects that may be conducted in

Most of the features of the future hybrid electric vehicles are enabled by a new energy flow management unit designed to split the instantaneous power demand between the internal combustion engine and the electric motor, ensuring both an efficient power supply and a reduced emission. Classic approaches that rely on static thresholds, optimized on a fixed drive cycle, cannot face

\\u000a Inspired by observing fireworks explosion, a novel swarm intelligence algorithm, called Fireworks Algorithm (FA), is proposed\\u000a for global optimization of complex functions. In the proposed FA, two types of explosion (search) processes are employed,\\u000a and the mechanisms for keeping diversity of sparks are also well designed. In order to demonstrate the validation of the FA,\\u000a a number of experiments were

During these years stochastic algorithms have deserved much attention from the computational biology research communities. In this paper we derive a hybrid version of the formerly known Metabolic Al- gorithm that is enriched with stochastic features, whose impact on the dynamics of the system is as prominent when the amount of metabo- lite becomes smaller. This hybrid procedure represents a

\\u000a A hybridoptimizationalgorithm based on Cat Swarm Optimization (CSO) and Artificial Bee Colony (ABC) is proposed in this\\u000a chapter. CSO is an optimizationalgorithm designed to solve numerical optimization problems, and ABC is an optimizationalgorithm\\u000a generated by simulating the behavior of bees finding foods. By hybridizing these two algorithms, the hybridalgorithm called\\u000a Hybrid PCSOABC is presented. Five

In this paper, a novel robust watermarking technique using particle swarm optimization and k-nearest neighbor algorithm is introduced to protect the intellectual property rights of color images in the spatial domain. In the embedding process, the color image is separated into non-overlapping blocks and each bit of the binary watermark is embedded into the individual blocks. Then, in order to extract the embedded watermark, features are obtained from watermark embedded blocks using the symmetric cross-shape kernel. These features are used to generate two centroids belonging to each binary (1 and 0) value of the watermark implementing particle swarm optimization. Subsequently, the embedded watermark is extracted by evaluating these centroids utilizing k-nearest neighbor algorithm. According to the test results, embedded watermark is extracted successfully even if the watermarked image is exposed to various image processing attacks.

A hybrid divide and conquer algorithm is one that switches from a divide and conquer to an iterative strategy at a specified problem size. Such algorithms can provide significant performance improvements relative to alternatives that use a single strategy. However, the identification of the optimal problem size at which to switch for a particular algorithm and platform can be challenging. We describe an automated approach to this problem that first conducts experiments to explore the performance space on a particular platform and then uses the resulting performance data to construct an optimalhybridalgorithm on that platform. We implement this technique in a tool, ''Ouroboros'', that automatically constructs a high-performance hybridalgorithm from a set of registered algorithms. We present results obtained with this tool for several classical divide and conquer algorithms, including matrix multiply and sorting, and report speedups of up to six times achieved over non-hybridalgorithms.

The focus of this research is on a hybrid method combining immune algorithm with a hill climbing local search algorithm for solving complex real-world optimization problems. The objective is to contribute to the development of more efficient optimization approaches with the help of immune algorithm and hill climbing algorithm. The hybridalgorithm combines the exploration speed of immune algorithm with

In recent years, the population algorithms are becoming increasingly robust and easy to use, based on Darwin's Theory of Evolution, perform a search for the best solution around a population that will progress according to several generations. This paper present variants of hybrid genetic algorithm - Genetic Algorithm and a bio-inspired hybridalgorithm - Particle Swarm Optimization, both combined with the local method - Powell Method. The developed methods were tested with twelve test functions from unconstrained optimization context.

Bento, David; Pinho, Diana; Pereira, Ana I.; Lima, Rui

Genetic algorithms (GAs) are stochastic adaptive algorithms whose search method is based on simulation of natural genetic inheritance and Darwinian striving for survival. They can be used to find approximate solutions to numerical optimization problems in cases where finding the exact optimum is prohibitively expensive, or where no algorithm is known. However, such applications can encounter problems that sometimes delay,

Hybrid actuators, which are a combination of two types of motor and mechanism, have good flexibility. In this paper, the kinematics analysis for a hybrid five-bar actuator is introduced. An optimization design of hybrid actuator is performed with reference to kinematics objective function. By the use of the properties of ergodicity, stochastic property, and "regularity" of chaos, a hybridoptimizationalgorithm is proposed based on chaos optimizationalgorithm (COA) and a gradient-based search. The efficiency of COA is much higher than some stochastic algorithms such as simulated anneal algorithm (SAA) and genetic algorithm (GA) when COA is used to a kind of continuous problems. The chaos optimizationalgorithm can improve the efficiency of searching in the whole field by gradually shrinking the area of optimization variable. The precision of optimum dimensions obtained by using the hybridoptimization method can be improved evidently. Finally, a numerical example is carried out, and the simulation results show that the optimization method is feasible and satisfactory in the design of hybrid actuator.

Multiple, often conflicting objectives arise naturally in most real-world optimization scenarios. As evolutionary algorithms possess several characteristics due to which they are well suited to this type of problem, evolution-based methods have been used for multiobjective optimization for more than a decade. Meanwhile evolutionary multiobjective optimization has become established as a separate sub- discipline combining the elds of evolutionary computation

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

DongSeop Lee; Luis Felipe Gonzalez; Jacques Périaux; Karkenahalli Srinivas

Mixed-integer dynamic optimization (MIDO) problems arise in chemical engineering whenever discrete and continuous decisions are to be made for a system described by a transient model. Areas of application include integrated design and control, synthesis of reactor networks, reduction of kinetic mechanisms and optimization of hybrid systems. This article presents new formulations and algorithms for solving MIDO problems. The algorithms

Vikrant Bansal; Vassilis Sakizlis; Roderick Ross; John D. Perkins; Efstratios N. Pistikopoulos

In this paper, a hybrid reflective hyperspectral target detection algorithm based on the Constrained Energy Minimization (CEM) and Kelly's detection algorithms is presented. Detection performance is evaluated using the dataset and scoring methodology of the Rochester Institute of Technology (RIT) Target Detection Blind Test project. Results show that the proposed hybridalgorithm enables the detection of subpixel targets at a false alarm rate lower than either CEM or Kelly's algorithms.

We present a simulation algorithm for dynamical fermions that combines the multiboson technique with the hybrid Monte Carlo algorithm. We find that the algorithm gives a substantial gain over the standard methods in practical simulations. We point out the ability of the algorithm to treat fermion zero modes in a clean and controllable manner.

This article presents a novel stochastic optimization approach to solve the constrained economic load dispatch problem using a hybridization of the harmony search algorithm and particle swarm optimization, named hybrid harmony search based on the swarm intelligence principle. Harmony search is a recently developed derivative-free, meta-heuristic optimizationalgorithm, which draws inspiration from the musical process of searching for a perfect

V. Ravikumar Pandi; B. K. Panigrahi; Ramesh C. Bansal; Swagatam Das; Ankita Mohapatra

The need for personal transportation must be harmonized by considering the impact of so huge number of vehicles on the environment. The adoption of hybrid electric vehicles can provide a sensible improvement from an environmental viewpoint, but at the same time makes more difficult the definition and implementation of the overall powertrain control mechanism. In fact, powertrain control problems are

A kind of serial-parallel hybrid polishing machine tool based on the elastic polishing theory is developed and applied to\\u000a finish mould surface with using bound abrasives. It mainly consists of parallel mechanism of three dimensional moving platform,\\u000a serial rotational mechanism of two degrees of freedom and the elastic polishing tool system. The active compliant control\\u000a and passive conformity of polishing

Guilian Wang; Yiqiang Wang; Ji Zhao; Guiliang Chen

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

Most scheduling problems are complex combinatorial problems and very difficult to solve [Manage. Sci. 35 (1989) 164; F.S. Hillier, G.J. Lieberman, Introduction to Operations Research, Holden-Day, San Francisco, CA, 1967]. That is why, lots of methods focus on the optimization according to a single criterion (makespan, workloads of machines, waiting times, etc.). The combining of several criteria induces additional complexity

The past few years have seen considerable progress in algorithmic development for the generation of gauge fields including the effects of dynamical fermions. The Rational Hybrid Monte Carlo (RHMC) algorithm, where Hybrid Monte Carlo is performed using a rational approximation in place the usual inverse quark matrix kernel is one of these developments. This algorithm has been found to be extremely beneficial in many areas of lattice QCD (chiral fermions, finite temperature, Wilson fermions etc.). We review the algorithm and some of these benefits, and we compare against other recent algorithm developements. We conclude with an update of the Berlin wall plot comparing costs of all popular fermion formulations.

This work studies a hybrid model in which an optimal search algorithm intended for discrete optimisation (A*) is combined with a heuristic algorithm for continuous optimisation (an evolution strategy). The resulting algorithm is successfully\\u000a evaluated on a set of functions exhibiting different features such as multimodality, noise or epistasis. The scalability of\\u000a the algorithm in the presence of epistasis is

A Chaotic OptimizationAlgorithm (COA) is applied to integrated AC\\/DC hybrid power system in which the multi-infeed DC power transmission system is included, and the research on coordination controls among the HVDC modulations has been investigated in this paper. Chaotic OptimizationAlgorithms, which have the features of easy implementation, short execution time and robust mechanisms of escaping from local minima,

Zheng Xi-yun; Li Xing-yuan; Wang Yu-hong; Xu Mei-mei; Mu Zi-long; Liu Jian; Wei Wei

A hybrid fast Hankel transform algorithm has been developed that uses several complementary features of two existing algorithms: Anderson's digital filtering or fast Hankel transform (FHT) algorithm and Chave's quadrature and continued fraction algorithm. A hybrid FHT subprogram ...

In order to improve the standard of management in hospitals and effectively control the cost of clinical treatments, this research primarily focuses on optimizing the scheduling of clinical pathways (CPs). A mathematical model for CP scheduling is constructed, and the hybrid genetic algorithm (HGA, combining a genetic algorithm with particle swarm optimization) is proposed for solving this problem so as to distribute medical resources and schedule the treatments of patients reasonably and effectively. The optimal search space can be further enlarged by introducing a new mutation mechanism, which allows a more satisfactory solution to be found. In particular, the relative patient waiting time and relative time efficiency are used as measure indexes, which are more scientific and effective than the usual indexes of absolute time and absolute time efficiency. After comparing absolute waiting time, relative waiting time, utilization of absolute waiting time, and utilization of relative waiting time waiting respectively, the conclusion can confidently be drawn that task scheduling obviously enhances patients' time efficiency, reduces time wastage and therefore promotes patient satisfaction with medical processes. Moreover, the patients can to a certain degree move away from their usual passive role in medical processes by using this scheduling system. In order to further validate the rationality and validity of the proposed method, the heuristic rules for CP scheduling are also tested using the same case. The results demonstrate that the proposed HGA achieves superior performance, in terms of precision, over those heuristic rules for CP scheduling. Therefore, we utilize HGA to optimize CP scheduling, thus providing a decision-making mechanism for medical staff and enhancing the efficiency of medical processes. This research has both theoretical and practical significance for electronic CP management, in particular. PMID:23576080

Describes an approach to solving optimalhybrid control problems using level set methods. Level set methods are powerful techniques for generating equipotential contours with applications in the realm of fluid mechanics, computer vision, material science, robotics, and geometry. The paper specifically deals with the problem of determining an optimal control path in a hybrid system by extending the “fast marching”

We present a new hybridoptimization method for the synthesis of fiber Bragg gratings (FBGs) with complex characteristics. The hybridoptimization method is a two-tier search that employs a global optimizationalgorithm [i.e., the tabu search (TS) algorithm] and a local optimization method (i.e., the quasi-Netwon method). First the TS global optimizationalgorithm is used to find a ``promising'' FBG

Rui Tao Zheng; Nam Quoc Ngo; Le Nguyen Binh; Swee Chuan Tjin

In this paper we describe some algorithms for noisy optimization in the context of problems from the gas transmission industry. The algorithms are implicit filtering, DIRECT, and a new hybrid of these methods, which uses DIRECT to find an intitial iterate for implicit filtering. We report on numerical results that illustrate the performance of the methods.

R. G. Carter; J. M. Gablonsky; A. Patrick; C. T. Kelley; O. J. Eslinger

The prediction of biological activity of a chemical compound from its structural features plays an important role in drug design. In this paper, we discuss the quantitative structure activity relationship (QSAR) prediction models developed on a dataset of 170 HIV protease enzyme inhibitors. Various chemical descriptors that encode hydrophobic, topological, geometrical and electronic properties are calculated to represent the structures of the molecules in the dataset. We use the hybrid-GA (genetic algorithm) optimization technique for descriptor space reduction. The linear multiple regression analysis (MLR), correlation-based feature selection (CFS), non-linear decision tree (DT), and artificial neural network (ANN) approaches are used as fitness functions. The selected descriptors represent the overall descriptor space and account well for the binding nature of the considered dataset. These selected features are also human interpretable and can be used to explain the interactions between a drug molecule and its receptor protein (HIV protease). The selected descriptors are then used for developing the QSAR prediction models by using the MLR, DT and ANN approaches. These models are discussed, analyzed and compared to validate and test their performance for this dataset. All the three approaches yield the QSAR models with good prediction performance. The models developed by DT and ANN are comparable and have better prediction than the MLR model. For ANN model, weight analysis is carried out to analyze the role of various descriptors in activity prediction. All the prediction models point towards the involvement of hydrophobic interactions. These models can be useful for predicting the biological activity of new untested HIV protease inhibitors and virtual screening for identifying new lead compounds.

In this paper, the so-called meta optimization problem, that is, the task of choosing a suitable optimization technique as well as favorable strategy parameters for an optimization problem at hand, is described. It is stated that under conditions most often found in practical optimizationshybrid techniques are best suited. The optimization system MASCOT is introduced that uses a hybrid technique

Heuristic and exact methods for solving the redundancy allocation problem are compared to an approach based on genetic algorithms. The various methods are applied to the bridge problem, which has been used as a benchmark in earlier work on optimization methods. Comparisons are presented in terms of the best configuration found by each method, and the computation effort which was necessary in order to find it.

Power Economic Dispatch (ED) is vital and essential daily optimization procedure in the system operation. Present day large power generating units with multi-valves steam turbines exhibit a large variation in the input-output characteristic functions, thus non-convexity appears in the characteristic curves. Various mathematical and optimization techniques have been developed, applied to solve economic dispatch (ED) problem. Most of these are calculus-based optimizationalgorithms that are based on successive linearization and use the first and second order differentiations of objective function and its constraint equations as the search direction. They usually require heat input, power output characteristics of generators to be of monotonically increasing nature or of piecewise linearity. These simplifying assumptions result in an inaccurate dispatch. Genetic algorithms have used to solve the economic dispatch problem independently and in conjunction with other AI tools and mathematical programming approaches. Genetic algorithms have inherent ability to reach the global minimum region of search space in a short time, but then take longer time to converge the solution. GA based hybrid approaches get around this problem and produce encouraging results. This paper presents brief survey on hybrid approaches for economic dispatch, an architecture of extensible computational framework as common environment for conventional, genetic algorithm and hybrid approaches based solution for power economic dispatch, the implementation of three algorithms in the developed framework. The framework tested on standard test systems for its performance evaluation. (authors)

Tahir Nadeem Malik; Aftab Ahmad [University of Engineering and Technology, Taxila (Pakistan); Shahab Khushnood [National Power Construction Corporation - NPCC, 9-Shadman II, Lahore -54000 (Pakistan)

A novel hybrid global optimization method applied for feedforward neural networks (NN) supervised learning is investigated. The network weights are determined by minimizing the traditional mean-square error function. The optimization technique, called GLPtauS is a combination of novel global optimization heuristic search based on low-discrepancy sequences of points, called LPtau Optimization (LPtauO), a Genetic Algorithm, and a Simplex local search.

In the development of hybrid electric vehicles (HEVs), more and more researches are concentrated on the renewable energy sources. Due to the limitation of the previous maximum power point tracking algorithm (MPPT), these renewable energy systems are controlled separately. In this paper, an optimal solar-thermoelectric hybrid energy system for HEVs is proposed with MPPT. This method can track the global

A new hybridoptimization method in reloading pattern search is presented in this paper, which mix genetic algorithm (GA) with tabu search (TS). The method combines global search of GA and local search of TS reasonably to enhance the search ability and computational efficiency. For verification and illustration of the advantage of this method, the proposed hybridoptimization method has been applied to the reactor reloading optimization calculation of Cartesian and hexagonal geometry core. The numerical results show that the hybrid method works faster and better than GA. (authors)

Optimal control using particle swarm optimization (PSO) is put forward in a parallel hybrid hydraulic excavator (PHHE). A power-train mathematical model of PHHE is illustrated along with the analysis of components' parameters. Then, the optimal control problem is addressed, and PSO algorithm is introduced to deal with this nonlinear optimal problem which contains lots of inequality/equality constraints. Then, the comparisons between the optimal control and rule-based one are made, and the results show that hybrids with the optimal control would increase fuel economy. Although PSO algorithm is off-line optimization, still it would bring performance benchmark for PHHE and also help have a deep insight into hybrid excavators. PMID:23818832

A combinatorial optimizationalgorithm, genetic-entropic algorithm, is proposed. This optimizationalgorithm is based on the genetic algorithms and the natural selection via entropic sampling. With the entropic sampling, this algorithm helps to escape local optima in the complex optimization problems. To test the performance of the algorithm, we adopt the NK model (N is the number of bits in the string and K is the degree of epistasis) and compare the performances of the proposed algorithm with those of the canonical genetic algorithm. It is found that the higher the K value, the better this algorithm can escape local optima and search near global optimum. The characteristics of this algorithm in terms of the power spectrum analysis together with the difference between two algorithms are discussed.

Genetic algorithms (GAs) are stochastic optimization heuristics in which searches in solution space are carried out by imitating the population genetics stated in Darwin's theory of evolution. We have focused this work on compact genetic algorithms (cGAs), which unlike standard GAs do not manage a population of solutions but only mimics its existence. We study several approaches that can be

José Ignacio Hidalgo; Manuel Prieto; Juan Lanchares; Ranieri Baraglia; Francisco Tirado; Oscar Garnica

Bioluminescence tomography (BLT) is a promising optical molecular imaging technique on the frontier of biomedical optics. In this paper, a generalized hybridalgorithm has been proposed based on the graph cuts algorithm and gradient-based algorithms. The graph cuts algorithm is adopted to estimate a reliable source support without prior knowledge, and different gradient-based algorithms are sequentially used to acquire an accurate and fine source distribution according to the reconstruction status. Furthermore, multilevel meshes for the internal sources are used to speed up the computation and improve the accuracy of reconstruction. Numerical simulations have been performed to validate this proposed algorithm and demonstrate its high performance in the multi-source situation even if the detection noises, optical property errors and phantom structure errors are involved in the forward imaging.

In this paper, we propose an optimizationalgorithm based on the intelligent behavior of stem cell swarms in reproduction and self-organization. Optimizationalgorithms, 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 optimizationalgorithm 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 optimizationalgorithms. The obtained results prove the superiority of the Modified Stem Cells Algorithm (MSCA).

Taherdangkoo, Mohammad; Paziresh, Mahsa; Yazdi, Mehran; Bagheri, Mohammad Hadi

In the game theoretic approach to the synthesis of reactive systems, specifications are often given in linear time logic (LTL).\\u000a Computing a winning strategy to an infinite game whose winning condition is the set of LTL properties is the main step in\\u000a obtaining an implementation. We present a practical hybridalgorithm—a combination of symbolic and explicit algorithm—for\\u000a the computation of

Discrete, gradient, and hybridoptimization methods are applied to the challenge of discovering molecules with optimized properties. The cost and performance of the approaches were studied using a tight-binding model to maximize the static first electronic hyperpolarizability of molecules. Our analysis shows that discrete branch and bound methods provide robust strategies for inverse chemical design involving diverse chemical structures. Based on the linear combination of atomic potentials, a hybrid discrete-gradient optimization strategy significantly improves the performance of the gradient methods. The hybrid method performs better than dead-end elimination and competes with branch and bound and genetic algorithms. The branch and bound methods for these model Hamiltonians are more cost effective than genetic algorithms for moderate-sized molecular optimization.

A Hybrid Big Bang–Big Crunch (HBB–BC) optimizationalgorithm 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.

This paper presents an evolutionary algorithm for searching for the optimal implementations of signal transforms and compares this approach against other search techniques. A single signal processing algorithm can be represented by a very large number of different but mathematically equivalent formulas. When these formulas are implemented in actual code, unfortunately their running times differ significantly. Signal processing algorithmoptimization

This paper develops an efficient and reliable evolutionary programming algorithm for solving the optimal power flow (OPF) problem. The class of curves used to describe generator performance does not limit the algorithm and the algorithm is also less sensitive to starting points. To improve the speed of convergence of the algorithm as well as its ability to handle larger systems,

RLV (reusable launch vehicle) ascent-trajectory optimization is a difficult problem due to the complexity of many physical constraints and optimization cost surfaces. In this paper, a heuristic technique based on a PSO (particle swarm optimization) algorithm is used. The highly constrained nonlinear trajectory planning problem is decomposed into control parameters search problems, The algorithm is able to generate a complete

This paper proposes a hybridalgorithm based on particle swarm optimization (PSO) for a multiobjective permutation flow shop scheduling problem, which is a typical NP-hard combinatorial optimization problem with strong engineering backgrounds. Not only does the proposed multiobjective algorithm (named MOPSO) apply the parallel evolution mechanism of PSO characterized by individual improvement, population cooperation, and competition to effectively perform exploration

The wireless systems that establish multiple input multiple output (MIMO) channels through multiple antennas at both ends of the communication link, have been proved to have tremendous potential to linearly lift the capacity of conventional scalar channel. In this paper, we present two efficient decision feedback equalization algorithms that achieve optimal and suboptimal detection order in MIMO spatial multiplexing systems. The new algorithms combine the recursive matrix inversion and ordered QR decomposition approaches, which are developed for nulling cancellation interaface Bell Labs layered space time (BLAST) and back substitution interface BLAST. As a result, new algorithms achieve total reduced complexities in frame based transmission with various payload lengths compared with the earlier methods. In addition, they enable shorter detection delay by carrying out a fast hybrid preprocessing. Moreover, the operation precision insensitivity of order optimization greatly relaxes the word length of matrix inversion, which is the most computational intensive part within the MIMO detection task.

The design of an optimal control strategy for a hybrid sys- tem is a matter of growing interest in computational en- gineering. The solution of optimization problems in most engineering disciplines often requires efficient parallelop- timization algorithms to solve these kinds of problems in reasonable time. Instead of introducing parallelism to selected components of an existing sequential algorithm, the algorithm

Thomas Barthl; Bernd Freisleben; Manfred Grauer; Frank Thilol

The inclusion of computer simulations in the study and design of complex engineering systems has created a need for efficient approaches to simulation-based optimization. For example, in water resources management problems, optimization problems regularly consist of objective functions and constraints that rely on output from a PDE-based simulator. Various assumptions can be made to simplify either the objective function or the physical system so that gradient-based methods apply, however the incorporation of realistic objection functions can be accomplished given the availability of derivative-free optimization methods. A wide variety of derivative-free methods exist and each method has both advantages and disadvantages. Therefore, to address such problems, we propose a hybrid approach, which allows the combining of beneficial elements of multiple methods in order to more efficiently search the design space. Specifically, in this paper, we illustrate the capabilities of two novel algorithms; one which hybridizes pattern search optimization with Gaussian Process emulation and the other which hybridizes pattern search and a genetic algorithm. We describe the hybrid methods and give some numerical results for a hydrological application which illustrate that the hybrids find an optimal solution under conditions for which traditional optimal search methods fail.

Fowler, Katie (Clarkson University, NY); Gray, Genetha Anne; Griffin, Joshua D. (SAS Institute, NC)

The uncertainty in many engineering problems can be handled through probabilistic, fuzzy, or interval methods. This paper aims to use a hybrid genetic algorithm for tackling such problems. The proposed hybridalgorithm integrates a simple local search strategy as the worst-case-scenario technique of anti-optimization with a constrained multi-objective evolutionary algorithm. The work demonstrates the use of a technique alternating between

Existing intrusion detection techniques emphasize on building intrusion detection model based on all features provided. But all features are not relevant and some of them are redundant and useless. In this paper, we propose and investigate a fast hybrid feature selection method - a fusion of Correlation-based Feature Selection, Support Vector Machine and Genetic Algorithm - to determine an optimal

This paper introduces a new hybrid crossover method for a real-coded genetic algorithm and its application to control system design of a power plant. Determining gains for controllers by using a genetic algorithm method usually involves multiple training stages. This method is not necessarily optimal. This paper applies a hybrid crossover method in a real-coded genetic algorithm to simultaneously find

Weighting exponent m is an important parameter in fuzzy c-means (FCM) clustering algorithm, which directly affects the performance of the algorithm and the validity of fuzzy cluster analysis. However, so far the optimal choice of m is still an open problem. A method of selecting the optimal m is proposed in this paper, which is based on the fuzzy decision

This paper deals with a new deterministic algorithm for finding intersecting pairs from a given set of N segments in the plane. The algorithm is asymptotically optimal and has time and space complexity O(AJ log N+ K) and 0( IV ) respectively, where K is the number of intersecting pairs. The algorithm may be used for finding intersections not only

The dynamic programming algorithm for query optimization has exponential complexity. An alternative polynomial time algorithm, the IK-KBZ algorithm, is severely limited in the queries it can optimize. Other algorithms have been proposed, including the greedy algorithm, iterative improvement, and simulated annealing. The AB algorithm, which combines randomization and neighborhood search with the IK-KBZ algorithm, is presented. The AB algorithm is

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 hybridoptimal 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.

With advances in algorithms and growing computing powers, quantum Monte Carlo (QMC) methods have become a leading contender for high accuracy calculations for the electronic structure of realistic systems. The performance gain on recent HPC systems is largely driven by increasing parallelism: the number of compute cores of a SMP and the number of SMPs have been going up, as the Top500 list attests. However, the available memory as well as the communication and memory bandwidth per element has not kept pace with the increasing parallelism. This severely limits the applicability of QMC and the problem size it can handle. OpenMP/MPI hybrid programming provides applications with simple but effective solutions to overcome efficiency and scalability bottlenecks on large-scale clusters based on multi/many-core SMPs. We discuss the design and implementation of hybrid methods in QMCPACK and analyze its performance on current HPC platforms characterized by various memory and communication hierarchies.

Esler, Kenneth P [ORNL; Mcminis, Jeremy [University of Illinois, Urbana-Champaign; Morales, Miguel A [Lawrence Livermore National Laboratory (LLNL); Clark, Bryan K. [Princeton University; Shulenburger, Luke [Sandia National Laboratory (SNL); Ceperley, David M [ORNL

Evolutionary computation has become an important problem solving methodology among many researchers. The population-based\\u000a collective learning process, selfadaptation, and robustness are some of the key features of evolutionary algorithms when compared\\u000a to other global optimization techniques. Even though evolutionary computation has been widely accepted for solving several\\u000a important practical applications in engineering, business, commerce, etc., yet in practice sometimes they

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

The present paper describes a fundamental study on structural bending design to reduce noise using a new evolutionary population-based heuristic algorithm called the particle swarm optimizationalgorithm (PSOA). The particle swarm optimizationalgorithm is a parallel evolutionary computation technique proposed by Kennedy and Eberhart in 1995. This algorithm is based on the social behavior models for bird flocking, fish schooling and other models investigated by zoologists. Optimal structural design problems to reduce noise are highly nonlinear, so that most conventional methods are difficult to apply. The present paper investigates the applicability of PSOA to such problems. Optimal bending design of a vibrating plate using PSOA is performed in order to minimize noise radiation. PSOA can be effectively applied to such nonlinear acoustic radiation optimization.

Real world optimization problems are used to judge the performance of any Evolutionary Algorithm (EA) over real world applications. This is why the performance of any EA over the real world optimization problems is very important for judging its efficiency. In this work, we represent a multi- population based memetic algorithm CDELS. It is hybridization of a competitive variant of

Path planning of uninhabited combat air vehicle (UCAV) is a complicated global optimum problem. Ant colony optimization (ACO) algorithm was originally presented under the inspiration during collective behavior study results on real ant system, and it has strong robustness and easy to combine with other methods in optimization. In this paper, we propose a hybrid ACO with satisficing decision algorithm

The crew scheduling problem (CSP) is an important problem for the transportation enterprises, especially when crew's cost is increasing. This justifies the researcher's effort in order to create algorithms which obtain good solutions for this kind of problems. Particularly, hybridalgorithms appear as an efficient alternative in order to solve any combinatorial optimization problem. Tabu Search (TS) is a simple

Secure storage yard is one of the optimal core goals of container transportation; thus, making the necessary storage arrangements has become the most crucial part of the container terminal management systems (CTMS). This paper investigates a random hybrid stacking algorithm (RHSA) for outbound containers that randomly enter the yard. In the first stage of RHSA, the distribution among blocks was analyzed with respect to the utilization ratio. In the second stage, the optimization of bay configuration was carried out by using the hybrid genetic algorithm. Moreover, an experiment was performed to test the RHSA. The results show that the explored algorithm is useful to increase the efficiency.

Successive linear programming, an optimal control algorithm, and a combination of linear programming and dynamic programming (LP-DP) are employed to optimize the operation of multireservoir hydrosystems given a deterministic inflow forecast. The algorithm maximize the value of energy produced at on-peak and off-peak rates, plus the estimated value of water remaining in storage at the end of the 12-month planning period. The LP-DP algorithm is clearly dominated: it takes longer to find a solution and produces significantly less hydropower than the other two procedures. Successive linear programming (SLP) appears to find the global maximum and is easily implemented. For simple systems the optimal control algorithm finds the optimum in about one fifth the time required by SLP but is harder to implement. Computing costs for a two-reservoir, 12-month deterministic problem averaged about seven cents per run using optimal control and 37 cents using successive linear programming.

In this work an optimizationalgorithm for the calculation of water unit cost from various RO candidate schemes was developed. Such an algorithm may be used for evaluation purposes when many RO candidate schemes are taken into account. The applicability of the method is demonstrated on an example in which six RO candidate schemes are examined.

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 optimizationalgorithms 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.

This paper presents an evolutionary algorithm for generic multiobjective design optimization problems. The algorithm is based on nondominance of solutions in the objective and constraint space and uses effective mating strategies to improve solutions that are weak in either. Since the methodology is based on nondominance, scaling and aggregation affecting conventional penalty function methods for constraint handling does not arise.

This paper introduces a new cellular genetic algorithm for solving multiobjective contin- uous optimization problems. Our approach is characterized by using an external archive to store non-dominated solutions and a feedback mechanism in which solutions from this archive randomly replaces existing individuals in the population after each iteration. The result is a simple and elitist algorithm called MOCell. Our proposal

A. J. Nebro; J. J. Durillo; F. Luna; B. Dorronsoro; E. Alba

Taking inspiration from the interacting process among organizations in human societies, this correspondence designs a kind of structured population and corresponding evolutionary operators to form a novel algorithm, organizational evolutionary algorithm (OEA), for solving both unconstrained and constrained optimization problems. In OEA, a population consists of organizations, and an organization consists of individuals. All evolutionary operators are designed to simulate

This paper presents a novel bacterial swarming algorithm (BSA) for global optimization. This algorithm is inspired by swarming behaviors of bacteria, in particular, focusing on the study of tumble and run actions which are the major part of the chemotactic process. Adaptive tumble and run operators are developed to improve the global and local search capability of the BSA, based

A new particle swarm optimization (PSO) algorithm is presented based on three methods of improvement in original PSO. First, the iteration formula of PSO is changed and simplified by removal of velocity parameter that is unnecessary during the course of evolution. Second, the dynamically decreasing inertia weight is employed to enhance the balance of global and local search of algorithm.

We present a new hybridoptimization method for the synthesis of fiber Bragg gratings (FBGs) with complex characteristics. The hybridoptimization method is a two-tier search that employs a global optimizationalgorithm [i.e., the tabu search (TS) algorithm] and a local optimization method (i.e., the quasi-Netwon method). First the TS global optimizationalgorithm 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. PMID:15603077

Zheng, Rui Tao; Ngo, Nam Quoc; Le Binh, Nguyen; Tjin, Swee Chuan

We present a new hybridoptimization method for the synthesis of fiber Bragg gratings (FBGs) with complex characteristics. The hybridoptimization method is a two-tier search that employs a global optimizationalgorithm [i.e., the tabu search (TS) algorithm] and a local optimization method (i.e., the quasi-Netwon method). First the TS global optimizationalgorithm 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.

Zheng, Rui Tao; Ngo, Nam Quoc; Binh, Le Nguyen; Tjin, Swee Chuan

A general algorithm is developed for online optimization of accelerator performance, i.e., online tuning, using the performance measure as the objective function. This method, named robust conjugate direction search (RCDS), combines the conjugate direction set approach of Powell's method with a robust line optimizer which considers the random noise in bracketing the minimum and uses parabolic fit of data points that uniformly sample the bracketed zone. It is much more robust against noise than traditional algorithms and is therefore suitable for online application. Simulation and experimental studies have been carried out to demonstrate the strength of the new algorithm.

In this paper we propose the use of the genetic algorithm (GA) as a tool to solve multiobjective optimization problems in structures. Using the concept of min–max optimum, a new GA-based multiobjective optimization technique is proposed and two truss design problems are solved using it. The results produced by this new approach are compared to those produced by other mathematical

This paper presents two new strategies that can be used to greatly improve the speed of connected component labeling algorithms. To assign a label to a new object, most connected component labeling algorithms use a scanning step that examines some of its neighbors. The first strategy exploits the dependencies among them to reduce the number of neighbors examined. When considering 8-connected components in a 2D image, this can reduce the number of neighbors examined from four to one in many cases. The second strategy uses an array to store the equivalence information among the labels. This replaces the pointer based rooted trees used to store the same equivalence information. It reduces the memory required and also produces consecutive final labels. Using an array instead of the pointer based rooted trees speeds up the connected component labeling algorithms by a factor of 5 {approx} 100 in our tests on random binary images.

\\u000a A hybrid adaptive memetic algorithm for a multi-objective combinatorial optimization problem is proposed in this paper. Different\\u000a solution fitness evaluation methods are hybridized to achieve global exploitation and exploration. At each generation, a wide\\u000a diversified set of weights are used to search across all regions in objective space, and each weighted linear utility function\\u000a is optimized with a simulated annealing.

In this paper, we present a hybrid ant colony optimization\\/particle swarm optimization (ACO\\/PSO) control algorithm for distributed swarm robots, where each robot can only communicate with its neighbors within its communication range. A virtual pheromone mechanism is proposed as the message passing coordination scheme among the robots. This hybrid ACO\\/PSO architecture adopts the feedback mechanism from environment of ACO and

A hybrid simplex artificial bee colony algorithm (HSABCA) which combines Nelder–Mead simplex method with artificial bee colony algorithm (ABCA) is proposed for inverse analysis problems. The proposed algorithm is applied to parameter identification of concrete dam-foundation systems. To verify the performance of HSABCA, it is compared with the basic ABCA and a real coded genetic algorithm (RCGA) on two examples:

\\u000a Numerical optimization of given objective functions is a crucial task in many real-life problems. The present article introduces\\u000a an immunological algorithm for continuous global optimization problems, called opt-IA. Several biologically inspired algorithms have been designed during the last few years and have shown to have very good performance\\u000a on standard test bed for numerical optimization.\\u000a \\u000a \\u000a In this paper we assess

Vincenzo Cutello; Giuseppe Narzisi; Giuseppe Nicosia; Mario Pavone

This paper considers the combined use of a hybrid numerical method for the modeling of acoustic mufflers and a genetic algorithm for multiobjective optimization. The hybrid numerical method provides accurate modeling of sound propagation in uniform waveguides with non-uniform obstructions. It is based on coupling a wave based modal solution in the uniform sections of the waveguide to a finite element solution in the non-uniform component. Finite element method provides flexible modeling of complicated geometries, varying material parameters, and boundary conditions, while the wave based solution leads to accurate treatment of non-reflecting boundaries and straightforward computation of the transmission loss (TL) of the muffler. The goal of optimization is to maximize TL at multiple frequency ranges simultaneously by adjusting chosen shape parameters of the muffler. This task is formulated as a multiobjective optimization problem with the objectives depending on the solution of the simulation model. NSGA-II genetic algorithm is used for solving the multiobjective optimization problem. Genetic algorithms can be easily combined with different simulation methods, and they are not sensitive to the smoothness properties of the objective functions. Numerical experiments demonstrate the accuracy and feasibility of the model-based optimization method in muffler design. PMID:21895077

In a clustering problem, the aim is to partition a given set of n points in d-dimensional space into k groups, called clusters, so that points within each cluster are near each other. Two objective functions frequently used to measure the performance of a clustering algorithm are, for any L4 metric, (a) the maximum distance between pairs of points in

This paper presents a hybrid technique for optimal discrete-time control of a continuous-time nonlinear Turret-gun system. For effective design of this nonlinear Turret-gun system with multi-flexible modes, the linearized state-space representation of the nonlinear system is block-decoupled into a multi-time scale structure using the fast and stable matrix sign algorithm. Then, to enhance the robust stability and robust performance of

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 approach produced designs which were often significant improvements over hybrid designs already reported on in the

A hybrid particle swarm optimizer with mass extinction, which has been suggested to be an important mechanism for evolutionary progress in the biological world, is presented to enhance the capacity in reaching an optimal solution. The tested results of three benchmark functions indicate this method improves the performance effectively.

We develop an algorithmichybrid for simulating multiscale fluid flow with microscopic fluctuations. Random fluctuations occur in fluids at microscopic scales, and these microscopic fluctuations can lead to macroscopic system effects. For example, in the Rayleigh-Taylor problem, where a relatively heavy gas sits on top of a relatively light gas, spontaneous microscopic fluctuation at the interface of the gases leads to turbulent mixing. Given near-term computational power, the physical and temporal domain on which these systems can be studied using traditional particle simulations is extremely limited. Therefore, we seek algorithmic solutions to increase the effective computing power available to study such problems. We develop an explicit numerical solver for the Landau-Lifshitz Navier-Stokes (LLNS) equations, which incorporate thermal fluctuations into macroscopic hydrodynamics via stochastic; fluxes. A major goal is to correctly preserve the influence of the microscopic fluctuations on the behavior of the system. We show that several classical approaches fail to accurately reproduce fluctuations in energy or density, and we introduce a customized conservative centered scheme with a third-order Runge-Kutta temporal integrator that is specficially designed to produce correct fluctuations in all conserved quantities. We then use the adaptive mesh and algorithm refinement (AMAR) paradigm to create a multiscale hybrid method by coupling our LLNS solver with the direct simulation Monte Carlo (DSMC) particle method. We present numerical tests of systems in and out of equilibrium, including time-dependent systems, and demonstrate dynamic adaptive refinement. Mean system behavior and second moment statistics of our simulations match theoretical values and benchmarks well. We find that particular attention should be paid to the spectrum of the flux at the interface between the particle and continuum methods, specifically at non-hydrodynamic time scales. As an extension of the basic hybrid method, we consider the dynamics of a binary mixture of gases. Benchmark test problems, including a system under concentration gradient, match theory and molecular simulation well. We find that to study mixtures of gases with unequal masses, at equilibrium it may be reasonable to neglect the Soret effect; however, inclusion of the baro-diffusion effect is important.

Two stochastic optimizationalgorithms 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

Wagner F. Sacco; Cassiano R. E. de oliveira; Cláudio M. N. A. Pereira

The optimizationalgorithms 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.

In the last decade, the notion of metric embeddings with small distortion received wide attention in the literature, with applications in combinatorial optimization, discrete mathematics and bio-informatics. The notion of embedding is, given two metric spaces on the same number of points, to flnd a bijection that minimizes maximum Lipschitz and bi-Lipschitz constants. One reason for the popularity of the

Nishanth Chandran; Ryan Moriarty; Rafail Ostrovsky; Omkant Pandey; Mohammad Ali Safari; Amit Sahai

The phase unwrapping is the final and trickiest step in any phase retrieval technique. Phase unwrapping by artificial intelligence methods (optimizationalgorithms) 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.

A new algorithm named energy management by successive linear programming (EMSLP) was developed to solve the optimization problem of the hydropower system operation. The EMSLP algorithm has two iteration levels: at the first level a stable solution is sought, and at the second the interior of the feasible region is searched to improve the objective function whenever its value decreases. The EMSLP algorithm has been tested using the Manitoba Hydro system data applied to a single reservoir system. To evaluate the performance of the algorithm the comparison has been made with the results obtained by the energy management and maintenance analysis (EMMA) program used in the Manitoba Hydro practice. The paper describes the EMSLP algorithm and presents the results of the comparison with EMMA.

This paper examines the forecasting performance of multi-layer feed forward neural networks in modeling a particular foreign exchange rates, i.e. Japanese Yen/US Dollar. The effects of two learning methods, Back Propagation and Genetic Algorithm, in which the neural network topology and other parameters fixed, were investigated. The early results indicate that the application of this hybrid system seems to be well suited for the forecasting of foreign exchange rates. The Neural Networks and Genetic Algorithm were programmed using MATLAB®.

Setyawati, Bina R.; Creese, Robert C.; Sahirman, Sidharta

This paper presents an efficient and reliable evolutionary-based approach to solve the optimal power flow (OPF) problem. The\\u000a proposed approach employs differential evolution algorithm for optimal settings of OPF problem control variables. The proposed\\u000a approach is examined and tested on the standard IEEE 30-bus test system with different objectives that reflect fuel cost minimization,\\u000a voltage profile improvement, and voltage stability

An optimal spectrum extraction procedure is described, and examples of its performance with CCD data are presented. The algorithm delivers the maximum possible signal-to-noise ratio while preserving spectrophotometric accuracy. The effects of moderate geometric distortion and of cosmic-ray hits on the spectrum are automatically accounted for. In tests with background-noise limited CCD spectra, optimal extraction offers a 70-percent gain in effective exposure time in comparison with conventional extraction procedures.

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 optimalalgorithm, 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.

In this paper, a model-based strategy for the real-time load control of parallel hybrid vehicles is presented. The aim is to develop a fuel-optimal control which is not relying on the a priori knowledge of the future driving conditions (global optimal control), but only upon the current system operation. The methodology developed is valid for those problem that are characterized

The present paper describes the use of a stochastic search procedure that is the basis of genetic algorithms, in developing near-optimal topologies of load-bearing truss structures. The problem addressed is one wherein the structural geometry is created from a specification of load conditions and available support points in the design space. The development of this geometry must satisfy kinematic stability

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.

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

In this paper we described a blind optimization technique with an in-expensive electronics for optical systems to maximize the output signal. Deformable mirror is the main optical element used in the system to correct the wavefront and increase the output signal. The mirror is controlled by genetic algorithms through the computer microphone port and two PCI-Express cards.

Mohammad R. N. Avanaki; S. A. Hojjatoleslami; R. Ebrahimpour; H. Sarmadi; A. G. H. Podoleanu

This paper deals with solution methods of optimal synthesis of planar mechanisms. A searching procedure is defined which applies genetic algorithms based on evolutionary techniques and the type of goal function. Problems of synthesis of four-bar planar mechanisms are used to test the method, showing that solutions are accurate and valid for all cases. The possibility of extending the method

This paper presents an enhanced genetic algorithm for the solution of the optimal power flow with both continuous and discrete control variables. The continuous control variables modeled are unit active power outputs and generator-bus voltage magnitudes, while the discrete ones are transformer-tap settings and switchable shunt devices. A number of functional operating constraints, such as branch flow limits, load bus

A. G. Bakirtzis; P. N. Biskas; C. E. Zoumas; V. Petridis

This paper presents an enhanced genetic algorithm (EGA) for the solution of the optimal power flow (OPF) with both continuous and discrete control variables. The continuous control variables modeled are unit active power outputs and generator-bus voltage magnitudes, while the discrete ones are transformer-tap settings and switchable shunt devices. A number of functional operating constraints, such as branch flow limits,

Anastasios G. Bakirtzis; Pandel N. Biskas; Christoforos E. Zoumas; Vasilios Petridis

In this paper, a novel optimizationalgorithm - artificial searching swarm algorithm (ASSA) is presented by analyzing the operating principle and uniform framework of the bionic intelligent optimizationalgorithm. ASSA simulates the process of solving optimal design problem to the process of searching optimal goal by searching swarm with the set rules, and finds the optimal solution through the search

A review is presented of available fuels and energy sources with regional planning strategies for hybrid energy optimization. The fuels include coal, crops, nuclear sources, geothermal, hydraulic power, and solar; energy sources are classified as genotypes which are easily applied at scaled levels within a defined region, and phenotypes which are modifications of regional sources to suit any site within

This paper investigates development of power management strategies tailored specifically to a medium truck with parallel hydraulic hybrid powertrain. The hydraulic hybrid vehicle (HHV) system is modeled in MATLAB\\/SIMULINK environment. As the starting point, this study considers rule-based power management strategy adopted from the previous HEV study. Dynamic Programming (DP) algorithm is used to find the optimal trajectories for gear

Bin Wu; Chan-Chiao Lin; Zoran Filipi; Huei Peng; Dennis Assanis

This study reports a detailed investigation of catalyst library design by genetic algorithm (GA). A methodology for assessing GA configurations is described. Operators, which promote the optimization speed while being robust to noise and outliers, are revealed through statistical studies. The genetic algorithms were implemented in GA platform software called OptiCat, which enables the construction of custom-made workflows using a tool box of operators. Two separate studies were carried out (i) on a virtual benchmark and (ii) on real surface response which is derived from HT screening. Additionally, we report a methodology to model a complex surface response by binning the search space in small zones that are then independently modeled by linear regression. In contrast to artificial neural networks, this approach allows one to obtain an explicit model in an analogical form that can be further used in Excel or entered in OptiCat to perform simulations. While speeding the implementation of a hybridalgorithm combining a GA with a knowledge-based extraction engine is described, while speeding up the optimization process by means of virtual prescreening the hybrid GA enables one to open the ``black-box'' by providing knowledge as a set of association rules.

Clerc, Frederic; Lengliz, Mourad; Farrusseng, David; Mirodatos, Claude; Pereira, Sílvia R. M.; Rakotomalala, Ricco

A hybrid particle swarm optimization (PSO) approach is proposed here to solve the distribution network reconfiguration (DNR) problem. This approach is a combination of the binary PSO algorithm and the discrete PSO algorithm. In the problem-solving process, the distribution network is simplified through grouping the branches, and then each group of branches is represented by one dimensional coding. Based on

Zhenkun Li; Xingying Chen; Kun Yu; Yi Sun; Haoming Liu

In this paper, we study an optimal design for a hybrid power source railway vehicle as an alternative to diesel railway vehicles. The hybrid power source railway vehicle is assumed to be composed of the fuel cell and the electric double layer capacitor. We apply the multiobjective optimization based on the genetic algorithm for the vehicle design, aiming at reduction of both initial cost and energy consumption. The pareto optimal solutions are obtained using the multiobjective optimization. First we develop a simulation model of the hybrid power source railway vehicle and its electric power control methods. Next we derive the pareto optimal solutions as a result of the multiobjective optimization. Finally, we categorize the pareto optimal solutions to some groups, which enables us to elucidate characteristics of the pareto optimal solutions. Consequently, using the multiobjective optimization approach we effectively comprehend the problem characteristics and can obtain the plural valuable solutions.

This brief proposes hybrid stable adaptive fuzzy controller design procedures utilizing the conventional Lyapunov theory and, the relatively newly devised harmony search (HS) algorithm-based stochastic approach. The objective is to design a self-adaptive fuzzy controller, optimizing both its structures and free parameters, such that the designed controller can guarantee desired stability and simultaneously it can provide satisfactory performance with a

Kaushik Das Sharma; Amitava Chatterjee; Anjan Rakshit

Design optimization of axial hybrid magnetic thrust bearings (with bias magnets) was carried out using multi-objective evolutionary algorithms (MOEAs) and compared with the case of electromagnetic bearings (without bias magnets). Mathematical models of objective functions and associated constraints are presented and discussed. The different aspects of implemented MOEA are discussed. It is observed that the size of the bearing with

Hydrologic time series forecasting is very an important area in water resource. Based on the multi-time scale and the nonlinear characteristics of the rainfall-runoff time series, a new hybrid neural network (NN) has been suggested by Genetic Algorithm (GA) selection the lag period of time series for NN input variables, optimization neural network architecture and connection weights. The evolved neural

Smart Grid technology is recognized as a key com- ponent of the solution to challenges such as the increasing electric demand, an aging utility infrastructure and workforce, and the environmental impact of greenhouse gases produced during elec- tric generation. This paper presents the application of a hybridoptimizationalgorithm for distributed energy resource (DER) management in Smart Grid operation. The

Bhuvaneswari Ramachandran; Sanjeev K. Srivastava; Chris S. Edrington; David A. Cartes

The protein folding problem consists of attempting to determine the native conformation of a protein given its primary structure. This study examines various methods of hybridizing a genetic algorithm implementation in order to minimize an energy function...

This paper presents a hybrid disparity estimation algorithm whichcombinesthe pixel-basedandregion-basedapproaches. In the pixel-based approach, we use the Gabor transform and variational refinement, and the resulting disparities are com- bined with region information obtained from image segmen- tation so that a region matching scheme can be further ap- plied. It will be shown with 3D reconstruction that our hybridalgorithm can

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 tie...

This thesis concerns the use of genetic algorithms in the optimization of the trajectories of low thrust spacecraft. Genetic algorithms are programming tools which use the principles of biological evolution and adaptation to optimize processes. These algo...

The authors have been using genetic algorithms to study the structures of atomic clusters and related problems. This is a problem where local minima are easy to locate, but barriers between the many minima are large, and the number of minima prohibit a systematic search. They use a novel mating algorithm that preserves some of the geometrical relationship between atoms, in order to ensure that the resultant structures are likely to inherit the best features of the parent clusters. Using this approach, they have been able to find lower energy structures than had been previously obtained. Most recently, they have been able to turn around the building block idea, using optimized structures from the GA to learn about systematic structural trends. They believe that an effective GA can help provide such heuristic information, and (conversely) that such information can be introduced back into the algorithm to assist in the search process.

Morris, J.R.; Deaven, D.M.; Ho, K.M.; Wang, C.Z.; Pan, B.C.; Wacker, J.G.; Turner, D.E. [Ames Lab., IA (United States)]|[Iowa State Univ., Ames, IA (United States). Dept. of Physics

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.

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 algorithmoptimization, 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.

This paper proposes a hybrid feature selection algorithm based on dynamic weighted ant colony algorithm. Features are treated as graph nodes to construct graph model. Ant colony algorithm is used to select features while support vector machine classifier is applied to evaluate the performance of feature subsets, and then feature pheromone is computed and updated based on the evaluation results.

Although the expectation-maximization (EM) algorithm has been widely used for finding maximum likelihood estimation of parameters in probabilistic models, it has the problem of trapping by local maxima. To overcome this problem, the deterministic annealing EM (DAEM) algorithm was once proposed and had achieved better performance than EM algorithm, but it is not very effective at avoiding local maxima. In this paper, a solution is proposed by integrating GA and DAEM into one procedure to further improve the solution quality. The population based search of genetic algorithm will produce different solutions and thus can increase the search space of DAEM. Therefore, the proposed algorithm will reach better solution than just using DAEM. The algorithm retains the property of DAEM and gets the better solution by genetic operation. Experiment results on Gaussian mixture model parameter estimation demonstrate that the proposed algorithm can achieve better performance.

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. optimization is studied.

Optimization of fixture layout (locator and clamp locations) is critical to reduce geometric error of the workpiece during machining process. In this paper, the application of particle swarm optimization (PSO) algorithm is presented to minimize the workpiece deformation in the machining region. A PSO based approach is developed to optimize fixture layout through integrating ANSYS parametric design language (APDL) of finite element analysis to compute the objective function for a given fixture layout. Particle library approach is used to decrease the total computation time. The computational experiment of 2D case shows that the numbers of function evaluations are decreased about 96%. Case study illustrates the effectiveness and efficiency of the PSO based optimization approach.

The genetic algorithm technique is a relatively new optimization tech- nique. In this paper we present a methodology for optimizing pipe networks using genetic algorithms. Unknown decision variables are coded as binary strings. We investigate a three-operator genetic algorithm comprising reproduction, crossover, and mutation. Results are compared with the techniques of complete enumeration and nonlinear programming. We apply the optimization

Angus R. Simpson; Graeme C. Dandy; Laurence J. Murphy

Abstract The objective of this research was the development,of a method,that integrated a data envelopment analysis economic model of production with a biophysical model, and included the capacity for optimization over multiple objectives. We specified a hybrid genetic algorithm using DEA as a local search method, and NSGA-II for calculation of the multiple objective Pareto optimal set. We describe a

Gerald Whittaker; Remegio Confesor; Stephen M. Griffith; Rolf Färe; Shawna Grosskopf; Jeffery J. Steiner; George W. Mueller-Warrant

Path integral hybrid Monte Carlo (PIHMC) algorithm for strongly correlated Bose fluids has been developed. This is an extended version of our previous method [S. Miura and S. Okazaki, Chem. Phys. Lett. 308, 115 (1999)] applied to a model system consisting of noninteracting bosons. Our PIHMC method for the correlated Bose fluids is constituted of two trial moves to sample path-variables describing system coordinates along imaginary time and a permutation of particle labels giving a boundary condition with respect to imaginary time. The path-variables for a given permutation are generated by a hybrid Monte Carlo method based on path integral molecular dynamics techniques. Equations of motion for the path-variables are formulated on the basis of a collective coordinate representation of the path, staging variables, to enhance the sampling efficiency. The permutation sampling to satisfy Bose-Einstein statistics is performed using the multilevel Metropolis method developed by Ceperley and Pollock [Phys. Rev. Lett. 56, 351 (1986)]. Our PIHMC method has successfully been applied to liquid helium-4 at a state point where the system is in a superfluid phase. Parameters determining the sampling efficiency are optimized in such a way that correlation among successive PIHMC steps is minimized. PMID:15268354

In this paper, a hybridalgorithm for precise eyes localization in color facial region is presented. In practice, most eye detection suffer from the influence of illumination and face pose. Multiple techniques are integrated in this algorithm to overcome these limitations, such as color space mapping, illumination correction, mouth detection, dynamic threshold and so on. The scheme consists of two

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 hybridoptimization 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.

This paper aims to compare the global search capability and overall performance of a number of Particle Swarm Optimization (PSO) based algorithms in the context solving the Dynamic Economic Dispatch (DED) problem which takes into account the operation limitations of generation units such as valve-point loading effect as well as ramp rate limits. The comparative study uses six PSO-based algorithms including the basic PSO and hybrid PSO algorithms using a popular benchmark test IEEE power system which is 10-unit 24-hour system with non-smooth cost functions. The experimental results show that one of the hybridalgorithms that combines the PSO with both inertia weight and constriction factor, and the Gaussian mutation operator (CBPSO-GM) is promising in achieving the near global optimal of a non-linear multi-modal optimization problem, such as the DED problem under the consideration.

Many techniques exist for developing optimal controllers. This paper investigates genetic algorithms as a means of finding optimal solutions over a parameter space. In particular, the genetic algorithm is applied to optimal tuning of a governor for a hydrogenerator plant. Analog and digital simulation methods are compared for use in conjunction with the genetic algorithmoptimization process. It is shown that analog plant simulation provides advantages in speed over digital plant simulation. This speed advantage makes application of the genetic algorithm in an actual plant environment feasible. Furthermore, the genetic algorithm is shown to possess the ability to reject plant noise and other system anomalies in its search for optimizing solutions.

Lansberry, J.E. (Illinois Univ., Urbana, IL (United States). Dept. of Electrical and Computer Engineering); Wozniak, L.; Goldberg, D.E. (Illinois Univ., Urbana, IL (United States). Dept. of General Engineering)

Selecting the optimal topology of a neural network for a particular application is a difficult task. Genetic Algorithm (GA) has been used to find the optimal neural network (NN) solution (i.e., hybrid technique) to calculate the pseudo-rapidity distribution of the shower particles for C12, O16, Si28, and S32 on nuclear emulsion. An efficient NN has been designed by GA to predict the distributions that are not present in the training set and matched them effectively. The proposed method shows a better fitting with experimental data. The hybrid technique GA-ANN simulation results prove a strong presence modeling in heavy ion collisions.

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

This paper gives a new evolutionary algorithm for structure topology optimization. It combines the merits of level set method (LSM) and evolutionary structure optimization (ESO) for structure topology optimization. The traditional LSM algorithm has some drawbacks, for instance, its optimal topology configuration is largely dependent on the initial configuration of the structure. Additionally, new holes cannot be inserted into the

Strengthen the environmental protection is one of the basic national policies in China. The optimization of urban drain layout plays an important role to the protection of water ecosystem and urban environment. The paper puts forward a method to properly locate urban drain using population based incremental learning (PBIL) algorithm. The main factors such as regional containing sewage capacity, sewage disposal capacity quantity limit of drains within specific area are considered as constraint conditions. Analytic hierarchy process is used to obtain weight of each factor, and spatial analysis of environmental influencing factors is carried on Based on GIS. Penalty function method is put forward to model the problem and object function is to guarantee economy benefit. The algorithm is applied to the drain layout engineering of Nansha District, Guangzhou City, China. The drain layout obtained though PBIL algorithm excels traditional method and it can protect the urban environment more efficiently and ensure the healthy development of water ecosystem more successfully. The result has also proved that PBIL algorithm is a good method in solving this question because of its robust performance and stability which supplied strong technologic support to the sustainable development of environment.

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.

This paper investigates the ability of Multiobjective Evolutionary Algorithms (MOEAs), namely the Non-dominated Sorting Genetic\\u000a Algorithm II (NSGA-II), Pareto Envelope-based Selection Algorithm (PESA) and Strength Pareto Evolutionary Algorithm 2 (SPEA2),\\u000a for solving complex portfolio optimization problems. The portfolio optimization problem is a typical bi-objective optimization\\u000a problem with objectives the reward that should be maximized and the risk that should be

The correction of a thermal model for a thermally controlled satellite in ground test conditions is studied using a Monte\\u000a Carlo hybridalgorithm. First, the global and local parameters are summarized according to sensitivity analyses on uncertain\\u000a parameters, and then the model correction is treated as a parameter optimization problem to be solved with a hybridalgorithm.\\u000a Finally, the correction

WenLong Cheng; Na Liu; Zhi Li; Qi Zhong; AiMing Wang; ZhiMin Zhang; ZongBo He

In order to test the high dynamic range error beyond one wavelength after the rough polish process, we design a phase retrieval hybridalgorithm based on diffraction angular spectrum theory. Phase retrieval is a wave front sensing method that uses the intensity distribution to reconstruct the phase distribution of optical field. Phase retrieval is established on the model of diffractive propagation and approach the real intensity distribution gradually. In this paper, we introduce the basic principle and challenges of optical surface measurement using phase retrieval, then discuss the major parts of phase retrieval: diffractive propagation and hybridalgorithm. The angular spectrum theory describes the diffractive propagation in the frequency domain instead of spatial domain, which simplifies the computation greatly. Through the theoretical analysis, the angular spectrum in discrete form is more effective when the high frequency part values less and the diffractive distance isn't far. The phase retrieval hybridalgorithm derives from modified GS algorithm and conjugate gradient method, aiming to solve the problem of phase wrapping caused by the high dynamic range error. In the algorithm, phase distribution is described by Zernike polynomials and the coefficients of Zernike polynomials are optimized by the hybridalgorithm. Simulation results show that the retrieved phase distribution and real phase distribution are quite contiguous for the high dynamic range error beyond ?.

We propose a novel stochastic optimizationalgorithm, hybrid simulated annealing (SA), to train hidden Markov models (HMMs) for visual speech recognition. In our algorithm, SA is combined with a local optimization operator that substitutes a better solution for the current one to improve the convergence speed and the quality of solutions. We mathematically prove that the sequence of the objective values converges in probability to the global optimum in the algorithm. The algorithm is applied to train HMMs that are used as visual speech recognizers. While the popular training method of HMMs, the expectation-maximization algorithm, achieves only local optima in the parameter space, the proposed method can perform global optimization of the parameters of HMMs and thereby obtain solutions yielding improved recognition performance. The superiority of the proposed algorithm to the conventional ones is demonstrated via isolated word recognition experiments. PMID:20064758

Marriage in honey bees optimization (MBO) is a metaheuristic optimizationalgorithm 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 optimizationalgorithms.

A hybrid deterministic–stochastic algorithm combining the simplex method (SM) and a genetic algorithm (GA) was applied to\\u000a the problem of extracting the optical and morphological properties of human skin (HSOMPs) from visual reflectance spectroscopy\\u000a data. The results using the GA-SM hybridalgorithm adopting tournament selection and selecting new sets of HSOMPs were compared\\u000a with those using other conventional optimizationalgorithms

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 optimizationalgorithm 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

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 optimizationalgorithm 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.

The introduction of the concept of swarm intelligence into ant colony optimization (ACO) algorithms has shown the rich possibilities of self-organization when dealing with difficult optimization. Indeed, the inherent flexibility and efficiency of ACO algorithms proved to be advantageous for difficult dynamic discrete problems, e.g. routing in telecommunication networks. Moreover, we believe that ant colony algorithms can be efficient for

According to the path information collected,combined with the characteristics of the car body, the data of the path has been analyzed, the path optimizingalgorithm for line track robots has been established, and the correctness of the algorithm has been verified in experiments in this paper. The algorithm design has some certain significance for the line track optimization of robots

In this paper we present an estimation of distribution par- ticle swarm optimizationalgorithm that borrows ideas from recent de- velopments in ant colony optimization which can be considered an es- timation of distribution algorithm. In the classical particle swarm opti- mization algorithm, particles exploit their individual memory to explore the search space. However, the swarm as a whole has

This article describes the design of highly complex physical instruments by using a canonical genetic algorithm (GA). The procedure can be applied to all instrument designs where performance goals can be quantified. It is particularly suited to the optimization of instrument design where local optima in the performance figure of merit are prevalent. Here, a GA is used to evolve the design of the neutron spin-echo spectrometer WASP which is presently being constructed at the Institut Laue-Langevin, Grenoble, France. A comparison is made between this artificial intelligence approach and the traditional manual design methods. We demonstrate that the search of parameter space is more efficient when applying the genetic algorithm, and the GA produces a significantly better instrument design. Furthermore, it is found that the GA increases flexibility, by facilitating the reoptimization of the design after changes in boundary conditions during the design phase. The GA also allows the exploration of ``nonstandard'' magnet coil geometries. We conclude that this technique constitutes a powerful complementary tool for the design and optimization of complex scientific apparatus, without replacing the careful thought processes employed in traditional design methods.

Hölzel, Robert; Bentley, Phillip M.; Fouquet, Peter

Ion mobility spectrometry (IMS) is increasingly employed to probe the structures of gas-phase ions, particularly those of proteins and other biological macromolecules. This process involves comparing measured mobilities to those computed for potential geometries, which requires evaluation of orientationally averaged cross sections using some approximate treatment of ion-buffer gas collisions. Two common models are the projection approximation (PA) and exact hard-spheres scattering (EHSS) that represent ions as collections of hard spheres. Though calculations for large ions and/or conformer ensembles take significant time, no algorithmicoptimization had been explored. Previous EHSS programs were dominated by ion rotation operations that allow orientational averaging. We have developed two new algorithms for PA and EHSS calculations: one simplifies those operations and greatly reduces their number, and the other disposes of them altogether by propagating trajectories from a random origin. The new algorithms were tested for a representative set of seven ion geometries including diverse sizes and shapes. While the best choice depends on the geometry in a nonobvious way, the difference between the two codes is generally modest. Both are much more efficient than the existing software, for example faster than the widely used Mobcal (implementing EHSS) approximately 10-30-fold. PMID:17300182

Shvartsburg, Alexandre A; Mashkevich, Stefan V; Baker, Erin Shammel; Smith, Richard D

In this paper, the disturbance attenuation properties for a class of linear hybrid systems are investigated, and a hybridoptimal persistent disturbance attenuation control problem is studied. First, a procedure is developed to determine the minimal l? induced gain of linear hybrid systems. However, for general hybrid systems, the termination of the procedure is not guaranteed. Then, the decidability issues

Techniques for optimizing a static voting type algorithm are presented. Our optimization model is based on minimizing communications cost subject to a given availability constraint. We describe a semi-exhaustive algorithm for solving this model. This algorithm utilizes a novel signature-based method for identifying equivalent vote combinations, and also an efficient algorithm for computing availability. Static algorithms naturally have the advantage of simplicity; however, votes and quorum sizes are not allowed to vary. Therefore, the optimized static algorithm was compared against the available copies method, a dynamic algorithm, to understand the relative performance of the two types of algorithms. We found that if realistic reconfiguration times are assumed, then no one type of algorithm is uniformly better. The factors that influence relative performance have been identified. The available copies algorithm does better when the update traffic ratio is small, and the variability in the inter-site communications cost is low. 15 refs., 1 fig., 3 tabs.

A hybrid evolutionary algorithm, consisting of a genetic algorithm (GA) and particle swarm optimization (PSO), is proposed. Generally, GAs maintain diverse solutions of good quality in multi-objective problems, while PSO shows fast convergence to the optimum solution. By coupling these algorithms, GA will compensate for the low diversity of PSO, while PSO will compensate for the high computational costs of GA. The hybridalgorithm was validated using standard test functions. The results showed that the hybridalgorithm has better performance than either a pure GA or pure PSO. The method was applied to an engineering design problem—the geometry of diesel engine combustion chamber reducing exhaust emissions such as NOx, soot and CO was optimized. The results demonstrated the usefulness of the present method to this engineering design problem. To identify the relation between exhaust emissions and combustion chamber geometry, data mining was performed with a self-organising map (SOM). The results indicate that the volume near the lower central part of the combustion chamber has a large effect on exhaust emissions and the optimum chamber geometry will vary depending on fuel injection angle.

Genetic algorithms (GAs) are stochastic optimization heuristics in which searches in solution space are carried out by imitating the population genetics stated in Darwin's theory of evolution. The compact genetic algorithm (cGA) does not manage a population of solutions but only mimics its existence. The combination of genetic and local search heuristic has been shown to be an effective approach

José Ignacio Hidalgo; Juan Lanchares; Aitor Ibarra; Román Hermida

This paper addresses a hybrid particle swarm optimization-based approach for solving a generating unit maintenance scheduling problem (GMS). We focus on the power system reliability such as reserve ratio better than cost function as the objective function of GMS problem. It is shown that particle swarm optimization-based method is more effective in obtaining feasible schedules such as GMS problem related

Young-Soo Park; Jin-Ho Kim; June-Ho Park; Jun-Hee Hong

This paper develops an Improved harmony search (IHS) algorithm for solving optimization problems. IHS employs a novel method for generating new solution vectors that enhances accuracy and convergence rate of harmony search (HS) algorithm. In this paper the impacts of constant parameters on harmony search algorithm are discussed and a strategy for tuning these parameters is presented. The IHS algorithm

This paper describes two new harmony search (HS) meta-heuristic algorithms for engineering optimization problems with continuous design variables. The key difference between these algorithms and traditional (HS) method is in the way of adjusting bandwidth (bw). bw is very important factor for the high efficiency of the harmony search algorithms and can be potentially useful in adjusting convergence rate of algorithms to optimal solution. First algorithm, proposed harmony search (PHS), introduces a new definition of bandwidth (bw). Second algorithm, improving proposed harmony search (IPHS) employs to enhance accuracy and convergence rate of PHS algorithm. In IPHS, non-uniform mutation operation is introduced which is combination of Yang bandwidth and PHS bandwidth. Various engineering optimization problems, including mathematical function minimization problems and structural engineering optimization problems, are presented to demonstrate the effectiveness and robustness of these algorithms. In all cases, the solutions obtained using IPHS are in agreement or better than those obtained from other methods.

The BP feed-forward neural network is popular in solving many non-linear multivariate and complex problems. The most important problem with neural network is to decide optimal structure and parameter settings. Literature presents a multitude of methods but there is no rigorous and accurate analytical method. This paper presents the hybrid approach of genetic algorithm and neural network computing for establishment

Optimal path planning for mobile robots plays an important role in the field of robotics. At present, there are many advanced algorithms used to solve this optimal problem. However, for those algorithms, it is very difficult to solve some path planning problems containing certain constraint conditions due to the complex background environment. Based on the intensified ant colony optimizationalgorithm,

This paper presents an application of an Ant Colony Optimization (ACO) algorithm to optimize the parameters in the design of a type of nonlinear PID controller. The ACO algorithm is a novel heuristic bionic algorithm, which is based on the behaviour of real ants in nature searching for food. In order to optimize the parameters of the nonlinear PID controller

Nature-inspired optimizationalgorithms, notably evolutionary algorithms (EAs), have been widely used to solve various scientific and engineering problems because of to their simplicity and flexibility. Here we report a novel optimizationalgorithm, group search optimizer (GSO), which is inspired by animal behavior, especially animal searching behavior. The framework is mainly based on the producer-scrounger model, which assumes that group members

Considering the coding-excitation technology applied in ultrasonic systems, pre-decoding by multi-center before beam-synthesis is recognized as the best method for decoding. Compared with the method of decoding after synthesizing, the former avoids the inferior quality of side-lobe performance invited by beam-synthesis (the attenuation is more than 15dB). However, it is restricted by its great requirement to hardware cost resources so that pre-decoding method couldn't be made the most of in practice. In order to resolve the practical issue, this paper advances a set of project to retrench hardware cost by optimizing the decoding algorithm in theory. The resulting data based on Golay code with Quartus II validates the validity and feasibility of this project.

In this paper, we present a powerful hybrid genetic algorithm based around a heuristic timetabling framework. This combines a direct representation of the timetable with heuristic crossover operators to ensure that the most fundamental constraints are never violated. We explain how the population is seeded so as to produce a solution which cannot be outperformed by the heuristic method alone.

This paper proposes a novel method to detect fire and\\/or smoke in real-time by processing the video data generated by an ordinary camera monitoring a scene. The objective of this work is recognizing and modeling fire shape evolution in stochastic visual phenomenon. It focuses on detection of fire in image sequences by applying a new hybridalgorithm that depends on

We present a time synchronization scheme for wireless sensor networks that aims to conserve sensor battery power while maintaining network connectivity for as long as possible. The proposed method creates a hierarchical tree by flooding the sensor network from a designated source point. It then uses a hybridalgorithm derived from the Timing-sync protocol for sensor networks (TSPN) and the

This paper proposes a hybrid quantum-inspired genetic algorithm (HQGA) for the multiobjective flow shop scheduling problem (FSSP), which is a typical NP-hard combinatorial optimization problem with strong engineering backgrounds. On the one hand, a quantum-inspired GA (QGA) based on Q-bit representation is applied for exploration in the discrete 0-1 hyperspace by using the updating operator of quantum gate and genetic

This work is focused on evaluation and effectiveness comparison of two different heuristic algorithms in context of hybrid model used for optimization of the pressurized water distribution systems: genetic algorithm (GA) and harmony search methodology (HS). The optimization of the water distribution system is a complex problem which involves determining the commercial diameter for each pipe in the network while satisfying the water demand and pressure at each node (least-cost design task). The optimal design is in this formulation of the problem the lowest cost design out of numerous possibilities. Hybrid models present a further step in this optimization task, by elimination of some disadvantages in its standard formulation where are heuristic methods applied usually alone (extensive fine-tuning, very big search space, no guarantee for global optimum especially in big problems, etc). In the proposed and described hybrid method two substantially different algorithmic techniques are employed - linear programming (LP) and heuristic algorithm (genetic algorithms or harmony search in this work). Authors put together the contribution each of these algorithms to common task in which best possibilities of each other are employed and disadvantages are eliminated (LP is not suitable for looped networks and heuristic methods do not guarantee global optimum). The GA or HS method is used in the outer loop of the proposed algorithm, which is intended for decomposing a complex looped network to a group of possible branched networks. The mathematical models using LP are then automatically set up in an inner loop for each selected (by GA or HS) member of this group of branched networks for their optimization. After evaluating the high number of possible branch networks (by LP which is nested in a GA or HS objective function), an optimal solution could be found for the original looped network. The advantage of using this hybrid method consists in the fact that GA or HS in this case has a much smaller searching space than in a case when these heuristic methodologies are used alone. Models were tested on the benchmark networks with focusing on evaluation of the influence of heuristic algorithms on the obtained results, e.g. which from these two heuristic methods applied in hybrid models offer results closer to the global optimum. The performance of particular hybrid combination is evaluated by an application for the optimization of the Hanoi network and for the triple Hanoi water supply network. The first problem is taken from the literature. The second is introduced by the authors for the sake of evaluating the proposed method also on a bigger problem than the known and thoroughly investigated benchmark models are. It was investigated that both the method give results more reliable in the terms of closeness to a global minimum than any tested heuristic alone and hybrid alternative with harmony search methodology surpassed hybrid alternative with GA as its heuristic part. This work was supported by the Slovak Research and Development Agency under the contract No. LPP - 0319-09.

In this article a new method for yield optimization (design centring) is introduced. The method has a statistical-geometrical nature, hence it is called hybrid. The method exploits the semi-definite programming applications in approximating the feasible region with two bounding ellipsoids. These ellipsoids are obtained using a two phase algorithm. In the first phase, the minimum volume ellipsoid enclosing the feasible

In this article a new method for yield optimization (design centring) is introduced. The method has a statistical-geometrical nature, hence it is called hybrid. The method exploits the semi-definite programming applications in approximating the feasible region with two bounding ellipsoids. These ellipsoids are obtained using a two phase algorithm. In the first phase, the minimum volume ellipsoid enclosing the feasible

Hybrid vehicles offer larger flexibility than conventional powertrains and, therefore, opportunities for improved fuel economy, but they need systematic design and optimization procedures to realize that potential. Especially choosing the best system structure, parametrization, and supervisory control algorithms is not trivial. This paper presents a tool which supports these tasks and which is based on a somewhat unusual system description.

This paper presents the evolution of combinational logic circuits by a new hybridalgorithm 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 hybridalgorithm 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 hybridalgorithm 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

One of the goals of researches on molecular docking is to develop robust algorithms with powerful ability in global optimization because the primary purpose of molecular docking is to find out the most stable conformation of receptor–ligand complexes. Inspired by the principles of Hierarchical Fair Competition, we propose Tribe-PSO, a multi-layered and multi-phased hybrid particle swarm optimization model, in this

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.

An ant colony optimization (ACO) algorithm offers algorithmic techniques for optimization by simulating the foraging behavior of a group of ants to perform incremental solution constructions and to realize a pheromone laying-and-following mechanism. Although ACO is first designed for solving discrete (combinatorial) optimization problems, the ACO procedure is also applicable to continuous optimization. This paper presents a new way of

Xiao-Min Hu; Jun Zhang; Henry Shu-Hung Chung; Yun Li; Ou Liu

This paper presents a study on optimization of methanol synthesis reactor to enhance overall production. A mathematical heterogeneous model of the reactor was used for optimization of reactor performance, both at steady state and dynamic conditions. Here, genetic algorithms were used as powerful methods for optimization of complex problems. Initially, optimal temperature profile along the reactor was studied. Then, a

In this paper we present a scheduling algorithm that assigns tasks of medium size grain. The behavior of the proposed algorithm, called extended latency time (ELT), is compared with the dominant sequence clustering (DSC) algorithm. One of the inputs values required by the ELT algorithm is the maximum number of processors available in the architecture. This value corresponds to the

The family of evolutionary or genetic algorithms is used in various fields of bioinformatics. Genetic algorithms (GAs) can be used for simultaneous comparison of a large pool of DNA or protein sequences. This article explains how the GA is used in combination with other methods like the progressive multiple sequence alignment strategy to get an optimal multiple sequence alignment (MSA). Optimal MSA get much importance in the field of bioinformatics and some other related disciplines. Evolutionary algorithms evolve and improve their performance. In this optimisation, the initial pair-wise alignment is achieved through a progressive method and then a good objective function is used to select and align more alignments and profiles. Child and subpopulation initialisation is based upon changes in the probability of similarity or the distance matrix of the alignment population. In this genetic algorithm, optimisation of mutation, crossover and migration in the population of candidate solution reflect events of natural organic evolution. PMID:24084242

The application of genetic algorithms to integrated discrete and configuration optimization of trusses is presented. It is mathematically formulated as a constrained nonlinear optimization problem with a mix of discrete sizing and continuous configuration variables. The components of genetic algorithms are described. The discrete sizing variables are treated by constructing mapping relationships between binary digit strings and discrete values by

Evolutionary algorithms (EA) have proved to be well suited for optimization problemswith multiple objectives. Due to their inherent parallelism they are able tocapture a number of solutions concurrently in a single run. In this report, wepropose a new evolutionary approach to multicriteria optimization, the StrengthPareto Evolutionary Algorithm (SPEA). It combines various features of previousmultiobjective EAs in a unique manner and

Directional over current relays (DOR) are used to protection of interconnected networks and looped distribution systems. Several techniques and formulations have been proposed to solve the optimal coordination of DOR problem. In this paper, harmony search algorithm (HSA) is proposed for optimal coordination of DOR in a looped distribution system. Then this algorithm is developed to a new Improvised harmony

Mostafa Barzegari; S. M. T Bathaee; Mohsen Alizadeh

The Human-Inspired Algorithm (HIA) is a new algorithm that uses a given population (a group of candidate solutions) to improve the search for optimal solutions to continuous functions in different optimization applications such as non-linear programming. HIA imitates the intelligent search strategies of mountain climbers who use modern techniques (such as binoculars and cell phones) to effectively find the highest

Luna Mingyi Zhang; Cheyenne Dahlmann; Yanqing Zhang

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 optimizationalgorithm, simulated annealing, is effective at dealing with local minima but is very slow. Therefore, the algorithm developed in this paper is a hybridalgorithm 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.

The hybrid coherent anti-Stokes Raman scattering (CARS) has been successful applied to fast chemical sensitive detections. As the development of femto-second pulse shaping techniques, it is of great interest to find the optimum pulse shapes for CARS. The optimum pulse shapes should minimize the non-resonant four wave mixing (NRFWM) background and maximize the CARS signal. A genetic algorithm (GA) is developed to make a heuristic searching for optimized pulse shapes, which give the best signal the background ratio. The GA is shown to be able to rediscover the hybrid CARS scheme and find optimized pulse shapes for customized applications by itself.

In this work we introduce a novel formulation of the association problem in visual tracking systems as a discrete optimization problem. The full data association problem is formulated as a search for the best tracking configuration to match hypothesis. We have implemented three local search algorithms: Hill Climbing, Simulated Annealing, and Tabu Search algorithms. These algorithms are guided by heuristic

Miguel A. Patricio; Iván Dotú; Jesús García; Antonio Berlanga; José M. Molina

In this paper, a new evolutionary multiobjective optimizationalgorithm is proposed. The approach is based on a micro genetic algorithm (micro-GA) which is a genetic algorithm with a very small population (four individuals were used in our experiment) and a reinitialization process. We use three forms of elitism and a memory to generate the initial population of the micro-GA. Our

Carlos Coello Coello Coello; Gregorio Toscano Pulido

This paper describes two new harmony search (HS) meta-heuristic algorithms for engineering optimization problems with continuous design variables. The key difference between these algorithms and traditional (HS) method is in the way of adjusting bandwidth (bw). bw is very important factor for the high efficiency of the harmony search algorithms and can be potentially useful in adjusting convergence rate of

While heuristic optimization is applied in environmental applications, ad-hoc algorithm configuration is typical. We use a multi-layer sorptive barrier design problem as a benchmark for an algorithm-tuning procedure, as applied to three heuristics (genetic algorithms, simulated ...

Recent advances in evolutionary algorithms show that coevolutionary architectures are effective ways to broaden the use of traditional evolutionary algorithms. This paper presents a cooperative coevolutionary algorithm (CCEA) for multiobjective optimization, which applies the divide-and-conquer approach to decompose decision vectors into smaller components and evolves multiple solutions in the form of cooperative subpopulations. Incorporated with various features like archiving, dynamic

Traditional analytical methods for traffic information can't meet to need of intelligent traffic system. Mining value-add information can deal with more traffic problems. The paper exploits a new clustering optimizationalgorithm to extract useful spatial clustered pattern for predicting long-term traffic flow from macroscopic view. Considering the sensitivity of initial parameters and easy falling into local extreme in FCM algorithm, the new algorithm applies Particle Swarm Optimization method, which can discovery the globe optimal result, to the FCM algorithm. And the algorithm exploits the union of the clustering validity index and objective function of the FCM algorithm as the fitness function of the PSO algorithm. The experimental result indicates that it is effective and efficient. For fuzzy clustering of road traffic data, it can produce useful spatial clustered pattern. And the clustered centers represent the locations which have heavy traffic flow. Moreover, the parameters of the patterns can provide intelligent traffic system with assistant decision support.

Hu, Chunchun; Shi, Wenzhong; Meng, Lingkui; Liu, Min

A limitation in traditional stepwise population pharmacokinetic model building is the difficulty in handling interactions between model components. To address this issue, a method was previously introduced which couples NONMEM parameter estimation and model fitness evaluation to a single-objective, hybrid genetic algorithm for global optimization of the model structure. In this study, the generalizability of this approach for pharmacokinetic model building is evaluated by comparing (1) correct and spurious covariate relationships in a simulated dataset resulting from automated stepwise covariate modeling, Lasso methods, and single-objective hybrid genetic algorithm approaches to covariate identification and (2) information criteria values, model structures, convergence, and model parameter values resulting from manual stepwise versus single-objective, hybrid genetic algorithm approaches to model building for seven compounds. Both manual stepwise and single-objective, hybrid genetic algorithm approaches to model building were applied, blinded to the results of the other approach, for selection of the compartment structure as well as inclusion and model form of inter-individual and inter-occasion variability, residual error, and covariates from a common set of model options. For the simulated dataset, stepwise covariate modeling identified three of four true covariates and two spurious covariates; Lasso identified two of four true and 0 spurious covariates; and the single-objective, hybrid genetic algorithm identified three of four true covariates and one spurious covariate. For the clinical datasets, the Akaike information criterion was a median of 22.3 points lower (range of 470.5 point decrease to 0.1 point decrease) for the best single-objective hybrid genetic-algorithm candidate model versus the final manual stepwise model: the Akaike information criterion was lower by greater than 10 points for four compounds and differed by less than 10 points for three compounds. The root mean squared error and absolute mean prediction error of the best single-objective hybrid genetic algorithm candidates were a median of 0.2 points higher (range of 38.9 point decrease to 27.3 point increase) and 0.02 points lower (range of 0.98 point decrease to 0.74 point increase), respectively, than that of the final stepwise models. In addition, the best single-objective, hybrid genetic algorithm candidate models had successful convergence and covariance steps for each compound, used the same compartment structure as the manual stepwise approach for 6 of 7 (86 %) compounds, and identified 54 % (7 of 13) of covariates included by the manual stepwise approach and 16 covariate relationships not included by manual stepwise models. The model parameter values between the final manual stepwise and best single-objective, hybrid genetic algorithm models differed by a median of 26.7 % (q? = 4.9 % and q? = 57.1 %). Finally, the single-objective, hybrid genetic algorithm approach was able to identify models capable of estimating absorption rate parameters for four compounds that the manual stepwise approach did not identify. The single-objective, hybrid genetic algorithm represents a general pharmacokinetic model building methodology whose ability to rapidly search the feasible solution space leads to nearly equivalent or superior model fits to pharmacokinetic data. PMID:22767341

Sherer, Eric A; Sale, Mark E; Pollock, Bruce G; Belani, Chandra P; Egorin, Merrill J; Ivy, Percy S; Lieberman, Jeffrey A; Manuck, Stephen B; Marder, Stephen R; Muldoon, Matthew F; Scher, Howard I; Solit, David B; Bies, Robert R

The redundancy optimization problem is a well known NP-hard problem which involves the selection of elements and redundancy levels to maximize system performance, given different system-level constraints. This article presents an efficient algorithm based on the harmony search algorithm (HSA) to solve this optimization problem. The HSA is a new nature-inspired algorithm which mimics the improvization process of music players.

Estimation of Distribution Algorithms have been proposed as a new paradigm\\u000afor evolutionary optimization. This paper focuses on the parallelization of\\u000aEstimation of Distribution Algorithms. More specifically, the paper discusses\\u000ahow to predict performance of parallel Mixed Bayesian OptimizationAlgorithm\\u000a(MBOA) that is based on parallel construction of Bayesian networks with\\u000adecision trees. We determine the time complexity of parallel

We substantially improve the known algorithms for approximating all the complex zeros of an nth degree polynomial p(x). Our new algorithms save both Boolean and arithmetic sequential time, versus the previous best algorithms of Schönhage [1], Pan [2], and Neff and Reif [3]. In parallel (NC) implementation, we dramatically decrease the number of processors, versus the parallel algorithm of Neff

This paper presents the modeling and design of an optimal Energy Management Strategy (EMS) for a flywheel-based hybrid vehicle, that does not use any electrical motor\\/generator, or a battery, for its hybrid functionalities. The hybrid drive train consists of only low-cost components, such as a flywheel module and a continuously variable transmission. This hybrid drive train is characterized by a

Koos van Berkel; Theo Hofman; Bas Vroemen; Maarten Steinbuch

Effective abnormality detection and diagnosis in Magnetic Resonance Images (MRIs) requires a robust segmentation strategy. Since manual segmentation is a time-consuming task which engages valuable human resources, automatic MRI segmentations received an enormous amount of attention. For this goal, various techniques have been applied. However, Markov Random Field (MRF) based algorithms have produced reasonable results in noisy images compared to other methods. MRF seeks a label field which minimizes an energy function. The traditional minimization method, simulated annealing (SA), uses Monte Carlo simulation to access the minimum solution with heavy computation burden. For this reason, MRFs are rarely used in real time processing environments. This paper proposed a novel method based on MRF and a hybrid of social algorithms that contain an ant colony optimization (ACO) and a Gossiping algorithm which can be used for segmenting single and multispectral MRIs in real time environments. Combining ACO with the Gossiping algorithm helps find the better path using neighborhood information. Therefore, this interaction causes the algorithm to converge to an optimum solution faster. Several experiments on phantom and real images were performed. Results indicate that the proposed algorithm outperforms the traditional MRF and hybrid of MRF-ACO in speed and accuracy. PMID:22377656

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.

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

Marriage in honey bees optimization (MBO) is a metaheuristic optimizationalgorithm 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 optimizationalgorithms. PMID:23935416

To reduce the noise amplification and ripple phenomenon in the restoration result by using the traditional Richardson-Lucy deconvolution method, a novel hybrid regularization image restoration algorithm based on total variation is proposed in this paper. The key ides is that the hybrid regularization terms are employed according to the characteristics of different regions in the image itself. At the same time, the threshold between the different regularization terms is selected according to the golden section point which takes into account the human eye's visual feeling. Experimental results show that the restoration results of the proposed method are better than that of the total variation Richardson-Lucy algorithm both in PSNR and MSE, and it has the better visual effect simultaneously.

This paper deals with the optimization of the instantaneous electrical generation\\/electrical storage power split in series hybrid electric vehicles (SHEV). Optimal energy management is related to the optimization of the instantaneous generation\\/storage power split in SHEV. Previously, a power split type solution of the series hybrid energy management problem has been attempted using a rule-based approach. Our approach performs a

Based on the approach of minimization of the cost function, this paper brings forward a kind of hybrid intelligent algorithm for image edge detection, which is based on the particle swarm and quanta evolution algorithm. The implementation of the hybrid intelligent algorithm is also discussed to do the edge detection. The simulation results show that the new algorithm has obtained the satisfied detection effect.

\\u000a In this paper we discuss a hybrid feature selection algorithm for the Quantitative Structure Activity Relationship (QSAR)\\u000a modelling. This is one of the goals in Predictive Toxicology domain, aiming to describe the relations between the chemical\\u000a structure of a molecule and its biological or toxicological effects, in order to predict the behaviour of new, unknown chemical\\u000a compounds. We propose a

Marian Viorel Craciun; Adina Cocu; Luminita Dumitriu; Cristina Segal

\\u000a For multiple sequence alignment problem in molecular biological sequence analysis, a hybrid genetic algorithm and an associated\\u000a software package called HGA-COFFEE are presented. The COFFEE function is used to measure individual fitness, and five novel\\u000a genetic operators are designed, a selection operator, two crossover operators and two mutation operators. One of the mutation\\u000a operators is designed based on the COFFEE’s

Many evolutionary computation methods have been proposed and applied to real world problems. But gradient methods are still effective in problems involving real-coded parameters. In addition, it is desirable to find not only an optimal solution but also plural optimal and semi-optimal solutions in most real world problems. Although a hybridalgorithm combining Immune Algorithm (IA) and Quasi-Newton method (QN) has been proposed for multiple solution search, its memory cell control sometimes fails to keep semi-optimal solutions whose evaluation value is not so high. In addition, because the hybridalgorithm applies QN only to memory cell candidates, QN can be used as local search operator only after global search by IA. This paper proposes an improved memory cell control which restricts existence of redundant memory cells, and a QN application method which uses QN even in early search stage. Experimental results have shown that the hybridalgorithm involving the proposed improvements can find optimal and semi-optimal solutions with high accuracy and efficiency even in high-dimensional multimodal functions involving epistasis.

A hybrid feature selection method called SU-GA-W is proposed to make full use of advantages of filter and wrapper methods. This method falls into two phases. The filter phase removes features with lower SU and guides the initialization of GA population; the wrapper phase searches the final feature subset. The effectiveness of this algorithm is demonstrated on various data sets.

This paper aims at developing an Automatic Music Genre Classification system and focuses on calculating algorithms that (ideally) can predict the music class in which a music file belongs. The proposed system is based on techniques from the fields of Signal Processing, Pattern Recognition, and Information Retrieval, as well as Heuristic Optimization Methods. One thousand music files are used for

Existing artificial immune optimizationalgorithms reflect a number of shortcomings, such as premature convergence and poor local search ability. This paper proposes a danger-theory-based immune network optimizationalgorithm, named dt-aiNet. The danger theory emphasizes that danger signals generated from changes of environments will guide different levels of immune responses, and the areas around danger signals are called danger zones. By defining the danger zone to calculate danger signals for each antibody, the algorithm adjusts antibodies' concentrations through its own danger signals and then triggers immune responses of self-regulation. So the population diversity can be maintained. Experimental results show that the algorithm has more advantages in the solution quality and diversity of the population. Compared with influential optimizationalgorithms, CLONALG, opt-aiNet, and dopt-aiNet, the algorithm has smaller error values and higher success rates and can find solutions to meet the accuracies within the specified function evaluation times.

A new approach based on ant algorithm (AA) and particle swarm optimization (PSO) algorithm is proposed for Multi-chip Module (MCM) interconnect test generation in this paper. Using the pheromone-updating rule and state transition rule, AA generates the initial candidate test vectors. PSO is employed to evolve the candidates generated by AA. The optimized search is guided by the swarm intelligent

The load carrying capacity, of externally pressurised and optimally shaped metallic shell, has been increased by 40% over the performance of an equivalent cylinder. The optimal geometry has been sought within a class of generalised ellipses by the application of simulated annealing algorithm.The optimal solution has been verified experimentally by collapsing two, nominally identical, CNC-machined, mild steel shells at about

Optimization for induction motor design is one of the interested subjects by electrical engineers. This paper proposes an Improved Genetic Algorithm (IGA) for optimization of 3-phase induction motor design. The proposed IGA possesses the characteristics of real number encoding, stochastic crossover operator, self-adaptable mutation operator and annealing penalty function, and multi- turns evolution strategy for solving nonlinear constrained multivariable optimization

Discusses a modification of the Newton algorithm applied to nonholonomic motion planning with energy optimization. The energy optimization is performed either by optimizing motion in the space of the Jacobian matrix derived from the nonholonomic system or coupling this motion with movement toward the goal. Resulting controls are smooth and easily generated by motors or thrusters. The two methods can

This study introduces a hybrid multi- objective evolutionary algorithm (MOEA) for the optimization of aircraft control system design. The strategy suggested here is composed mainly of two stages. The first stage consists of training an Artificial Neural Network (ANN) with objective values as inputs and decision variables as outputs to model an approximation of the inverse of the objective function

Salem F. Adra; Ahmed I. Hamody; Ian Griffin; Peter J. Fleming

Many exact algorithms for NP-hard graph problems adopt the old Davis-Putman branch-and-reduce paradigm. The performance of these algorithms often suffers from the increasing number of graph modifications, such as deletions, that reduce the problem instance and have to be "taken back" frequently during the search process. The use of efficient data structures is necessary for fast graph modification modules as well as fast take-back procedures. In this paper, we investigate practical implementation-based aspects of exact algorithms by providing a hybrid graph representation that addresses the take-back challenge and combines the advantage of {O}(1) adjacency-queries in adjacency-matrices with the advantage of efficient neighborhood traversal in adjacency-lists.

Abu-Khzam, Faisal N.; Langston, Michael A.; Mouawad, Amer E.; Nolan, Clinton P.

A new hybrid technique for optimization of a multivariable function is proposed. This method is applied to the problem of complex time Green's function of multilayer media. This technique combines Particle Swarm search algorithm with the gradient based quasi-Newton method. Superiority of the method is demonstrated by comparing its results with other optimization techniques.

Mohsen Ghaffari-Miab; Amin Farmahini-Farahani; Reza Faraji-Dana; Caro Lucas

Control strategy plays as significant a role as component sizing in achieving optimum fuel economy of a fuel cell hybrid vehicle (FCHV). In this paper, the performance optimization of FCHVs is formulated as a combined control\\/plant optimization problem. Inspired by stochastic dynamic programming results, a parameterizable and near-optimal controller is proposed. The proposed controller enables us to formulate and solve

Active control of fixed wing aircraft using piezoelectric materials has the potential to improve its aeroelastic response while reducing weight penalties. However, the design of active aircraft wings is a complex optimization problem requiring the use of formal optimization techniques. In this paper, a hybridoptimization procedure is applied to the design of a scaled airplane wing model, represented by

Aditi Chattopadhyay; Charles E. Seeley; Ratneshwar Jha

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.

This paper presents the comparison results on the performance of the Artificial Bee Colony (ABC) algorithm for constrained\\u000a optimization problems. The ABC algorithm has been firstly proposed for unconstrained optimization problems and showed that\\u000a it has superior performance on these kind of problems. In this paper, the ABC algorithm has been extended for solving constrained\\u000a optimization problems and applied to

In this contribution we deal with a multi-criteria shape optimization of an induction furnace. We want to find shape parameters of the furnace in such a way, that two different criteria are optimized. Since they cannot be optimized simultaneously, instead of one optimum we find set of partially optimal designs, so called Pareto front. We compare two different approaches to the optimization, one using nonlinear conjugate gradient method and second using variation of genetic algorithm. As can be seen from the numerical results, genetic algorithm seems to be the right choice for this problem. Solution of direct problem (coupled problem consisting of magnetic and heat field) is done using our own code Agros2D. It uses finite elements of higher order leading to fast and accurate solution of relatively complicated coupled problem. It also provides advanced scripting support, allowing us to prepare parametric model of the furnace and simply incorporate various types of optimizationalgorithms.

Ku?s, Pavel; Mach, František; Karban, Pavel; Doležel, Ivo

As XML (eXtensible Mark-up Language) is gaining its popularity in data exchange over the Web, querying XML data has become an important issue to be addressed. In native XML databases (NXD), XML documents are usually modeled as trees and XML queries are typically specified in path expression. The primitive structural relationships are Parent-Child (P-C), Ancestor-Descendant (A-D), sibling and ordered query. Thus, a suitable and compact labeling scheme is crucial to identify these relationships and henceforth to process the query efficiently. We propose a novel labeling scheme consisting of < self-level:parent> to support all these relationships efficiently. Besides, we adopt the decomposition-matching-merging approach for structural query processing and propose a hybrid query optimization technique, TwigINLAB to process and optimize the twig query evaluation. Experimental results indicate that TwigINLAB can process all types of XML queries 15% better than the TwigStack algorithm in terms of execution time in most test cases.

Assortment optimization problems intend to seek the best way of placing a given set of rectangles within a minimum-area rectangle. Such problems are often formulated as a quadratic mixed 0–1 program. Many current methods for assortment problems are either unable to find an optimal solution or being computationally inefficient for reaching an optimal solution. This paper proposes a new method

Many real-world optimization problems are subjected to uncertainties that may be characterized by the presence of noise in the objective functions. The estimation of distribution algorithm (EDA), which models the global distribution of the population for searching tasks, is one of the evolutionary computation techniques that deals with noisy information. This paper studies the potential of EDAs; particularly an EDA based on restricted Boltzmann machines that handles multi-objective optimization problems in a noisy environment. Noise is introduced to the objective functions in the form of a Gaussian distribution. In order to reduce the detrimental effect of noise, a likelihood correction feature is proposed to tune the marginal probability distribution of each decision variable. The EDA is subsequently hybridized with a particle swarm optimizationalgorithm in a discrete domain to improve its search ability. The effectiveness of the proposed algorithm is examined via eight benchmark instances with different characteristics and shapes of the Pareto optimal front. The scalability, hybridization, and computational time are rigorously studied. Comparative studies show that the proposed approach outperforms other state of the art algorithms. PMID:22264074

Shim, Vui Ann; Tan, Kay Chen; Chia, Jun Yong; Al Mamun, Abdullah

Finite element analysis is used to compute the current distribution of the human cochlea during cochlear implant electrical stimulation. Genetic algorithms are then applied in conjunction with the finite element analysis to optimize the shape of cochlear ...

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 configura...

The ant colony optimization (ACO) algorithm is a fast suboptimal meta-heuristic based on the behavior of a set of ants that\\u000a communicate through the deposit of pheromone. It involves a node choice probability which is a function of pheromone strength\\u000a and inter-node distance to construct a path through a node-arc graph. The algorithm allows fast near optimal solutions to\\u000a be

Most engineering optimizationalgorithms are based on numerical linear and nonlinear programming methods that require substantial\\u000a gradient information and usually seek to improve the solution in the neighborhood of a starting point. These algorithms, however,\\u000a reveal a limited approach to complicated real-world optimization problems. If there is more than one local optimum in the\\u000a problem, the result may depend on

In this paper an optimal method based on neuro-fuzzy for controlling parallel hybrid electric vehicles is presented. In parallel hybrid electric vehicles the required torque for driving and operating the onboard accessories is generated by a combination of internal combustion engine and an electric motor. The power sharing between the internal combustion engine and the electric motor is the key

In this work we present optimizations of a grid-based projector-augmented wave method software, GPAW for the Blue Gene\\/P architecture. The improvements are achieved by exploring the advantage of shared and distributed memory programming also known as hybrid programming. The work focuses on optimizing a very time consuming operation in GPAW, the finite-different stencil operation, and different hybrid programming approaches are

Mads Ruben Burgdorff Kristensen; Hans Henrik Happe; Brian Vinter

We describe a primal-dual framework for the design and analysis of online strongly convex optimizationalgorithms. Our framework yields the tightest known logarithmic regret bounds for Follow-The-Leader and for the gradient de- scent algorithm proposed in Hazan et al. (2006). We then show that one can inter- polate between these two extreme cases. In particular, we derive a new algorithm

The paper describes a new algorithm of optimal control system design for a linear, time-invariant, single-input single-output plant. The proposed algorithm has low computational complexity, high stability and possesses the clear physical sense of the existence conditions of the design problem solution. The reference (command) signal and disturbances can include random and regular components. The algorithm is based on the

Traveling salesman problem (TSP) is a typical NP-complete problem, of which the search space increases with the number of cities. Genetic algorithm (GA) is an efficient optimizationalgorithm characterized with explicit parallelism and robustness, applicable to TSP. In this paper, we compare the performance of the existing GAs in searching the solution for TSP and find a superior combination of

In this paper, we propose a micro genetic algorithm with three forms of elitism for multiobjective optimization. We show how this relatively simple algorithm coupled with an external file and a diversity approach based on geographical distribution can generate efficiently the Pareto fronts of several difficult test functions (both constrained and unconstrained). A metric based on the average distance to

Due to increasing interest in solving real-world optimization problems using evolutionary algorithms (EAs), researchers have recently developed a number of real-parameter genetic algorithms (GAs). In these studies, the main research effort is spent on developing an efficient recombination operator. Such recombination operators use probability distributions around the parent solutions to create an offspring. Some operators emphasize solutions at the center

A genetic algorithm technique is developed for the optimal design of a supplemental lighting system for greenhouse crop production. The approach uses the evolutionary parallel search capabilities of genetic algorithms to design the pattern layout of the lamps (luminaires), their mounting heights and their wattages. The total number and the exact positions of luminaires are not predefined (even though possible

Evolutionary algorithm (EA) has become popular in global optimization with applications widely used in many industrial areas. However, there exists probable premature convergence problem when rugged contour situation is encountered. As to the original genetic algorithm (GA), no matter single population or multi-population cases, the ways to prevent the problem of probable premature convergence are to implement various selection methods,

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

Marco Antonio Montes de Oca; Thomas Stützle; Mauro Birattari; Marco Dorigo

Proposes a recurrent learning algorithm for designing the controllers of continuous dynamical systems in optimal control problems. The controllers are in the form of unfolded recurrent neural nets embedded with physical laws from classical control techniques. The learning algorithm is characterized by a double forward-recurrent-loops structure for solving both temporal recurrent and structure recurrent problems. The first problem results from

A novel method of optimizing the continuous variable constitutive relation of multi-layered chiral slab based on genetic algorithm was discussed in this paper. This method derived continuous variable constitutive relations automatically, then calculated the reflection coefficient by wave-splitting method. Furthermore the genetic algorithm was used to search the maximum absorbability of the chiral slab with certain thickness

Y. C. Gao; Jian Hua Xu; De Shuang Zhao; Shu Zhang Liu

In this paper we present an algorithm for a special case of wire routing. Given a rectangular circuit component on a planar surface with terminals around its boundary, the algorithm finds an optimal set of paths in the plane connecting specified pairs of terminals. The paths are restricted to lie on the outside of the component and must consist of

In this paper, we propose a new optimization technique by modifying a chaos optimizationalgorithm (COA) based on the fractal theory. We first implement the weighted gradient direction-based chaos optimization in which the chaotic property is used to determine the initial choice of the optimization parameters both in the starting step and in the mutations applied when a convergence to local minima occurred. The algorithm is then improved by introducing a method to determine the optimal step size. This method is based on the fact that the sensitive dependence on the initial condition of a root finding technique (such as the Newton-Raphson search technique) has a fractal nature. From all roots (step sizes) found by the implemented technique, the one that most minimizes the cost function is employed in each iteration. Numerical simulation results are presented to evaluate the performance of the proposed algorithm.

The connections between optimization and control theory have been explored by many researchers and optimizationalgorithms have been applied with success to optimal control. The rapid pace of developments in model predictive control has given rise to a host of new problems to which optimization has yet to be applied. Concurrently, developments in optimization, and especially in interior-point methods, have produced a new set of algorithms that may be especially helpful in this context. In this paper, we reexamine the relatively simple problem of control of linear processes subject to quadratic objectives and general linear constraints. We show how new algorithms for quadratic programming can be applied efficiently to this problem. The approach extends to several more general problems in straightforward ways.

This article presents a dynamic programming-based particle swarm optimization (DP-based PSO) algorithm for solving an inventory management problem for large-scale construction projects under a fuzzy random environment. By taking into account the purchasing behaviour and strategy under rules of international bidding, a multi-objective fuzzy random dynamic programming model is constructed. To deal with the uncertainties, a hybrid crisp approach is

Clustering is inherently a difficult problem, both with respect to the construction of adequate objective functions as well as to the optimization of the objective functions. In this paper, we suggest an objective function called the Weighted Sum Validity Function (WSVF), which is a weighted sum of the several normalized cluster validity functions. Further, we propose a Hybrid Niching Genetic Algorithm (HNGA), which can be used for the optimization of the WSVF to automatically evolve the proper number of clusters as well as appropriate partitioning of the data set. Within the HNGA, a niching method is developed to preserve both the diversity of the population with respect to the number of clusters encoded in the individuals and the diversity of the subpopulation with the same number of clusters during the search. In addition, we hybridize the niching method with the k-means algorithm. In the experiments, we show the effectiveness of both the HNGA and the WSVF. In comparison with other related genetic clustering algorithms, the HNGA can consistently and efficiently converge to the best known optimum corresponding to the given data in concurrence with the convergence result. The WSVF is found generally able to improve the confidence of clustering solutions and achieve more accurate and robust results. PMID:16366242

The design of efficient algorithms for large-scale gas dynamics computations with hybrid (heterogeneous) computing systems whose high performance relies on massively parallel accelerators is addressed. A high-order accurate finite volume algorithm with polynomial reconstruction on unstructured hybrid meshes is used to compute compressible gas flows in domains of complex geometry. The basic operations of the algorithm are implemented in detail for massively parallel accelerators, including AMD and NVIDIA graphics processing units (GPUs). Major optimization approaches and a computation transfer technique are covered. The underlying programming tool is the Open Computing Language (OpenCL) standard, which performs on accelerators of various architectures, both existing and emerging.

In this study an unsupervised way of fire pixel detection from video frames is depicted. A hybrid clustering algorithm is proposed, depending on color samples in video frames. A modified k-mean clustering algorithm is used here. In this algorithm hierarchical and partition clustering are used to build the hybrid. The results are analyzed with color base threshold method by considering

To solve complex global optimization problems, Artificial Physics Optimization (APO) algorithm is presented based on Physicomimetics framework, which is a population-based stochastic algorithm inspired by physical force. The solutions (particles) sampled from the feasible region of the problems are treated as physical individuals. Each individual has a mass, position and velocity. The mass of each individual corresponds to a user-defined

Optimal distillation sequencing is a method for obtaining the best structure of multicomponent separation processes. Due to the significant contribution of the distillation sequences to the capital and operating costs for the whole chemical process, the development of a systematic framework that will select the optimal distillation sequences becomes an important research issue. Since distillation sequencing is a combinatorial problem,

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.

Talatahari, S.; Farahmand Azar, B.; Sheikholeslami, R.; Gandomi, A. H.

To solve the multi-objective problems, a novel hybrid particle swarm optimizationalgorithm is proposed(called HPSODE). The new algorithm includes three major improvement: (I)Population initialization is constructed by statistical method Uniform Design, (II)Regeneration method has two phases: the first phase is particles updated by adaptive PSO model with constriction factor ?, the second phase is Differential Evolution operator with archive, (III)A new accept rule called Distance/volume fitness is designed to update archive. Experiment on ZDTx and DTLZx problems by jMetal 2.1, the results show that the new hybridalgorithm significant outperforms OMOPSO, SMPSO in terms of additive Epsilon, HyperVolume, Genetic Distance, Inverted Genetic Distance.

Optimal power flow (OPF) problem has already been attempted as a static optimization problem, by adopting conventional gradient-based methods and more recently, nonconventional ones, such as evolutionary algorithms. However, as the loads, generation capacities and network connections in a power system are always in a changing status, these static-oriented methods are of limited use for this issue. This paper presents

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

Funnel-and-gate systems (FGSs), which constitute a common variant of permeable reactive barriers used for in situ treatment of groundwater, pose particular challenges to the task of design optimization. Because of the complex interplay of funnels and gates, the evolutionary algorithms applied have to cope with multimodality, nonseparability, and nonlinearity of the optimization task. We analyze these features in a test

|Ant colony optimization is a recently proposed heuristic procedure inspired by the behavior of real ants. This article applies the procedure to model specification searches in structural equation modeling and reports the results. The results demonstrate the capabilities of ant colony optimizationalgorithms for conducting automated searches.|

Although the genetic algorithm (GA) is powerful and not limited by restrictive assumptions about search space of optimization problems, it has a convergency problem when it is used to find the optimum point with high accuracy. The object of developing an intelligent GA is to improve this weakness. In this paper, the optimization procedures of an intelligent GA are investigated,

This paper introduces the Mesh Adaptive Direct Search (MADS) class of algorithms for nonlinear optimization. MADS extends the Generalized Pattern Search (GPS) class by allowing local exploration, called polling, in an asymptotically dense set of directions in the space of optimization variables. This means that under certain hypotheses, including a weak constraint qualification due to Rockafellar, MADS can treat constraints

Optimal power flow (OPF) is one of the main functions of power generation operation and control. It determines the optimal setting of generating units. It is therefore of great importance to solve this problem as quickly and accurately as possible. This paper presents the solution of the OPF using genetic algorithm technique. This paper proposes a new methodology for solving

Dimensional and form accuracy of a workpiece are influenced by the fixture layout selected for the machining operation. Hence, optimization of fixture layout is a critical aspect of machining fixture design. This paper presents a fixture layout optimization technique that uses the genetic algorithm (GA) to find the fixture layout that minimizes the deformation of the machined surface due to

Based on Markowitz' theory of asset portfolio, a multiple-goal optimization model of portfolio investment was set up considering both risk and return. Then applying ant colony optimizationalgorithm to solve the model, we got a better result than that of using Lingo.

A natural optimization model that formulates many online resource allocation and revenue management problems is the online linear program (LP) where the constraint matrix is revealed column by column along with the objective function. We provide a near-optimalalgorithm for this surprisingly general class of online problems under the assumption of random order of arrival and some mild conditions on

This paper proposes an algorithm using multi-objective Particle Swarm Optimization (MOPSO) for finding robust solutions against small perturbations of design variables. If an optimal solution is sensitive to small perturbations of variables, it may be inappropriate or risky for practical use. Robust optimization finds solutions which are moderately good in terms of optimality and also good in terms of robustness

In this talk, we explore the benefits of hybridoptimization using parallel versions of DIRECT and asynchronous generating set search (GSS) for optimization. Both of these methods are derivative-free, making them useful for a variety of science and engineering problems. Our goal is to ideally find a global minimum, but practically to find a good local minimum in a small

This chapter introduces two Perl programs that implement graphical tools for exploring the performance of stochastic local search algorithms for biobjective optimization problems. These tools are based on the concept of the empirical attainment function (EAF), which describes the probabilistic distribution of the outcomes obtained by a stochastic algorithm in the objective space. In particular, we consider the visualization of attainment surfaces and differences between the first-order EAFs of the outcomes of two algorithms. This visualization allows us to identify certain algorithmic behaviors in a graphical way. We explain the use of these visualization tools and illustrate them with examples arising from practice.

López-Ibáñez, Manuel; Paquete, Luís; Stützle, Thomas

The ideas of discrete Lagrangian methods for conservative systems are exploited for the construction of algorithms applicable in optimal ship routing problems. The algorithm presented here is based on the discretisation of Hamilton's principle of stationary action Lagrangian and specifically on the direct discretization of the Lagrange-Hamilton principle for a conservative system. Since, in contrast to the differential equations, the discrete Euler-Lagrange equations serve as constrains for the optimization of a given cost functional, in the present work we utilize this feature in order to minimize the cost function for optimal ship routing.

Kosmas, O. T.; Vlachos, D. S.; Simos, T. E. [University of Peloponnese, 22100 Tripoli (Greece)

This paper presents design optimization of an axial fan blade with hybrid multi-objective evolutionary algorithm (hybrid MOEA). Reynolds-averaged Navier-Stokes equations with shear stress transport turbulence model are discretized by the finite volume approximations and solved on hexahedral grids for the flow analyses. The validation of the numerical results was performed with the experimental data for the axial and tangential velocities. Six design variables related to the blade lean angle and blade profile are selected and the Latin hypercube sampling of design of experiments is used to generate design points within the selected design space. Two objective functions namely total efficiency and torque are employed and the multi-objective optimization is carried out to enhance total efficiency and to reduce the torque. The flow analyses are performed numerically at the designed points to obtain values of the objective functions. The Non-dominated Sorting of Genetic Algorithm (NSGA-II) with ? -constraint strategy for local search coupled with surrogate model is used for multi-objective optimization. The Pareto-optimal solutions are presented and trade-off analysis is performed between the two competing objectives in view of the design and flow constraints. It is observed that total efficiency is enhanced and torque is decreased as compared to the reference design by the process of multi-objective optimization. The Pareto-optimal solutions are analyzed to understand the mechanism of the improvement in the total efficiency and reduction in torque.

Kim, Jin-Hyuk; Choi, Jae-Ho; Husain, Afzal; Kim, Kwang-Yong

We study the distributed averaging problem on an arbitrary network with a gossip constraint, which means that no node communicates with more than one neighbour in every time slot. We consider algorithms which are linear iterations, where each iteration is described by a random matrix picked i.i.d. from some distribution. We derive conditions that this distribution must satisfy so that

Stephen Boyd; Arpita Ghosh; Balaji Prabhakar; Shah Devavrat

Let A be a Las Vegas algorithm, i.e., A is a randomized algorithmthat always produces the correct answer when it stops but whose runningtime is a random variable. We consider the problem of minimizingthe expected time required to obtain an answer from A using strategieswhich simulate A as follows: run A for a fixed amount of timet 1 , then

This paper describes the optimization of conductor size and the voltage regulator location and magnitude of long rural distribution lines. The optimization minimizes the lifetime cost of the lines, including capital costs and losses while observing voltage drop and operational constraints using a Genetic Algorithm (GA). The GA optimization is applied to a real Single Wire Earth Return (SWER) network in regional Queensland and results are presented.

Wishart, Michael; Ledwich, Gerard; Ghosh, Arindam [Queensland University of Technology, Brisbane, Queensland (Australia); Ivanovich, Grujica [Ergon Energy, Toowoomba, Queensland (Australia)

Chromosome design has been shown to be a crucial element in developing genetic algorithms which approach global solutions without premature convergence. The consecutive positioning of parameters with high-correlations and relevance enhances the creation of genetic building blocks which are likely to persist across recombination to provide genetic inheritance. Incorporating positional gene relevance is challenging, however, in multi-dimensional design problems. We present a hybrid chromosome designed for optimizing a fragmented patch antenna which combines linear and two-dimensional gene representations. We compare previous results obtained with a linear chromosome to solutions obtained with this new hybrid representation.

Numerous detection algorithms, using various sensor modalities, have been developed for the detection of mines in cluttered and noisy backgrounds. The performance for each detection algorithm is typically reported in terms of the Receiver Operating Characteristic (ROC), which is a plot of the probability of detection versus false alarm as a function of the threshold setting on the output decision variable of each algorithm. In this paper we present multi-sensor decision fusion algorithms that combine the local decisions of existing detection algorithms for different sensors. This offers, in certain situations, an expedient, attractive and much simpler alternative to "starting over" with the redesign of a new algorithm which fuses multiple sensors at the data level. The goal in our multi-sensor decision fusion approach is to exploit complimentary strengths of existing multi-sensor algorithms so as to achieve performance (ROC) that exceeds the performance of any sensor algorithm operating in isolation. Our approach to multi-sensor decision fusion is based on optimal signal detection theory, using the likelihood ratio. We consider the optimal fusion of local decisions for two sensors, GPR (ground penetrating radar) and MD (metal detector). A new robust algorithm for decision fusion is presented that addresses the problem that the statistics of the training data is not likely to exactly match the statistics of the test data. ROC's are presented and compared for real data.

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.

Non-linear inverse problems in the geosciences often involve probabilistic sampling of multimodal density functions or global optimization and sometimes both. Efficient algorithmic tools for carrying out sampling or optimization in challenging cases are of major interest. Here results are presented of some numerical experiments with a technique, known as Parallel Tempering, which originated in the field of computational statistics but is finding increasing numbers of applications in fields ranging from Chemical Physics to Astronomy. To date, experience in use of Parallel Tempering within earth sciences problems is very limited. In this paper, we describe Parallel Tempering and compare it to related methods of Simulated Annealing and Simulated Tempering for optimization and sampling, respectively. A key feature of Parallel Tempering is that it satisfies the detailed balance condition required for convergence of Markov chain Monte Carlo (McMC) algorithms while improving the efficiency of probabilistic sampling. Numerical results are presented on use of Parallel Tempering for trans-dimensional inversion of synthetic seismic receiver functions and also the simultaneous fitting of multiple receiver functions using global optimization. These suggest that its use can significantly accelerate sampling algorithms and improve exploration of parameter space in optimization. Parallel Tempering is a meta-algorithm which may be used together with many existing McMC sampling and direct search optimization techniques. It's generality and demonstrated performance suggests that there is significant potential for applications to both sampling and optimization problems in the geosciences.

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 optimizationalgorithms are given.

Cr?ciunescu, Aurelian; Popescu, Claudia; Popescu, Mihai; Florea, Leonard Marin

Background The importance and power of isotope-based metabolic flux analysis and its contribution to understanding the metabolic network is increasingly recognized. Its application is, however, still limited partly due to computational inefficiency. 13C metabolic flux analysis aims to compute in vivo metabolic fluxes in terms of metabolite balancing extended by carbon isotopomer balances and involves a nonlinear least-squares problem. To solve the problem more efficiently, improved numerical optimization techniques are necessary. Results For flux computation, we developed a gradient-based hybridoptimizationalgorithm. Here, independent flux variables were compactified into [0, 1)-ranged variables using a single transformation rule. The compactified parameters could be discriminated between non-identifiable and identifiable variables after model linearization. The developed hybridalgorithm was applied to the central metabolism of Bacillus subtilis with only succinate and glutamate as carbon sources. This creates difficulties caused by symmetry of succinate leading to limited introduction of 13C labeling information into the system. The algorithm was found to be superior to its parent algorithms and to global optimization methods both in accuracy and speed. The hybridoptimization with tolerance adjustment quickly converged to the minimum with close to zero deviation and exactly re-estimated flux variables. In the metabolic network studied, some fluxes were found to be either non-identifiable or nonlinearly correlated. The non-identifiable fluxes could correctly be predicted a priori using the model identification method applied, whereas the nonlinear flux correlation was revealed only by identification runs using different starting values a posteriori. Conclusion This fast, robust and accurate optimization method is useful for high-throughput metabolic flux analysis, a posteriori identification of possible parameter correlations, and also for Monte Carlo simulations to obtain statistical qualities for flux estimates. In this way, it contributes to future quantitative studies of central metabolic networks in the framework of systems biology.

Approximating the inference probability Pr[X = xjE = e] in any sense, even fora single evidence node E, is NP-hard. This result holds for belief networks that areallowed to contain extreme conditional probabilities---that is, conditional probabilitiesarbitrarily close to 0. Nevertheless, all previous approximation algorithms have failedto approximate efficiently many inferences, even for belief networks without extremeconditional probabilities.We prove that we

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

Locating wells is an important step in oil exploitation. This paper proposes a novel approach, which first combines particle\\u000a swarm optimization, genetic algorithm, and a reservoir simulation evaluation tool to optimize the locations of vertical wells.\\u000a Simulation results show that the convergence efficiency of our approach outperforms traditional genetic algorithm and overcomes\\u000a the disadvantage of particle swarm algorithm that would

Xiaojian Dong; Zhijian Wu; Chao Dong; Zhangxin Chen; Hui Wang

Recursive identification of nonlinear systems is investigated using radial basis function networks. A novel approach is adopted which employs a hybrid clustering and least squares algorithm. The recursive clustering algorithm adjusts the centers of the ra...

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 hybridsalgorithms 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.

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.

In the present paper, the simulation and optimization of asynchronous AC motor through its controlling and modeling by convert d-q with Simulink of Mat lab Software has been studied. By utilization of classic PI controller and also with phase controller has been optimized which membership function center of it has been optimized by new intelligent algorithms such as Emperor and

The coverage problem is a crucial issue in wireless sensor networks (WSN), where a high coverage rate ensures a high quality of service of the WSN. This paper proposes a new algorithm to optimize sensor coverage using particle swarm optimization (PSO) and Voronoi diagram. PSO is used to find the optimal deployment of the sensors that gives the best coverage

: Many different procedures have been proposed for optimization calculationswhen first derivatives are not available. Further, several researchers havecontributed to the subject, including some who wish to prove convergence theorems,and some who wish to make any reduction in the least calculated valueof the objective function. There is not even a key idea that can be used as afoundation of a

Singleton arc consistency (SAC) enhances the pruning capability of arc consistency by ensuring that the network cannot become arc inconsistent af- ter the assignment of a value to a variable. Algo- rithms have already been proposed to enforce SAC, but they are far from optimal time complexity. We give a lower bound to the time complexity of en- forcing SAC,

The step size leading to the absolute minimum of the constant modulus (CM) criterion along the search direction can be obtained algebraically at each iteration among the roots of a third-degree polynomial. The resulting optimal step-size CMA (OS-CMA) is compared with other CM-based iterative techniques in terms of performance-versus-complexity trade-off.

Several combinatorial optimization problems choose elements to minimize the total cost of constructing a feasible solution that satisfies requirements of clients. In the Steiner Tree problem, for example, edges must be chosen to connect terminals (clients); in Vertex Cover, vertices must be chosen to cover edges (clients); in Facility Location, facilities must be chosen and demand vertices (clients) connected to

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 hybridalgorithm is compared with a genetic algorithm recently published.

Nepomuceno, Juan A.; Troncoso, Alicia; Aguilar–Ruiz, Jesús S.

This paper examines a novel optimization technique called genetic algorithms and its application to the optimization of reliability allocation strategies. Reliability allocation should occur in the initial stages of design, when the objective is to determine an optimal breakdown or allocation of reliability to certain components or subassemblies in order to meet system specifications. The reliability allocation optimization is applied to the design of a cluster tool, a highly complex piece of equipment used in semiconductor manufacturing. The problem formulation is presented, including decision variables, performance measures and constraints, and genetic algorithm parameters. Piecewise ``effort curves`` specifying the amount of effort required to achieve a certain level of reliability for each component of subassembly are defined. The genetic algorithm evolves or picks those combinations of ``effort`` or reliability levels for each component which optimize the objective of maximizing Mean Time Between Failures while staying within a budget. The results show that the genetic algorithm is very efficient at finding a set of robust solutions. A time history of the optimization is presented, along with histograms or the solution space fitness, MTBF, and cost for comparative purposes.

In a multimodal optimization task, the main purpose is to find multiple optimal solutions (global and local), so that the user can have better knowledge about different optimal solutions in the search space and as and when needed, the current solution may be switched to another suitable optimum solution. To this end, evolutionary optimizationalgorithms (EA) stand as viable methodologies mainly due to their ability to find and capture multiple solutions within a population in a single simulation run. With the preselection method suggested in 1970, there has been a steady suggestion of new algorithms. Most of these methodologies employed a niching scheme in an existing single-objective evolutionary algorithm framework so that similar solutions in a population are deemphasized in order to focus and maintain multiple distant yet near-optimal solutions. In this paper, we use a completely different strategy in which the single-objective multimodal optimization problem is converted into a suitable bi-objective optimization problem so that all optimal solutions become members of the resulting weak Pareto-optimal set. With the modified definitions of domination and different formulations of an artificially created additional objective function, we present successful results on problems with as large as 500 optima. Most past multimodal EA studies considered problems having only a few variables. In this paper, we have solved up to 16-variable test problems having as many as 48 optimal solutions and for the first time suggested multimodal constrained test problems which are scalable in terms of number of optima, constraints, and variables. The concept of using bi-objective optimization for solving single-objective multimodal optimization problems seems novel and interesting, and more importantly opens up further avenues for research and application. PMID:21591888

An ant colony optimization (ACO) algorithm offers algorithmic techniques for optimization by simulating the foraging behavior of a group of ants to perform incremental solution constructions and to realize a pheromone laying-and-following mechanism. Although ACO is first designed for solving discrete (combinatorial) optimization problems, the ACO procedure is also applicable to continuous optimization. This paper presents a new way of extending ACO to solving continuous optimization problems by focusing on continuous variable sampling as a key to transforming ACO from discrete optimization to continuous optimization. The proposed SamACO algorithm consists of three major steps, i.e., the generation of candidate variable values for selection, the ants' solution construction, and the pheromone update process. The distinct characteristics of SamACO are the cooperation of a novel sampling method for discretizing the continuous search space and an efficient incremental solution construction method based on the sampled values. The performance of SamACO is tested using continuous numerical functions with unimodal and multimodal features. Compared with some state-of-the-art algorithms, including traditional ant-based algorithms and representative computational intelligence algorithms for continuous optimization, the performance of SamACO is seen competitive and promising. PMID:20371409

Hu, Xiao-Min; Zhang, Jun; Chung, Henry Shu-Hung; Li, Yun; Liu, Ou

This article uses a hybridoptimization 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.

We present the "Near Optimal IsoSurface Extraction"(NOISE) algorithm for rapidly extracting isosurfaces fromstructured and unstructured grids. Using the span space, anew representation of the underlying domain, we developan isosurface extraction algorithm with a worst case complexityof O(pn + k) for the search phase, where n is the sizeof the data set and k is the number of cells intersected bythe

Yarden Livnat; Han-wei Shen; Christopher R. Johnson

http:\\/\\/www.polito.it\\/cadema Abstract. The aim of this work is to propose and validate a new multi- objective optimizationalgorithm based on the emulation of the immune system behavior. The rationale of this work is that the artificial im- mune system has, in its elementary structure, the main features required by other multiobjective evolutionary algorithms described in literature. The proposed approach is

This work presents a self-tuning feedwater heater model. This work continues the work on first-principle gray-box methodology applied to diagnostics and condition assessment of power plant components. The objective of this work is to review and benchmark the optimizationalgorithms regarding the time required to achieve the best model fit to operational power plant data. The paper recommends the most effective algorithm to be used in the model adjustment process.

A new software tool making use of a genetic algorithm for multi-objective experimental optimization (GAME.opt) was developed based on a strength Pareto evolutionary algorithm. The software deals with high dimensional variable spaces and unknown interactions of design variables. This approach was evaluated by means of multi-objective test problems replacing the experimental results. A default parameter setting is proposed enabling users without expert knowledge to minimize the experimental effort (small population sizes and few generations).

Hybrid systems combine time-driven and event-driven dynamics. This is a natural framework for manufacturing processes: The physical characteristics of production parts undergo changes at various operations described by time-driven models, while the timing control of operations is described by event-driven models. Accordingly, in the framework we propose, manufactured parts are characterized by physical states (e.g. temperature, geometry) subject to time-driven

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.

The problem of minimizing a function f(x):RJ ? R, subject to constraints on the vector variable x, occurs frequently in inverse problems. Even without constraints, finding a minimizer of f(x) may require iterative methods. We consider here a general class of iterative algorithms that find a solution to the constrained minimization problem as the limit of a sequence of vectors, each solving an unconstrained minimization problem. Our sequential unconstrained minimization algorithm (SUMMA) is an iterative procedure for constrained minimization. At the kth step we minimize the function G_k(x)=f(x)+g_k(x), to obtain xk. The auxiliary functions gk(x):D ? RJ ? R+ are nonnegative on the set D, each xk is assumed to lie within D, and the objective is to minimize the continuous function f:RJ ? R over x in the set C=\\overline D , the closure of D. We assume that such minimizers exist, and denote one such by \\hat x . We assume that the functions gk(x) satisfy the inequalities 0\\leq g_k(x)\\leq G_{k-1}(x)-G_{k-1}(x^{k-1}), for k = 2, 3, .... Using this assumption, we show that the sequence {f(xk)} is decreasing and converges to f({\\hat x}) . If the restriction of f(x) to D has bounded level sets, which happens if \\hat x is unique and f(x) is closed, proper and convex, then the sequence {xk} is bounded, and f(x^*)=f({\\hat x}) , for any cluster point x*. Therefore, if \\hat x is unique, x^*={\\hat x} and \\{x^k\\}\\rightarrow {\\hat x} . When \\hat x is not unique, convergence can still be obtained, in particular cases. The SUMMA includes, as particular cases, the well-known barrier- and penalty-function methods, the simultaneous multiplicative algebraic reconstruction technique (SMART), the proximal minimization algorithm of Censor and Zenios, the entropic proximal methods of Teboulle, as well as certain cases of gradient descent and the Newton-Raphson method. The proof techniques used for SUMMA can be extended to obtain related results for the induced proximal distance method of Auslander and Teboulle.

To effectively improve the recognition accuracy of the speech emotion recognition system, a hybridalgorithm which combines Continuous Hidden Markov Model (CHMM), All-Class-in-One Neural Network (ACON) and Support Vector Machine (SVM) is proposed. In SVM and ACON methods, some global statistics are used as emotional features, while in CHMM method, instantaneous features are employed. The recognition rate by the proposed method is 92.25%, with the rejection rate to be 0.78%. Furthermore, it obtains the relative increasing of 8.53%, 4.69% and 0.78% compared with ACON, CHMM and SVM methods respectively. The experiment result confirms the efficiency of distinguishing anger, happiness, neutral and sadness emotional states.

Ant Colony Optimization (ACO) is a meta- heuristic introduced by Dorigo et al. (9) which uses ideas from nature to find solutions to instances of the Travelling Salesman Problem (TSP) and other combinatorial optimisation problems. In this paper we analyse the parameter settings of the ACO algo- rithm. These determine the behaviour of each ant and are critical for fast

In this paper, a hybrid extended Kalman filter (EKF) and switching particle swarm optimization (SPSO) algorithm is proposed for jointly estimating both the parameters and states of the lateral flow immunoassay model through available short time-series measurement. Our proposed method generalizes the well-known EKF algorithm by imposing physical constraints on the system states. Note that the state constraints are encountered very often in practice that give rise to considerable difficulties in system analysis and design. The main purpose of this paper is to handle the dynamic modeling problem with state constraints by combining the extended Kalman filtering and constrained optimizationalgorithms via the maximization probability method. More specifically, a recently developed SPSO algorithm is used to cope with the constrained optimization problem by converting it into an unconstrained optimization one through adding a penalty term to the objective function. The proposed algorithm is then employed to simultaneously identify the parameters and states of a lateral flow immunoassay model. It is shown that the proposed algorithm gives much improved performance over the traditional EKF method. PMID:22025755

Zeng, Nianyin; Wang, Zidong; Li, Yurong; Du, Min; Liu, Xiaohui

Constrained optimization problems (COPs) are very important in that they frequently appear in the real world. A COP, in which both the function and constraints may be nonlinear, consists of the optimization of a function subject to constraints. Constraint handling is one of the major concerns when solving COPs with particle swarm optimization (PSO) combined with the Nelder-Mead simplex search method (NM-PSO). This article proposes embedded constraint handling methods, which include the gradient repair method and constraint fitness priority-based ranking method, as a special operator in NM-PSO for dealing with constraints. Experiments using 13 benchmark problems are explained and the NM-PSO results are compared with the best known solutions reported in the literature. Comparison with three different meta-heuristics demonstrates that NM-PSO with the embedded constraint operator is extremely effective and efficient at locating optimal solutions.

Mixed-model assembly line sequencing is one of the most important strategic problems in the field of production management where diversified customers' demands exist. In this article, three major goals are considered: (i) total utility work, (ii) total production rate variation and (iii) total setup cost. Due to the complexity of the problem, a hybrid multi-objective algorithm based on particle swarm optimization (PSO) and tabu search (TS) is devised to obtain the locally Pareto-optimal frontier where simultaneous minimization of the above-mentioned objectives is desired. In order to validate the performance of the proposed algorithm in terms of solution quality and diversity level, the algorithm is applied to various test problems and its reliability, based on different comparison metrics, is compared with three prominent multi-objective genetic algorithms, PS-NC GA, NSGA-II and SPEA-II. The computational results show that the proposed hybridalgorithm significantly outperforms existing genetic algorithms in large-sized problems.

Rahimi-Vahed, A. R.; Mirghorbani, S. M.; Rabbani, M.

Context: Mathematical optimization can be used as a computational tool to obtain the optimal solution to a given problem in a systematic and efficient way. For example, in twice-differentiable functions and problems with no constraints, the optimization consists of finding the points where the gradient of the objective function is zero and using the Hessian matrix to classify the type of each point. Sometimes, however it is impossible to compute these derivatives and other type of techniques must be employed such as the steepest descent/ascent method and more sophisticated methods such as those based on the evolutionary algorithms. Aims: We present a simple algorithm based on the idea of genetic algorithms (GA) for optimization. We refer to this algorithm as AGA (asexual genetic algorithm) and apply it to two kinds of problems: the maximization of a function where classical methods fail and model fitting in astronomy. For the latter case, we minimize the chi-square function to estimate the parameters in two examples: the orbits of exoplanets by taking a set of radial velocity data, and the spectral energy distribution (SED) observed towards a YSO (Young Stellar Object). Methods: The algorithm AGA may also be called genetic, although it differs from standard genetic algorithms in two main aspects: a) the initial population is not encoded; and b) the new generations are constructed by asexual reproduction. Results: Applying our algorithm in optimizing some complicated functions, we find the global maxima within a few iterations. For model fitting to the orbits of exoplanets and the SED of a YSO, we estimate the parameters and their associated errors.

This paper applies genetic algorithms (GAs), a powerful general-purpose biologically motivated optimization technique, to the multi-objective problem of spectrum optimization. Two objectives, color and efficiency, are address using real spectra, although the addition of other objectives (e.g., color rendering, color temperature) is relatively straightforward. The direct application of the method presented is to transform the spectrum of newly developed lighting

Neil H. Eklund; Oak Grove Scientific; Mark J. Embrechts

This paper presents the applications of steady-state genetic algorithms to discrete optimization of trusses. It is mathematically formulated as a constrained nonlinear optimization problem with discrete design variables. Discrete design variables are treated by a two-stage mapping process which is constructed by the mapping relationships between unsigned decimal integers and discrete values. With small generation gap and careful modification, steady-state

Musician’s behavior-inspired harmony search (HS) algorithm was first applied to the optimal operation scheduling of a multiple\\u000a dam system. The HS model tackled a popular benchmark system with four dams. Results showed that the HS model found five different\\u000a global optimal solutions with identical maximum benefit from hydropower generation and irrigation, while enhanced GA model\\u000a (real-value coding, tournament selection, uniform

Engineering design often requires solutions to constrained optimization problems with highly nonlinear objective and constraint\\u000a functions. The optimal solutions of most design problems lie on the constraint boundary. In this paper, Infeasibility Driven\\u000a Evolutionary Algorithm (IDEA) is presented that searches for optimum solutions near the constraint boundary. IDEA explicitly\\u000a maintains and evolves a small proportion of infeasible solutions. This behavior is

Hemant K. Singh; Amitay Isaacs; Tapabrata Ray; Warren Smith

This paper presents a hybrid discrete harmony search (DUS) algorithm for solving the blocking flow shop scheduling problem with the objective to minimize makespan. The proposed hybrid DUS algorithm utilizes discrete job permutations to represent harmonies and applies a job-permutation-based improvisation scheme to generate new harmonies. An initialization scheme based on a variant of the Minimum Blocking Tardiness (MBT) heuristic

Yu-Yan Han; Quan-Ke Pan; J. J. Liang; Jun-qing Liifl

Facing the huge amounts of data, the familiar classification algorithms show the shortages on time efficiency, robustness and accuracy. So this article puts the Hybrid Intelligent Systems into the research of classification algorithm. Based on the cognitive psychology and aggregative model theory, the article proposes a new Hybrid Intelligent System: R-FC-DENN, according to Rough Set, Clustering theory, Fuzzy Logic, Genetic

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.

Thyagarajan, T.; Shanmugam, J.; Ponnavaikko, M.; Panda, R.C.

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.

For estimating the states or outputs of a Markov process, the symbol-by-symbol maximum a posteriori(MAP) algorithm is optimal. However, this algorithm, even in its recursive form, poses technical difficultiesbecause of numerical representation problems, the necessity of non-linear functions and a high number ofadditions and multiplications. MAP like algorithms operating in the logarithmic domain presented in thepast solve the numerical problem

Patrick Robertson; Peter Hoeher; Emmanuelle Villebrun

A new hybrid multibias analytical\\/decomposition-based parameter extraction procedure for GaAs FETs is described. The analytical calculations are integrated into an existing decomposition-based optimizer in a complementary approach, further increasing the robustness of the existing algorithm. It is illustrated that, in order to increase the reliability with which the full 15-element small-signal model can be extracted, it is necessary to exploit

Cornell van Niekerk; J. A. du Preez; D. M. M.-P. Schreurs

An optimization framework based on the use of hybrid models is presented for preparative chromatographic processes. The first step in the hybrid model strategy involves the experimental determination of the parameters of the physical model, which consists of the full general rate model coupled with the kinetic form of the steric mass action isotherm. These parameters are then used to carry out a set of simulations with the physical model to obtain data on the functional relationship between various objective functions and decision variables. The resulting data is then used to estimate the parameters for neural-network-based empirical models. These empirical models are developed in order to enable the exploration of a wide variety of different design scenarios without any additional computational requirements. The resulting empirical models are then used with a sequential quadratic programming optimizationalgorithm to maximize the objective function, production rate times yield (in the presence of solubility and purity constraints), for binary and tertiary model protein systems. The use of hybrid empirical models to represent complex preparative chromatographic systems significantly reduces the computational time required for simulation and optimization. In addition, it allows both multivariable optimization and rapid exploration of different scenarios for optimal design. PMID:14763840

Nagrath, Deepak; Messac, Achille; Bequette, B Wayne; Cramer, S M

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.

This paper presents a heuristic optimality criterion algorithm for shape design of fluid flow. In this algorithm, the lattice Boltzmann method (LBM) is utilized to calculate the flow field of a fluid domain which is divided into elemental cells. A heuristic optimality criterion is applied for cells at the solid-fluid interface, i.e. the dynamic pressure for fluid cells and the viscous stress on their neighboring solid cells. An automatic program is processed step by step to exchange the positions of solid and fluid cells identified by the optimality criterion, with the objective of decreasing the flow resistance at the constraint of constant fluid volume. To illustrate the procedure of this algorithm for shape design of fluid flow, two simple examples are presented: one with fluid flowing through a right angle elbow and the other through a converging T-junction. Numerical results show that this algorithm can successfully reduce the total pressure drop of the system, demonstrating its potential applications in engineering optimal design.

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

Within the Linguistic Modeling Þeld, one of the most important applications of Fuzzy Rule-Based Systems, the automatic learning from numerical data of the fuzzy linguistic rules composing these systems is an important task. In this paper we introduce a novel way of addressing the problem making use of Ant Colony Optimization (ACO) algorithms. To do so, the learning task will

Ant Colony Optimization (ACO) has become quite popular in recent years. In contrast to many successful applications, the theoretical founda- tion of this randomized search heuristic is rather weak. Building up such a theory is demanded to understand how these heuristics work as well as to come up with better algorithms for certain problems. Up to now, only convergence results

In order to allocate traffic emergency rescue resources on expressway, considering rescue time and resources costs as the objective, stochastic variables are introduced into constraints and a corresponding stochastic programming model is established, due to the stochastic resource requirements of accidents. Because of large numbers of rescue depots and black-spots, a stochastic simulation of particle swarm optimization (PSO) algorithm is

The concept of Pareto optimality is applied to the study of choice tradeoffs between reflectivity and thickness in the design of multilayer microwave absorbers. Absorbers composed of a given number of layers of absorbing materials selected from a predefined database of available materials are considered. Three types of Pareto genetic algorithms for absorber synthesis are introduced and compared to each

In image processing, classification and compression are very common operations. Compression and classification algorithms are conventionally independent of each other and performed sequentially. However, some class distinctions may be lost after a minimum distortion compression. In this paper, two new schemes are developed that combine the compression and classification operations in order to optimize some classification metrics. In other words,

A novel paging scheme under delay bounds is proposed for personal communication systems. This paging scheme is independent of the location probability distributions of the mobile users and satisfies the delay bounds, while minimizing the amount of bandwidth used for locating a mobile user. The proposed paging scheme includes the optimal partition algorithm and paging procedure with respect to paging

Global structural optimizations with a genetic algorithm were performed for atomic cluster and surface systems including aluminum atomic clusters, Si magic clusters on the Si(111) 7x7 surface, silicon high-index surfaces, and Ag-induced Si(111) reconstruc...

This article reports the study of fractional dynamics during the evolution of a particle swarm optimization (PSO) algorithm. Some initial swarm particles are randomly changed, for stimulating the system response, and its effect is compared with a non-perturbed reference. The perturbation effect in the PSO evolution is observed in the perspective of the fitness time behavior of the best particle.

E. J. Solteiro Pires; P. B. de Oliveira; J. A. T. Machado; I. S. Jesus

A wind turbine tower optimization program was developed, using a genetic algorithm. This allowed a rational analysis to reduce the mass of turbine tower, by considering, for example, the distributions of diameter and wall thickness, and the positions of flanges and access ports to navigation lights. Both extreme and fatigue loads were calculated, based on wind turbine design requirements and

The particle swarm optimizationalgorithm is analyzed using standard results from the dynamic system theory. Graphical parameter selection guidelines are derived. The exploration–exploitation tradeoff is discussed and illustrated. Examples of performance on benchmark functions superior to previously published results are given.

Data mining is a process that uses technology to bridge the gap between data and logical decision-making. The jargon itself offers a promising view of organized data manipulation for extracting valuable information and knowledge from high volume of data. Copious techniques are developed to fulfill this aspiration. This paper outlines an ant colony optimizationalgorithm which is used newly in

A Genetic Algorithm (GA) is used in this paper for the optimal operation, result in better solution than the existing one, of the pipeline systems under transient conditions caused by valve closure. Simulation of pipeline system is carried out here by the Implicit Method of Characteristics, a method recently developed and introduced by the authors. This method uses an element-wise

A new tire design procedure capable of determining the optimum tire construction was developed by combining a finite element method approach with mathematical programming and a genetic algorithm (GA). Both procedures successfully generated optimized belt structures. The design variables in the mathematical programming were belt angle and belt width. Using the merits of a GA which enabled the use of

Themain contribution ofthiswork is an O(nlogr~ +k)-timeal gorithmfo rcomputingall k intersections among n line segments in the plane, This time complexity IS easdy shown to be optimal. Within thesame asymptotic cost, ouralgorithm canalso construct thesubdiwslon of theplancdefmed by the segments and compute which segment (if any) lies right above (or below) each intersection and each endpoint. The algorithm has been

In recent years, various types of equipment have become more intelligent. In this research, we propose an intelligent lighting system for providing required illuminance to specified locations, and develop autonomous distributed optimizationalgorithm which enables advanced lighting control. This system consists of multiple intelligent lighting fixtures, multiple movable illumination sensors and a power meter connected to a network. There is

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

In this paper, a novel pre-compression rate-distortion optimizationalgorithm is proposed, which can reduce computation power and memory requirement of JPEG 2000 encoder. It can reduce the wasted computational power of the entropy coder (EBCOT Tier-1) and unnecessary memory requirement for the code-stream. Distortion and rate of coding passes are calculated and estimated before coding, and therefore truncation point is selected before coding. Experimental results show that the computation time of EBCOT Tier-1 and memory requirement for the code-stream can be greatly reduced,especially at high compression ratio. The quality of the proposed algorithm is slightly lower than that of post-compression rate-distortion optimizationalgorithm.

Efficient Global Optimization (EGO) is a competent evolutionary algorithm which can be useful for problems with expensive cost functions [1,2,3,4,5]. The goal is to find the global minimum using as few function evaluations as possible. Our research indicates that EGO requires far fewer evaluations than genetic algorithms (GAs). However, both algorithms do not always drill down to the absolute minimum, therefore the addition of a final local search technique is indicated. In this paper, we introduce three "endgame" techniques. The techniques can improve optimization efficiency (fewer cost function evaluations) and, if required, they can provide very accurate estimates of the global minimum. We also report results using a different cost function than the one previously used [2,3].

In this paper, the performance of the Multi Dynamics Algorithm for Global Optimization (MAGO) is studied through simulation using five standard test functions. To guarantee that the algorithm converges to a global optimum, a set of experiments searching for the best combination between the only two MAGO parameters -number of iterations and number of potential solutions, are considered. These parameters are sequentially varied, while increasing the dimension of several test functions, and performance curves were obtained. The MAGO was originally designed to perform well with small populations; therefore, the self-adaptation task with small populations is more challenging while the problem dimension is higher. The results showed that the convergence probability to an optimal solution increases according to growing patterns of the number of iterations and the number of potential solutions. However, the success rates slow down when the dimension of the problem escalates. Logit Model is used to determine the mutual effects between the parameters of the algorithm.

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 optimizationalgorithm, 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.

Hogenboom, Alexander; Milea, Viorel; Frasincar, Flavius; Kaymak, Uzay

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.

Rahideh, A.; Korakianitis, T.; Ruiz, P.; Keeble, T.; Rothman, M. T.

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 optimizationalgorithm based

This article presents a hybrid particle-finite element algorithm for high-velocity impact. In the hybrid approach, both finite elements and meshless particles represent the same material simultaneously. The initial hybrid concept was introduced and developed by Fahrenthold and others. It is based on a Hamiltonian formulation for both the particles and elements, and it has used a structured arrangement for the

We propose in this paper an optimized, polymorphic, hybrid multicast routing protocol for MANET. This new polymorphic protocol attempts to benefit from the high efficiency of proactive behavior (in terms of quicker response to transmission requests) and the limited network traffic overhead of the reactive behavior, while being power, mobility, and vicinity-density (in terms of number of neighbor nodes per

Adel Ben Mnaouer; Lei Chen; Chuan Heng Foh; Juki Wirawan Tantra

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

In this paper, we explore hybrid parallel global optimization using Dividing Rectangles (DIRECT) and asynchronous generating set search (GSS). Both DIRECT and GSS are derivative-free and so require only objective function values; this makes these methods applicable to a wide variety of science and engineering problems. DIRECT is a global search method that strategically divides the search space into ever-smaller

This paper proposes a hybrid particle swarm optimization (HPSO) for a practical distribution state estimation. The proposed method considers nonlinear characteristics of the practical equipment and actual limited measurements in distribution systems. The method can estimate load and distributed generation output values at each node by minimizing the difference between measured and calculated voltages and currents. The feasibility of the

This paper proposes a practical distribution system state estimation method using a hybrid particle swarm optimization. The proposed method considers nonlinear characteristics of the practical equipment and actual measurements in distribution systems. The method can estimate load and distributed generation output values at each node by minimizing the difference between measured and calculated state variables. The feasibility of the proposed

Shigenori Naka; Takamu Genji; Toshiki Yura; Y. Fukuyama

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.

Global structural optimizations with a genetic algorithm were performed for atomic cluster and surface systems including aluminum atomic clusters, Si magic clusters on the Si(111) 7 x 7 surface, silicon high-index surfaces, and Ag-induced Si(111) reconstructions. First, the global structural optimizations of neutral aluminum clusters Al{sub n} (n up to 23) were performed using a genetic algorithm coupled with a tight-binding potential. Second, a genetic algorithm in combination with tight-binding and first-principles calculations were performed to study the structures of magic clusters on the Si(111) 7 x 7 surface. Extensive calculations show that the magic cluster observed in scanning tunneling microscopy (STM) experiments consist of eight Si atoms. Simulated STM images of the Si magic cluster exhibit a ring-like feature similar to STM experiments. Third, a genetic algorithm coupled with a highly optimized empirical potential were used to determine the lowest energy structure of high-index semiconductor surfaces. The lowest energy structures of Si(105) and Si(114) were determined successfully. The results of Si(105) and Si(114) are reported within the framework of highly optimized empirical potential and first-principles calculations. Finally, a genetic algorithm coupled with Si and Ag tight-binding potentials were used to search for Ag-induced Si(111) reconstructions at various Ag and Si coverages. The optimized structural models of {radical}3 x {radical}3, 3 x 1, and 5 x 2 phases were reported using first-principles calculations. A novel model is found to have lower surface energy than the proposed double-honeycomb chained (DHC) model both for Au/Si(111) 5 x 2 and Ag/Si(111) 5 x 2 systems.

A penalty adapting ant algorithm is presented in an attempt to eliminate the dependency of ant algorithms on the penalty parameter used for the solution of constrained optimization problems. The method uses an adapting mechanism for determination of the penalty parameter leading to elimination of the costly process of penalty parameter tuning. The method is devised on the basis of observation that for large penalty parameters, infeasible solutions will have a higher total cost than feasible solutions and vice versa. The method therefore uses the best feasible and infeasible solution costs of the iteration to adaptively adjust the penalty parameter to be used in the next iteration. The pheromone updating procedure of the max-min ant system is also modified to keep ants on and around the boundary of the feasible search space where quality solutions can be found. The sensitivity of the proposed method to the initial value of the penalty parameter is investigated and indicates that the method converges to optimal or near-optimal solutions irrespective of the initial starting value of the penalty parameter. This is significant as it eliminates the need for sensitivity analysis of the method with respect to the penalty factor, thus adding to the computational efficiency of ant algorithms. Furthermore, it is shown that the success rate of the search algorithm in locating an optimal solution is increased when a self-adapting mechanism is used. The presented method is applied to a benchmark pipe network optimization problem in the literature and the results are presented and compared with those of existing algorithms.

An algorithm for optimal sizing of all the components of a stand-alone hybrid wind-diesel electrical power generation system, in the sense of minimum energy cost is reported in this paper. The total system reactive power balance condition, which is not often considered for designing stand-alone systems, is included with others in the nonlinear constrained optimization technique employed to maximize the

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.

The paper presents an application of a constructive learning algorithm to optimization of circuits. For a given Boolean function f. a fresh constructive learning algorithm builds circuits belonging to the smallest F{sub n,m} class of functions (n inputs and having m groups of ones in their truth table). The constructive proofs, which show how arbitrary Boolean functions can be implemented by this algorithm, are shortly enumerated An interesting aspect is that the algorithm can be used for generating both classical Boolean circuits and threshold gate circuits (i.e. analogue inputs and digital outputs), or a mixture of them, thus taking advantage of mixed analogue/digital technologies. One illustrative example is detailed The size and the area of the different circuits are compared (special cost functions can be used to closer estimate the area and the delay of VLSI implementations). Conclusions and further directions of research are ending the paper.

\\u000a This paper proposes a novel approach to performing residual bandwidth optimization with QoS guarantees in multi-class networks.\\u000a The approach combines the use of a new highly scalable hybrid GA-VNS algorithm (Genetic Algorithm with Variable Neighborhood\\u000a Search) with the efficient and accurate estimation of QoS requirements using empirical effective bandwidth estimations. Given\\u000a a QoS-aware demand matrix, experimental results indicate that the

Gajaruban Kandavanam; Dmitri Botvich; Sasitharan Balasubramaniam; Brendan Jennings

Thermal performance of solar air collector depends on many parameters as inlet air temperature, air velocity, collector slope and properties related to collector. In this study, the effect of the different parameters which affect the performance of the solar air collector are investigated. In order to maximize the thermal performance of a solar air collector genetic algorithm (GA) and artificial bee colony algorithm (ABC) have been used. The results obtained indicate that GA and ABC algorithms can be applied successfully for the optimization of the thermal performance of solar air collector.

Vibration dynamic characteristics have been a major issue in the modeling and mechanical analysis of large hydro generators. An algorithm is developed for identifying vibration dynamic characteristics by means of hybrid genetic algorithm. From the measured dynamic responses of a hydro generator, an appropriate estimation algorithm is needed to identify the loading parameters, including the main frequencies and amplitudes of

This paper proposes a hybrid PSO\\/ACO algorithm for hierarchical classification, where the classes to be predicted are arranged in a tree-like hierarchy. The performance of the algorithm is evaluated on a challenging biological data set, involving the hierarchical functional classification of enzymes. The proposed algorithm is compared with an existing PSO for classification, which was also adapted for hierarchical classification.

Steady flat flight is widely used in the flight simulator training as an ideal initial state. To ensure the accurate solving of the steady flat flight state a hybrid genetic algorithm is put forward. The algorithm based on the new concept of ldquoindividual learning potentialityrdquo make the Lamarckian learning and Baldwinina learning genetic algorithm combination together organically according to the

Luan Zhibo; Huang Qitao; Jiang Hongzhou; Li Hongren

In this paper, a TSK-type fuzzy model (TFM) with a hybrid evolutionary learning algorithm (HELA) is proposed. The proposed HELA method combines the compact genetic algorithm (CGA) and the modified variable-length genetic algorithm (MVGA). Both the number of fuzzy rules and the adjustable parameters in the TFM are designed concurrently by the HELA method. In the proposed HELA method, individuals

In this paper, a TSK-type fuzzy model (TFM) with a hybrid evolutionary learning algorithm (HELA) is proposed. The proposed HELA method combines the compact ge- netic algorithm (CGA) and the modified variable-length genetic algorithm (MVGA). Both the number of fuzzy rules and the adjustable parameters in the TFM are designed concurrently using the HELA method. In the proposed HELA method,

A hybrid adaptive channel equalization technique for quadrature amplitude modulation (QAM) signals is proposed. The proposed algorithm, which is referred to as the modified constant modulus algorithm (MCMA), minimizes an error cost function that includes both amplitude and phase of the equalizer output. In addition to the amplitude-dependent term that is provided by the conventional constant modulus algorithm (CMA), the

Recursive identification of non-linear systems is investigated using radial basis function networks. A novel approach is adopted which employs a hybrid clustering and least squares algorithm. The recursive clustering algorithm adjusts the centres of the radial basis function network while the recursive least squares algorithm estimates the connection weights of the network. Because these two recursive learning rules are both

The paper develops and implements a highly applicable framework for the computation of coupled aerostructural design optimization.\\u000a The multidisciplinary aerostructural design optimization is carried out and validated for a tested wing and can be easily\\u000a extended to complex and practical design problems. To make the framework practical, the study utilizes a high-fidelity fluid\\/structure\\u000a interface and robust optimizationalgorithms for an

In this paper, three hybrid harmony search (HS) algorithms, namely, hybrid harmony search (hHS) algorithm, hybrid globalbest harmony search (hgHS) algorithm and hybrid modified globalbest harmony search (hmgHS) algorithm, are developed for solving the flow shop scheduling with blocking to minimize the total flow time. Firstly, a largest position value (LPV) rule is proposed to convert continuous harmony vectors into

Circular ring microstrip antennas have several interesting properties that make it attractive in wireless applications. Although several analysis techniques such as cavity model, generalized transmission line model, Fourier-Hankel transform domain and the method of matched asymptotic expansion have been studied by researchers, there is no efficient design tool that has been incorporated with a suitable optimizationalgorithm. In this paper, the cavity model analysis along with the genetic optimizationalgorithm is presented for the design of circular ring microstrip antennas. The method studied here is based on the well-known cavity model and the optimization of the dimensions and feed point location of the circular ring antenna is performed via the genetic optimizationalgorithm, to achieve an acceptable antenna operation around a desired resonance frequency. The antennas designed by this efficient design procedure were realized experimentally, and the results are compared. In addition, these results are also compared to the results obtained by the commercial electromagnetic simulation tool, the FEM based software, HFSS by ANSOFT.

Nowadays, analyzing human facial image has gained an ever-increasing importance due to its various applications. Image segmentation is required as a very important and fundamental operation for significant analysis and interpretation of images. Among the segmentation methods, image thresholding technique is one of the most well-known methods due to its simplicity, robustness, and high precision. Thresholding based on optimization of the objective function is among the best methods. Numerous methods exist for the optimization process and bacterial foraging optimization (BFO) is among the most efficient and novel ones. Using this method, optimal threshold is extracted and then segmentation of facial skin is performed. In the proposed method, first, the color facial image is converted from RGB color space to Improved Hue-Luminance-Saturation (IHLS) color space, because IHLS has a great mapping of the skin color. To perform thresholding, the entropy-based method is applied. In order to find the optimum threshold, BFO is used. In order to analyze the proposed algorithm, color images of the database of Sahand University of Technology of Tabriz, Iran were used. Then, using Otsu and Kapur methods, thresholding was performed. In order to have a better understanding from the proposed algorithm; genetic algorithm (GA) is also used for finding the optimum threshold. The proposed method shows the better results than other thresholding methods. These results include misclassification error accuracy (88%), non-uniformity accuracy (89%), and the accuracy of region's area error (89%).

We introduce a filter-based evolutionary algorithm (FEA) for constrained optimization. The filter used by an FEA explicitly imposes the concept of dominance on a partially ordered solution set. We show that the algorithm is provably robust for both linear and nonlinear problems and constraints. FEAs use a finite pattern of mutation offsets, and our analysis is closely related to recent convergence results for pattern search methods. We discuss how properties of this pattern impact the ability of an FEA to converge to a constrained local optimum.

Clevenger, Lauren M. (University of New Mexico); Hart, William Eugene; Ferguson, Lauren Ann (Texas Tech University)

This paper proposes a novel algorithm for function approximation that extends the standard generalized regression neural network. Instead of a single bandwidth for all the kernels, we employ a multiple-bandwidth configuration. However, unlike previous works that use clustering of the training data for the reduction of the number of bandwidths, we propose a distinct scheme that manages a dramatic bandwidth reduction while preserving the required model complexity. In this scheme, the algorithm partitions the training patterns to groups, where all patterns within each group share the same bandwidth. Grouping relies on the analysis of the local nearest neighbor distance information around the patterns and the principal component analysis with fuzzy clustering. Furthermore, we use a hybridoptimization procedure combining a very efficient variant of the particle swarm optimizer and a quasi-Newton method for global optimization and locally optimal fine-tuning of the network bandwidths. Training is based on the minimization of a flexible adaptation of the leave-one-out validation error that enhances the network generalization. We test the proposed algorithm with real and synthetic datasets, and results show that it exhibits competitive regression performance compared to other techniques. PMID:18179064

Goulermas, John Y; Zeng, Xiao-Jun; Liatsis, Panos; Ralph, Jason F

We investigate the role of additional information in reducing the computational complexity of the global optimization problem (GOP). Following this approach, we develop GMG -- an algorithm to find the Global Minimum with a Guarantee. The new algorithm breaks up an originally continuous GOP into a discrete (grid) search problem followed by a descent problem. The discrete search identifies the basin of attraction of the global minimum after which the actual location of the minimizer is found upon applying a descent algorithm. The algorithm is first applied to the golf course problem, which serves as a litmus test for its performance in the presence of both complete and degraded additional information. GMG is further assessed on a set of standard benchmark functions. We then illustrate the performance of the the validated algorithm on a simple realization of the monocular passive ranging (MPR) problem in remote sensing, which consists of identifying the range of an airborne target (missile, plane, etc.) from its observed radiance. This inverse problem is set as a GOP whereby the difference between the observed and model predicted radiances is minimized over the possible ranges and atmospheric conditions. We solve the GOP using GMG and report on the performance of the algorithm.

D'Helon, Cassius [ORNL; Protopopescu, Vladimir A [ORNL; Wells, Jack C [ORNL; Barhen, Jacob [ORNL

This paper proposes a RSM-based hybrid evolutionary algorithm (RHEA) which combined the merits of the popular programs such\\u000a as genetic algorithm (GA), tabu search method and response surface methodology (RSM). This algorithm, for improving the convergent\\u000a speed that is thought to be the demerit of genetic algorithm, uses response surface methodology and simplex method. The mutation\\u000a of GA offers random

This paper introduces a multilayer traffic network model and traffic network clustering method for solving the route selection problem (RSP) in car navigation system (CNS). The purpose of the proposed method is to reduce the computation time of route selection substantially with acceptable loss of accuracy by preprocessing the large size traffic network into new network form. The proposed approach further preprocesses the traffic network than the traditional hierarchical network method by clustering method. The traffic network clustering considers two criteria. We specify a genetic clustering algorithm for traffic network clustering and use NSGA-II for calculating the multiple objective Pareto optimal set. The proposed method can overcome the size limitations when solving route selection in CNS. Solutions provided by the proposed algorithm are compared with the optimal solutions to analyze and quantify the loss of accuracy.

The main objectives of data replication are improved availability and reduced communications cost for queries. Maintaining the various copies consistent, however, increases the communications cost incurred by updates. For a given degree of replication, the choice of a specific concurrency control algorithm can have a significant impact on the total communications cost. In this paper we present various models for analyzing and understanding the trade-offs between the potentially opposing objectives of maximum resiliency and minimum communications cost in the context of the quorum consensus class of algorithms. It is argued that an optional vote assignment is one which meets given resiliency goals and yet incurs the least communications cost compared with all other alternative assignments. A mathematical model for vote assignment is developed, and optimalalgorithms are presented. It is demonstrated that significant cost savings can be realized from these approaches. 14 refs., 1 fig., 2 tabs.

Ant Colony Optimization (ACO) has become quite popular in recent years. In contrast to many successful applications, the theoretical\\u000a foundation of this randomized search heuristic is rather weak. Building up such a theory is demanded to understand how these\\u000a heuristics work as well as to come up with better algorithms for certain problems. Up to now, only convergence results have

The paper presents an optimized design method for a flexible and economical embedded DSP system that can implement complex processing algorithms as biometric recognition, real-time image processing, etc. It consists of a floating-point DSP, 512 Kbytes data RAM, 1 Mbytes FLASH program memory, a CPLD for achieving flexible logic control of input channel and a RS-485 transceiver for local network

This paper describes a non-generational geneticalgorithm for multiobjective optimization.The fitness of each individual in thepopulation is calculated incrementally basedon the degree in which it is dominated inthe Pareto sense, or close to other individuals.The closeness of individuals is measuredusing a sharing function. The performanceof the algorithm presented is comparedto previous efforts on three multiobjectiveoptimization problems of growing difficulty.The behavior ...

This research proposes a method of obtaining an optimal trajectory of robot manipulator by using Harmony Search (HS). Despite\\u000a the fact that the Sequential Quadratic Programming (SQP) is popular as a solving method for optimum trajectory problems, SQP\\u000a needs a suitable initial value. The HS algorithm does however not require such process of setting initial value. Two results\\u000a are compared

To enable efficient resource provisioning in HaaS (Hardware as a Service) cloud systems, virtual machine packing, which migrate\\u000a virtual machines to minimize running real node, is essential. The virtual machine packing problem is a multi-objective optimization\\u000a problem with several parameters and weights on parameters change dynamically subject to cloud provider preference. We propose\\u000a to employ Genetic Algorithm (GA) method, that

Heat transfer enhancing surfaces are of interest for a wide range of industrial applications. The aim of this article is to provide a robust automated method for the design of two-dimensional enhanced surfaces. Multiobjective optimizationalgorithms are employed; the competing objectives addressed are the maximization of the heat transfer and the minimization of the pressure drop for Re = 1,000 and Pr = 0.74.

In this paper, we consider approximation algorithms for optimizing a generic multi-variate homogeneous polynomial function, subject to homogeneous quadratic constraints. Such optimiza- tion models have wide applications, e.g., in signal processing, magnetic resonance imaging (MRI), data training, approximation theory, and portfolio selection. Since polynomial functions are non- convex in general, the problems under consideration are all NP-hard. In this paper

Many real-world engineering design problems involve the simultaneous optimization of several conflicting objectives. In this paper, a method combining the struggle genetic crowding algorithm with Pareto-based population ranking is proposed to elicit trade-off frontiers. The new method has been tested on a variety of published problems, reliably locating both discontinuous Pareto frontiers as well as multiple Pareto frontiers in multi-modal

In tolerancing, the Out-Of-Roundness factor determines the relativecircularity of planar shapes. The measurement of concern in this workis the Minimum Radial Separation, as recommended by the AmericanNational Standards Institute (ANSI). Here we show that the algorithmgiven in Le and Lee [4] runs in \\\\Theta(n2) time even for convex polygons.Furthermore, we present an optimal O(n) time algorithm to computethe Minimum Radial

The quality of environmental decisions should be gauged according to managers' objectives. Management objectives generally seek to maximize quantifiable measures of system benefit, for instance population growth rate. Reaching these goals often requires a certain degree of learning about the system. Learning can occur by using management action in combination with a monitoring system. Furthermore, actions can be chosen strategically to obtain specific kinds of information. Formal decision making tools can choose actions to favor such learning in two ways: implicitly via the optimizationalgorithm that is used when there is a management objective (for instance, when using adaptive management), or explicitly by quantifying knowledge and using it as the fundamental project objective, an approach new to conservation.This paper outlines three conservation project objectives - a pure management objective, a pure learning objective, and an objective that is a weighted mixture of these two. We use eight optimizationalgorithms to choose actions that meet project objectives and illustrate them in a simulated conservation project. The algorithms provide a taxonomy of decision making tools in conservation management when there is uncertainty surrounding competing models of system function. The algorithms build upon each other such that their differences are highlighted and practitioners may see where their decision making tools can be improved. ?? 2010 Elsevier Ltd.

Probert, W. J. M.; Hauser, C. E.; McDonald-Madden, E.; Runge, M. C.; Baxter, P. W. J.; Possingham, H. P.

Active control of fixed wing aircraft using piezoelectric materials has the potential to improve its aeroelastic response while reducing weight penalties. However, the design of active aircraft wings is a complex optimization problem requiring the use of formal optimization techniques. In this paper, a hybridoptimization procedure is applied to the design of a scaled airplane wing model, represented by a flat composite plate, with piezoelectric actuation to improve the aeroelastic response. Design objectives include reduced static displacements, improved passenger comfort during gust and increased damping. Constraints are imposed on the electric power consumption and ply stresses. Design variables include composite stacking sequence, actuator/sensor locations and controller gain. Numerical results indicate significant improvements in the design objectives and physically meaningful optimal designs.

Chattopadhyay, Aditi; Seeley, Charles E.; Jha, Ratneshwar

In this paper, a novel hybridalgorithm 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 hybridalgorithm 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 hybridalgorithm. 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 hybridalgorithm 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.

A systematic procedure is devised to tackle the design of membrane-hybrid systems for waste reduction. A membrane-hybrid system corresponds to any separation network that employs reverse-osmosis modules, booster pumps, turbines and mass exchangers (e.g. extractors, adsorption columns, ion exchangers, etc.). The proposed approach provides a generally-applicable framework for simultaneously screening all potential separation processes of interest. The problem is formulated as an optimal synthesis task. The solution to this task provides the minimum-cost hybrid configuration, types and sizes of reverse-osmosis units, mass exchangers, pumps and turbines. It also identifies the best distribution of streams and waste reduction loads. A case study is tackled to illustrate the applicability of the devised procedure.

El-Halwagi, M.M. [Auburn Univ., AL (United States)

Optimization of combined structural and control systems is a complex problem requiring an inordinate amount of computer-processing time, especially the solution of the eigenvalue problem of a general unsymmetric square real matrix with complex eigenvalues and eigenvectors, which is frequently used in such problem. The few algorithms presented in the literature thus far have been applied to small structures with a few members and controllers only. Parallel processing on new-generation multiprocessor computers provides an opportunity to solve large-scale problems. In this paper, the integrated structural and control optimization problem is formulated by including constraints on displacements, stresses, and closed-loop eigenvalues and the corresponding damping factors. Then, parallel algorithms are presented for integrated optimization of structures on shared-memory multiprocessors such as the Cray YMP 8/864 supercomputer. In particular, parallel algorithms are presented for the solution of complex eigenvalue problems encountered in structural control problems using the method of matrix iteration for dominant eigenvalue(s). The solution is divided into two parts. The first part is the iteration for dominant eigenvalue(s) and the corresponding eigenvector(s) and the second part is the reduction of the matrix to obtain the smaller eigenvalue(s) and the corresponding eigenvector(s).

In a generic decision process, optimal stopping theory aims to achieve a good tradeoff between decision performance and time consumed, with the advantages of theoretical decision-making and predictable decision performance. In this paper, optimal stopping theory is employed to develop an effective hybrid model for the mode decision problem, which aims to theoretically achieve a good tradeoff between the two interrelated measurements in mode decision, as computational complexity reduction and rate-distortion degradation. The proposed hybrid model is implemented and examined with a multiview encoder. To support the model and further promote coding performance, the multiview coding mode characteristics, including predicted mode probability and estimated coding time, are jointly investigated with inter-view correlations. Exhaustive experimental results with a wide range of video resolutions reveal the efficiency and robustness of our method, with high decision accuracy, negligible computational overhead, and almost intact rate-distortion performance compared to the original encoder. PMID:23269749

This paper presents a new versatile optimizationalgorithm called modified particle swarm optimizationalgorithm (MPSO) for solving the economic dispatch (ED) problem of power systems. Compared with the classical PSO algorithm, several modified operators, such as mutation operator and neighborhood magnifying operator, are employed by MPSO to ensure the algorithm search globally in theory. Based on the stochastic analysis theorem,

Evolutionary optimizationalgorithms have been recently applied to optimal digital IIR filter design. In this paper, we apply a Bandwidth Adaptive Harmony Search (BAHS) algorithm to the design of 1- dimensional IIR filters. Harmony Search is an evolutionary algorithm, which emulates the improvisation process of musicians. We have modified the algorithm by setting the bandwidth equal to the standard deviation

Sayan Ghosh; Debarati Kundu; Kaushik Suresh; Swagatam Das; Ajith Abraham

This paper provides an improved search algorithm for optimal route planning by rebuilding the search area in intelligent transport system. The theory foundation is that the classical Dijsktra algorithm has not any directional feature during searching the optimal path, and the bidirectional Dijsktra has its own limit. Based on the analysis of the two algorithms, the new improved algorithm proposed

This paper presents a method to solve the vector optimization problem that determines both the noninferior solution set and the best compromise solution employing a modified genetic algorithm. The algorithm differs from the conventional one in the definition of fitness value and convergence criterion. Some parameters of the algorithm are adjusted to the vector optimization. The algorithm also contains the

Dong-Joon Sim; Hyun-Kyo Jung; Song-Yop Hahn; Jong-Soo Won

Thispaper introduces ordinal hill climbing algorithms for addressingdiscrete manufacturing process design optimization problems usingcomputer simulation models. Ordinal hill climbing algorithmscombine the search space reduction feature of ordinal optimizationwith the global search feature of generalized hill climbing algorithms.By iteratively applying the ordinal optimization strategy withinthe generalized hill climbing algorithm framework, the resultinghybrid algorithm can be applied to intractable discrete optimizationproblems. Computational

The Genetic Algorithms, GAs, are a method of global optimization that we use in the stage of optimization in the design of optical systems. In the case of optical design and optimization, the efficiency and convergence speed of GAs are related with merit function, crossover operator, and mutation operator. In this study we present a comparison between several genetic algorithms implementations using different optical systems, like achromatic cemented doublet, air spaced doublet and telescopes. We do the comparison varying the type of design parameters and the number of parameters to be optimized. We also implement the GAs using discreet parameters with binary chains and with continuous parameter using real numbers in the chromosome; analyzing the differences in the time taken to find the solution and the precision in the results between discreet and continuous parameters. Additionally, we use different merit function to optimize the same optical system. We present the obtained results in tables, graphics and a detailed example; and of the comparison we conclude which is the best way to implement GAs for design and optimization optical system. The programs developed for this work were made using the C programming language and OSLO for the simulation of the optical systems.

López-Medina, Mario E.; Vázquez-Montiel, Sergio; Herrera-Vázquez, Joel

Two new algorithms for deriving optimal and near-optimal flowcharts from limited entry decision tables are presented. Both take into account rule frequencies and the time needed to test conditions. One of the algorithms, called the optimum-finding algorithm, leads to a flowchart which truly minimizes execution time for a decision table in which simple rules are already contracted to complex rules.

In this paper we consider a multi-objective group scheduling problem in hybrid flexible flowshop with sequence-dependent setup\\u000a times by minimizing total weighted tardiness and maximum completion time simultaneously. Whereas these kinds of problems are\\u000a NP-hard, thus we proposed a multi-population genetic algorithm (MPGA) to search Pareto optimal solution for it. This algorithm\\u000a comprises two stages. First stage applies combined objective

An existing side-fired stream reformer is simulated using a rigorous model with proven reaction kinetics, incorporating aspects of heat transfer in the furnace and diffusion in the catalyst pellet. Thereafter, optimal conditions, which could lead to an improvement in its performance, are obtained. An adaptation of the nondominated sorting genetic algorithm is employed to perform a multiobjective optimization. For a fixed production rate of hydrogen from the unit, the simultaneous minimization of the methane feed rate and the maximization of the flow rate of carbon monoxide in the syngas are chosen as the two objective functions, keeping in mind the processing requirements, heat integration, and economics. For the design configuration considered in this study, sets of Pareto-optimal operating conditions are obtained. The results are expected to enable the engineer to gain useful insights into the process and guide him/her in operating the reformer to minimize processing costs and to maximize profits.

Rajesh, J.K.; Gupta, S.K.; Rangaiah, G.P.; Ray, A.K.

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.

The object of this study is to develop optimization procedures that account for both the optical heterogeneity as well as photosensitizer (PS) drug distribution of the patient prostate and thereby enable delivery of uniform photodynamic dose to that gland. We use the heterogeneous optical properties measured for a patient prostate to calculate a light fluence kernel (table). PS distribution is then multiplied with the light fluence kernel to form the PDT dose kernel. The Cimmino feasibility algorithm, which is fast, linear, and always converges reliably, is applied as a search tool to choose the weights of the light sources to optimize PDT dose. Maximum and minimum PDT dose limits chosen for sample points in the prostate constrain the solution for the source strengths of the cylindrical diffuser fibers (CDF). We tested the Cimmino optimization procedures using the light fluence kernel generated for heterogeneous optical properties, and compared the optimized treatment plans with those obtained using homogeneous optical properties. To study how different photosensitizer distributions in the prostate affect optimization, comparisons of light fluence rate and PDT dose distributions were made with three distributions of photosensitizer: uniform, linear spatial distribution, and the measured PS distribution. The study shows that optimization of individual light source positions and intensities are feasible for the heterogeneous prostate during PDT.

Altschuler, Martin D.; Zhu, Timothy C.; Hu, Yida; Finlay, Jarod C.; Dimofte, Andreea; Wang, Ken; Li, Jun; Cengel, Keith; Malkowicz, S. B.; Hahn, Stephen M.

An adaptive algorithm for drifting environments is proposed and tested in simulated environments. Two powerful problem solving\\u000a technologies namely Neural Networks and Genetic Algorithms are combined to produce intelligent agents that can adapt to changing\\u000a environments. Online learning enables the intelligent agents to capture the dynamics of changing environments efficiently.\\u000a The algorithm’s efficiency is demonstrated using a mine sweeper application.

\\u000a Vehicle routing problems (VRP) arise in many real-life applications within transportation and logistics. This paper considers\\u000a vehicle routing models with fuzzy travel times and its hybrid intelligent algorithm. Two new types of credibility programming\\u000a models including fuzzy chance-constrained programming and fuzzy chance-constrained goal programming are presented to model\\u000a fuzzy VRP. A hybrid intelligent algorithm based on fuzzy simulation and genetic

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.

Chandra, Ramu Sharat (Niskayuna, NY); Liu, Yan (Ballston Lake, NY); Bose, Sumit (Niskayuna, NY); de Bedout, Juan Manuel (West Glenville, NY)

The Swinging Door Trending algorithm and the Douglas-Peucker algorithm are both staple lossy compression algorithms. The former one is widely used in real-time database software of industry, while the latter one is more popular for spatial data processing. In this paper, these two algorithms are compared first to summarize their advantages and disadvantages. And then, an optimized lossy compression algorithm

A hybrid Wind\\/Photovoltaic\\/hydrogen\\/fuel cell generation system is designed to supply power demand. The major components of the system i.e. wind turbine generators, photovoltaic arrays and DC\\/AC converter may be subjected to failure. Also, solar radiation, wind speed and load data are assumed to be entirely deterministic. The goal of this design is to use a novel multi-objective optimizationalgorithm to

H. R. Baghaee; G. B. Gharehpetian; A. Kashefi Kaviani

Vertical coordinate and vertical mixing algorithms included in the HYbrid Coordinate Ocean Model (HYCOM) are evaluated in low-resolution climatological simulations of the Atlantic Ocean. The hybrid vertical coordinates are isopycnic in the deep ocean interior, but smoothly transition to level (pressure) coordinates near the ocean surface, to sigma coordinates in shallow water regions, and back again to level coordinates in

The aim of this work is to show the use of a well-known type of evolutionary computational optimization technique, ant colony optimization (ACO), in a typical electromagnetic problem: linear array synthesis. To this aim, an algorithm based on the fundamentals of ant colony optimization has been developed. The algorithm uses real numbers. Some examples using different optimization criteria are presented.

A optimization model of sizing the storage section in a renewable power generation system was set up, and two methods were used to solve the model: genetic algorithm or combinatorial optimization by genetic algorithm and neural network. The system includes the photovoltaic arrays, the lead-acid battery and a flywheel. The optimal sizing can be considered as a constrained optimization problem:

Ant colony optimization (ACO) is a metaheuristic for various optimization problems, especially the hard combinatorial optimization problems. However, existing ACO algorithms suffer from search stagnation and exorbitantly long computation time. To alleviate these shortcomings, an improved ACO algorithm, called GSP-ANT, is presented in this paper. It maintains a good solution pool (GSP) and alternately uses the optimal solution and suboptimal

Biomedical journal articles contain a variety of image types that can be broadly classified into two categories: regular images, and graphical images. Graphical images can be further classified into four classes: diagrams, statistical figures, flow charts, and tables. Automatic figure type identification is an important step toward improved multimodal (text + image) information retrieval and clinical decision support applications. This paper describes a feature-based learning approach to automatically identify these four graphical figure types. We apply Evolutionary Algorithm (EA), Binary Particle Swarm Optimization (BPSO) and a hybrid of EA and BPSO (EABPSO) methods to select an optimal subset of extracted image features that are then classified using a Support Vector Machine (SVM) classifier. Evaluation performed on 1038 figure images extracted from ten BioMedCentralÂ® journals with the features selected by EABPSO yielded classification accuracy as high as 87.5%.

Cheng, Beibei; Wang, Renzhong; Antani, Sameer; Stanley, R. Joe; Thoma, George R.

One of the central issues in dextrous robotic hand grasping is to balance external forces acting on the object and at the same time achieve grasp stability and minimum grasping effort. A companion paper shows that the nonlinear friction-force limit constraints on grasping forces are equivalent to the positive definiteness of a certain matrix subject to linear constraints. Further, compensation of the external object force is also a linear constraint on this matrix. Consequently, the task of grasping force optimization can be formulated as a problem with semidefinite constraints. In this paper, two versions of strictly convex cost functions, one of them self-concordant, are considered. These are twice-continuously differentiable functions that tend to infinity at the boundary of possible definiteness. For the general class of such cost functions, Dikin-type algorithms are presented. It is shown that the proposed algorithms guarantee convergence to the unique solution of the semidefinite programming problem associated with dextrous grasping force optimization. Numerical examples demonstrate the simplicity of implementation, the good numerical properties, and the optimality of the approach.

Buss, M. [Technical Univ. of Munich (Germany). Inst. of Automatic Control Engineering; Faybusovich, L. [Univ. of Notre Dame, IN (United States). Dept. of Mathematics; Moore, J.B. [Australian National Univ., Canberra, Australia Capital Territory (Australia)

A method for the optimization of a grid-connected wind turbine system is presented. The behaviour of the system components is coupled in a non-linear way, and optimization must take into account technical and economical aspects of the complete system design. The annual electrical energy cost is estimated using a cost model for the wind turbine rotor, nacelle and tower and an energy output model based on the performance envelopes of the power coefficient of the rotor, CP, on the Weibull parameters k and c and on the power law coefficient of the wind profile. In this study the site is defined with these three parameters and the extreme wind speed Vmax. The model parameters vary within a range of possible values. Other elements of the project (foundation, grid connection, financing cost, etc.) are taken into account through coefficients. The optimal values of the parameters are determined using genetic algorithms, which appear to be efficient for such a problem. These optimal values were found to be very different for a Mediterranean site and a northern European site using our numerical model. Optimal wind turbines at the Mediterranean sites considered in this article have an excellent profitability compared with reference northern European wind turbines. Most of the existing wind turbines appear to be well designed for northern European sites but not for Mediterranean sites.

Diveux, T.; Sebastian, P.; Bernard, D.; Puiggali, J. R.; Grandidier, J. Y.

The multi-objective genetic algorithm (MOGA) is applied to the multi-disciplinary conceptual design problem for a three-stage launch vehicle (LV) with a hybrid rocket engine (HRE). MOGA is an optimization tool used for multi-objective problems. The parallel coordinate plot (PCP), which is a data mining method, is employed in the post-process in MOGA for design knowledge discovery. A rocket that can deliver observing micro-satellites to the sun-synchronous orbit (SSO) is designed. It consists of an oxidizer tank containing liquid oxidizer, a combustion chamber containing solid fuel, a pressurizing tank and a nozzle. The objective functions considered in this study are to minimize the total mass of the rocket and to maximize the ratio of the payload mass to the total mass. To calculate the thrust and the engine size, the regression rate is estimated based on an empirical model for a paraffin (FT-0070) propellant. Several non-dominated solutions are obtained using MOGA, and design knowledge is discovered for the present hybrid rocket design problem using a PCP analysis. As a result, substantial knowledge on the design of an LV with an HRE is obtained for use in space transportation.

This paper presents an effective and simple algorithm, called hybridalgorithm, suitable for passive localization in cellular networks. This novel method fulfils the stringent requirements of the system developed within the WISECOM project (co-funded by the European Commission) that aims to restore GSM (Global System for Mobile Communication) or 3G (Third Generation) infrastructure thanks to the backhaul over satellite and

Dimitri Tassetto; Eriza Hafid Fazli; Markus Werner

A genetic algorithm for partitioning a hypergraph into two disjoint graphs of minimum ratio cut is presented. As the Fiduccia-Mattheyses graph partitioning heuristic turns out to be not effective when used in the context of a hybrid genetic algorithm, we propose a modification of the Fiduccia-Mattheyses heuristic for more effective and faster space search by introducing a number of novel

Clinical pathways' variances present complex, fuzzy, uncertain and high-risk characteristics. They could cause complicating diseases or even endanger patients' life if not handled effectively. In order to improve the accuracy and efficiency of variances handling by Takagi-Sugeno (T-S) fuzzy neural networks (FNNs), a new variances handling method for clinical pathways (CPs) is proposed in this study, which is based on T-S FNNs with novel hybrid learning algorithm. And the optimal structure and parameters can be achieved simultaneously by integrating the random cooperative decomposing particle swarm optimizationalgorithm (RCDPSO) and discrete binary version of PSO (DPSO) algorithm. Finally, a case study on liver poisoning of osteosarcoma preoperative chemotherapy CP is used to validate the proposed method. The result demonstrates that T-S FNNs based on the proposed algorithm achieves superior performances in efficiency, precision, and generalization ability to standard T-S FNNs, Mamdani FNNs and T-S FNNs based on other algorithms (CPSO and PSO) for variances handling of CPs. PMID:20862603

Du, Gang; Jiang, Zhibin; Diao, Xiaodi; Ye, Yan; Yao, Yang

Real time tool path generation consists of off-line design and real time interpolation of tool paths. An hybrid curve is the intersection of a parametric surface and an implicit surface. Previous work in tool path interpolation focused mainly in the interpolation of parametric curves. Tool paths designed by drive surface methods are hybrid curves which, in general, cannot be represented

Non-Dominated Sorting Genetic Algorithm 2 (NSGA 2) was used to optimize optical systems with multiple objectives. The systems selected for study are Cooke triplets, Petzval lens and double Gauss lens. The objectives are minimization of aberration coefficients for spherical aberration, distortion, and the sum of coefficients of all third order monochromatic aberrations. CODE V RTM was used as a ray tracer. A set of trade-off solutions representing the optima, known as Pareto-Optima in multi-objective analysis, was obtained. A comparison of obtained optima to the known optima was done. Pareto-Optima in objective space for the selected Petzval lens design problem are shown to exhibit saddle points having unique trade-off features, which can not be detected in traditional gradient-based scalar optimization. Various optimization strategies are illustrated which ensure a diverse set of Pareto-Optima offering alternate manufacturing choices. Based on the results, a fourth objective was identified (sum of lateral and axial color coefficients) that is necessary to make valid trade-off decisions. The expansion of objectives followed by re-optimization provided unique trade-off solutions. Based on power and symmetry distribution of the component elements for the Cooke triplet system, addition and deletion of elements were carried out. The fourth objective added for that study is the minimization of the required number of elements. For the double Gauss lens system, the Pareto optimal surface indicated alternate manufacturing choices. There is a clear diversity of the Pareto optimal front in both objective and decision vector space. These studies have clearly illustrated the advantages of evolutionary multi-objective optimization techniques in optical system design.

A nonsingular transformation algorithm which converts a singular control problem to nonsingular one was developed to calculate\\u000a optimal control profiles. Several chemical engineering problems for applying a nonsingular transformation algorithm were presented\\u000a and optimal profiles were calculated in this paper. The singular control algorithm and nonsingular transformation algorithm\\u000a were compared. The efficiency of the transformation algorithm was displayed in this

For the aloha based anti-collision algorithm in RFID networks, the tag collisions could greatly reduce the throughput of the system. If the number of tags was gotten, the throughput could be greatly improved. Based on maximum likelihood estimation, the proposed hybrid tag number estimation scheme combined the binary search based anti-collision algorithm and aloha based anti-collision algorithm to estimate the

Zheng-Ping Li; Shu-Jun Guo; Yu-Hua Wang; Zhi-Hao Yang; Min Zhang

The configuring of a radial basis function network (RBFN) consists of selecting the network parameters (centers and widths in RBF units and weights between the hidden and output layers) and network architecture. The issues of suboptimum and overfitting, however, often occur in RBFN configuring. This paper presented a hybrid particle swarm optimization (HPSO) algorithm to simultaneously search the optimal network structure and parameters involved in the RBFN (HPSORBFN) with an ellipsoidal Gaussian function as a basis function. The continuous version of PSO was used for parameter training, while the modified discrete PSO was employed to determine the appropriate network topology. The proposed HPSORBFN algorithm was applied to modeling the inhibitory activities of substituted bis[(acridine-4-carboxamide)propyl]methylamines to murine P388 leukemia cells and the bioactivities of COX-2 inhibitors. The results were compared with those obtained from RBFNs with the parameters optimized by continuous PSO and by conventionally RBFN training the algorithm for a fixed network topology, indicating that the HPSO was competent for RBFN configuring in that it converged quickly toward the optimal solution and avoided overfitting. PMID:17125190

Using a global approach, a wind hybrid system operation is simulated and the evolution of several parameters is analysed, such as the wasted energy, the fuel consumption and the role of the wind turbine subsystem in the global production. This analysis shows that all the energies which take part in the system operation are more dependent on the wind turbine size than on the battery storage capacity. A storage of 2 or 3 days is sufficient, because an increase in storage beyond these values does not have a notable impact on the performance of the wind hybrid system. Finally, a cost study is performed to determine the optimal configuration of the system conducive to the lowest cost of electricity production.

Notton, G.; Cristofari, C.; Poggi, P.; Muselli, M.

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

As there is a growing interest in applications of multi-objective optimization methods to real-world problems, it is essential to develop efficient algorithms to achieve better performance in engineering design and resources optimization. An efficient algorithm for multi-objective optimization, based on swarm intelligence principles, is presented in this article. The proposed algorithm incorporates a Pareto dominance relation into particle swarm optimization

The research presented in this dissertation is motivated by the need for new, efficient algorithms for the solution of two important problems currently faced by the air-traffic control community: (i) optimal scheduling of aircraft arrivals at congested airports, and (ii) optimal National Airspace System (NAS) wide traffic flow management. In the first part of this dissertation, we present an optimal airport arrival scheduling algorithm, which works within a hierarchical scheduling structure. This structure consists of schedulers at multiple points along the arrival-route. Schedulers are linked through acceptance-rate constraints, which are passed up from downstream metering-points. The innovation in this scheduling algorithm is that these constraints are computed by using an Eulerian model-based optimization scheme. This rate computation removes inefficiencies introduced in the schedule through ad hoc acceptance-rate computations. The scheduling process at every metering-point uses its optimal acceptance-rate as a constraint and computes optimal arrival sequences by using a combinatorial search-algorithm. We test this algorithm in a dynamic air-traffic environment, which can be customized to emulate different arrival scenarios. In the second part of this dissertation, we introduce a novel two-level control system for optimal traffic-flow management. The outer-level control module of this two-level control system generates an Eulerian-model of the NAS by aggregating aircraft into interconnected control-volumes. Using this Eulerian model of the airspace, control strategies like Model Predictive Control are applied to find the optimal inflow and outflow commands for each control-volume so that efficient flows are achieved in the NAS. Each control-volume has its separate inner-level control-module. The inner-level control-module takes in the optimal inflow and outflow commands generated by the outer control-module as reference inputs and uses hybrid aircraft models to search for optimal trajectories to be flown by each aircraft so that the flows commanded by the outer control-module are achieved. The two-level control system is tested in a dynamic simulation. Furthermore, as a component of the Eulerian part of this two-level system, we present a method for deriving an aggregate airspace-model in real-time, without depending on online integration of aircraft trajectories. This method uses a baseline Eulerian airspace-model, which is derived offline using historical track-data. In real-time, parameters of this model are adapted depending on the differences between the baseline-model and the real-world. This book-keeping based model-derivation indirectly retains some trajectory information. Hence, it serves as an excellent trade-off between Eulerian and trajectory-based modeling approaches. Most importantly, as a vital improvement over previous approaches, we take into consideration the control-dependent nature of the Eulerian-model while computing optimal flow-control decisions. As a proof of concept, we derive a baseline model for the Fort-Worth center and adapt it to predict sector-counts for another set of air traffic data. We also demonstrate the use of this model in a simulation-based optimization scheme for regulating the arrival flow at the Dallas Fort-Worth airport. An application to optimal re-routing strategy computation is also presented.