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1

Hessian approximation algorithms for hybrid optimization methods  

Microsoft Academic Search

This article introduces Hessian approximation algorithms to estimate the search direction of the quasi-Newton methods for solving optimization problems of continuous parameters. The proposed algorithms are quite different from other well-known quasi-Newton methods, such as symmetric rank-one, Davidon–Fletcher–Powell, and Broyden–Fletcher–Goldfarb–Shanno, in that the Hessian matrix is not calculated from the gradient information, rather directly from the function values. The proposed

Min-Jea Tahk; Moon-Su Park; Hyun-Wook Woo; Hyoun-Jin Kim

2009-01-01

2

Hybrid Particle Swarm - Evolutionary Algorithm for Search and Optimization  

Microsoft Academic Search

Particle Swarm Optimization (PSO) technique has proved its ability to deal with very complicated optimization and search prob- lems. Several variants of the original algorithm have been proposed. This paper proposes a novel hybrid PSO - evolutionary algorithm for solving the well known geometrical place problems. Finding the geometrical place could be sometimes a hard task. In almost all situations

Crina Grosan; Ajith Abraham; Sangyong Han; Alexander F. Gelbukh

2005-01-01

3

An Efficient Hybrid Algorithm for Optimization of Discrete Structures  

Microsoft Academic Search

Presented in this paper is a hybrid algorithm 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

Amitay Isaacs; Tapabrata Ray; Warren Smith

2008-01-01

4

A Hybrid Genetic Algorithm for Routing Optimization in IP Networks  

E-print Network

engineering are demonstrated. Keywords ­ IP Traffic engineering, genetic algorithm, bandwidth-delay sensitiveA Hybrid Genetic Algorithm for Routing Optimization in IP Networks Utilizing Bandwidth and Delay traffic engineering, which relies on conventional, destination-based routing protocols. We introduce

Riedl, Anton

5

Hybrid Approach to Optimal Packing Using Genetic Algorithm and Coulomb Potential Algorithm  

Microsoft Academic Search

It is difficult and computationally time-consuming to find the best possible solutions for blank packing problems, because they include a lot of underlying combinational conditions. This paper presents two approaches for packing two-dimensional irregular-shaped polygonal elements—a real-encoded genetic algorithm and a hybrid algorithm using a real-encoded genetic algorithm and a local optimization algorithm. The local optimization algorithm presented is a

Biswajit Mahanty; Rajneesh Kumar Agrawal; Shrikrishna Shrin; Sourish Chakravarty

2007-01-01

6

A Hybrid Ant Colony Algorithm for Loading Pattern Optimization  

NASA Astrophysics Data System (ADS)

Electricité de France (EDF) operates 58 nuclear power plant (NPP), of the Pressurized Water Reactor (PWR) type. The loading pattern (LP) optimization of these NPP is currently done by EDF expert engineers. Within this framework, EDF R&D has developed automatic optimization tools that assist the experts. The latter can resort, for instance, to a loading pattern optimization software based on ant colony algorithm. This paper presents an analysis of the search space of a few realistic loading pattern optimization problems. This analysis leads us to introduce a hybrid algorithm based on ant colony and a local search method. We then show that this new algorithm is able to generate loading patterns of good quality.

Hoareau, F.

2014-06-01

7

Application of the hybrid algorithm combining ant colony optimization algorithm with microgenetic algorithm to the optimization of multilayered radar absorbing coating  

Microsoft Academic Search

A new optimization technique based on the hybrid algorithm combining ant colony optimization algorithm 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

Kun Chao; Yunlin Liu; Rugui Yang

2008-01-01

8

Operational Optimal Ship Routing Using a Hybrid Parallel Genetic Algorithm  

E-print Network

Optimization of ship routing depends on several parameters, like ship and cargo characteristics, environmental factors, topography, international navigation rules, crew comfort etc. The complex nature of the problem leads to oversimplifications in analytical techniques, while stochastic methods like simulated annealing can be both time consuming and sensitive to local minima. In this work, a hybrid parallel genetic algorithm - estimation of distribution algorithm is developed in the island model, to operationally calculate the optimal ship routing. The technique, which is applicable not only to clusters but to grids as well, is very fast and has been applied to very difficult environments, like the Greek seas with thousands of islands and extreme micro-climate conditions.

O. T. Kosmas; D. S. Vlachos

2008-11-13

9

Hybrid genetic algorithm research and its application in problem optimization  

Microsoft Academic Search

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

2004-01-01

10

An optimized hybrid encode based compression algorithm for hyperspectral image  

NASA Astrophysics Data System (ADS)

Compression is a kernel procedure in hyperspectral image processing due to its massive data which will bring great difficulty in date storage and transmission. In this paper, a novel hyperspectral compression algorithm based on hybrid encoding which combines with the methods of the band optimized grouping and the wavelet transform is proposed. Given the characteristic of correlation coefficients between adjacent spectral bands, an optimized band grouping and reference frame selection method is first utilized to group bands adaptively. Then according to the band number of each group, the redundancy in the spatial and spectral domain is removed through the spatial domain entropy coding and the minimum residual based linear prediction method. Thus, embedded code streams are obtained by encoding the residual images using the improved embedded zerotree wavelet based SPIHT encode method. In the experments, hyperspectral images collected by the Airborne Visible/ Infrared Imaging Spectrometer (AVIRIS) were used to validate the performance of the proposed algorithm. The results show that the proposed approach achieves a good performance in reconstructed image quality and computation complexity.The average peak signal to noise ratio (PSNR) is increased by 0.21~0.81dB compared with other off-the-shelf algorithms under the same compression ratio.

Wang, Cheng; Miao, Zhuang; Feng, Weiyi; He, Weiji; Chen, Qian; Gu, Guohua

2013-12-01

11

Multidisciplinary Design and Optimization of Multistage Ground-launched Boost Phase Interceptor Using Hybrid Search Algorithm  

Microsoft Academic Search

This article proposes a multidisciplinary design and optimization (MDO) strategy for the conceptual design of a multistage ground-based interceptor (GBI) using hybrid optimization algorithm, which associates genetic algorithm (GA) as a global optimizer with sequential quadratic programming (SQP) as a local optimizer. The interceptor is comprised of a three-stage solid propulsion system for an exoatmospheric boost phase intercept (BPI). The

Qasim Zeeshan; Dong Yunfeng; Khurram Nisar; Ali Kamran; Amer Rafique

2010-01-01

12

A homogeneous superconducting magnet design using a hybrid optimization algorithm  

NASA Astrophysics Data System (ADS)

This paper employs a hybrid optimization algorithm with a combination of linear programming (LP) and nonlinear programming (NLP) to design the highly homogeneous superconducting magnets for magnetic resonance imaging (MRI). The whole work is divided into two stages. The first LP stage provides a global optimal current map with several non-zero current clusters, and the mathematical model for the LP was updated by taking into account the maximum axial and radial magnetic field strength limitations. In the second NLP stage, the non-zero current clusters were discretized into practical solenoids. The superconducting conductor consumption was set as the objective function both in the LP and NLP stages to minimize the construction cost. In addition, the peak-peak homogeneity over the volume of imaging (VOI), the scope of 5 Gauss fringe field, and maximum magnetic field strength within superconducting coils were set as constraints. The detailed design process for a dedicated 3.0 T animal MRI scanner was presented. The homogeneous magnet produces a magnetic field quality of 6.0 ppm peak-peak homogeneity over a 16 cm by 18 cm elliptical VOI, and the 5 Gauss fringe field was limited within a 1.5 m by 2.0 m elliptical region.

Ni, Zhipeng; Wang, Qiuliang; Liu, Feng; Yan, Luguang

2013-12-01

13

SEARCH OPTIMIZATION USING HYBRID PARTICLE SUB SWARMS AND EVOLUTIONARY ALGORITHMS  

Microsoft Academic Search

Particle Swarm Optimization (PSO) technique proved its ability to deal with very complicated optimization and search problems. Several variants of the original algorithm have been proposed. This paper proposes a variant of the PSO technique named Independent Neighborhoods Particle Swarm Optimization (INPSO) dealing with sub-swarms for solving the well known geometrical place problems. Finding the geometrical place can be sometimes

CRINA GROSAN; AJITH ABRAHAM; MONICA NICOARA

2005-01-01

14

Experimental Study on a Hybrid Nature-Inspired Algorithm for Financial Portfolio Optimization  

Microsoft Academic Search

\\u000a Hybrid intelligent schemes have proven their efficiency in solving NP-hard optimization problems. Portfolio optimization refers\\u000a to the problem of finding the optimal combination of assets and their corresponding weights which satisfies a specific investment\\u000a goal and various constraints. In this study, a hybrid intelligent metaheuristic, which combines the Ant Colony Optimization\\u000a algorithm and the Firefly algorithm, is proposed in tackling

Giorgos Giannakouris; Vassilios Vassiliadis; Georgios Dounias

2010-01-01

15

A hybrid optimization algorithm for the job-shop scheduling problem  

Microsoft Academic Search

The job-shop scheduling problem is a NP-hard combinational optimization and one of the best-known machine scheduling problems. Genetic algorithm is an effective search algorithm to solve this problem; however the quality of the best solution obtained by the algorithm has to improve due to its limitation. The paper proposes a novel hybrid optimization algorithm for the job-shop scheduling problem, which

Qiang Zhou; Xunxue Cui; Zhengshan Wang; Bin Yang

2009-01-01

16

A new hybrid optimization algorithm for the job-shop scheduling problem  

Microsoft Academic Search

A new hybrid optimization algorithm is proposed for the problem of finding the minimum makespan in the job-shop scheduling environment. The new algorithm is based on the principle of particle swarm optimization (PSO). PSO employs a collaborative population-based search, which combines local search (by self experience) and global search (by neighboring experience), possessing high search efficiency. Simulated annealing (SA) employs

Xia Weijun; Wu Zhiming; Zhang Wei; Yang Genke

2004-01-01

17

A hybrid of genetic algorithm and particle swarm optimization for recurrent network design  

Microsoft Academic Search

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

Chia-feng Juang

2004-01-01

18

Optimization of a total internal reflection lens by using a hybrid Taguchi-simulated annealing algorithm  

NASA Astrophysics Data System (ADS)

In this paper, we propose a new method for optimization of a total internal reflection (TIR) lens by using a hybrid Taguchi-simulated annealing algorithm. The conventional simulated annealing (SA) algorithm is a method for solving global optimization problems and has also been used in non-imaging systems in recent years. However, the success of SA depends heavily on the annealing schedule and initial parameter setting. In this study, we successfully incorporated the Taguchi method into the SA algorithm. The new hybrid Taguchi-simulated annealing algorithm provides more precise search results and has lower initial parameter dependence.

Chao, Shih-Min; Whang, Allen Jong-Woei; Chou, Chun-Han; Su, Wei-Shao; Hsieh, Tsung-Heng

2014-03-01

19

Multiobjective optimization and hybrid evolutionary algorithm to solve constrained optimization problems.  

PubMed

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

Wang, Yong; Cai, Zixing; Guo, Guanqi; Zhou, Yuren

2007-06-01

20

Effective hybrid evolutionary computational algorithms for global optimization and applied to construct prion AGAAAAGA fibril models  

E-print Network

Evolutionary algorithms are parallel computing algorithms and simulated annealing algorithm is a sequential computing algorithm. This paper inserts simulated annealing into evolutionary computations and successful developed a hybrid Self-Adaptive Evolutionary Strategy $\\mu+\\lambda$ method and a hybrid Self-Adaptive Classical Evolutionary Programming method. Numerical results on more than 40 benchmark test problems of global optimization show that the hybrid methods presented in this paper are very effective. Lennard-Jones potential energy minimization is another benchmark for testing new global optimization algorithms. It is studied through the amyloid fibril constructions by this paper. To date, there is little molecular structural data available on the AGAAAAGA palindrome in the hydrophobic region (113-120) of prion proteins.This region belongs to the N-terminal unstructured region (1-123) of prion proteins, the structure of which has proved hard to determine using NMR spectroscopy or X-ray crystallography ...

Zhang, Jiapu

2010-01-01

21

Methods for Integrating Knowledge with the Three-Weight Optimization Algorithm for Hybrid Cognitive Processing  

Microsoft Academic Search

In this paper we consider optimization as an approach for quickly and flexibly developing hybrid cognitive ca- pabilities that are efficient, scalable, and can exploit knowledge to improve solution speed and quality. In this context, we focus on the Three-Weight Algorithm, which aims to solve general optimization problems. We propose novel methods by which to integrate knowl- edge with this

Nate Derbinsky; Jose Bento; Jonathan S. Yedidia

2013-01-01

22

An adaptive hybrid algorithm based on particle swarm optimization and differential evolution for global optimization.  

PubMed

Particle swarm optimization (PSO) and differential evolution (DE) are both efficient and powerful population-based stochastic search techniques for solving optimization problems, which have been widely applied in many scientific and engineering fields. Unfortunately, both of them can easily fly into local optima and lack the ability of jumping out of local optima. A novel adaptive hybrid algorithm based on PSO and DE (HPSO-DE) is formulated by developing a balanced parameter between PSO and DE. Adaptive mutation is carried out on current population when the population clusters around local optima. The HPSO-DE enjoys the advantages of PSO and DE and maintains diversity of the population. Compared with PSO, DE, and their variants, the performance of HPSO-DE is competitive. The balanced parameter sensitivity is discussed in detail. PMID:24688370

Yu, Xiaobing; Cao, Jie; Shan, Haiyan; Zhu, Li; Guo, Jun

2014-01-01

23

Optimization Algorithms  

Microsoft Academic Search

\\u000a The right choice of an optimization algorithm can be crucially important in finding the right solutions for a given optimization\\u000a problem. There exist a diverse range of algorithms for optimization, including gradient-based algorithms, derivative-free\\u000a algorithms and metaheuristics. Modern metaheuristic algorithms are often nature-inspired, and they are suitable for global\\u000a optimization. In this chapter, we will briefly introduce optimization algorithms such

Xin-She Yang

24

Extended optimization for 350X zoom optics via hybrid Tagushi genetic algorithm  

NASA Astrophysics Data System (ADS)

This research proposed a new method HTGA (Hybrid Taguchi Genetic Algorithm) for extended optimization of 350X zoom optics with DOE (Diffractive Optical Element) in order to eliminate chromatic aberration efficiently. Thanks to negative Abbe number of DOE, the optimal eliminating chromatic aberration could optimized and minimized with DOE coefficient and glass material. Following the advanced technology applied to micro lens and etching process, precisely-made micro DOE element now is possible to be manufactured in a large number. The steady Taguchi method incorporated with the genetic algorithm (GA), called hybrid Taguchi-genetic algorithm (HTGA), proposed in this research, have reached success in determining the best position for DOE plane and conclusively eliminate the chromatic aberration of 350 Zoom optics with DOE element and various glass materials.

Tsai, Chen-Mu; Fang, Yi Chin; Lin, Han-Ching

2008-11-01

25

A hybrid algorithm for instant optimization of beam weights in anatomy-based intensity modulated radiotherapy: A performance evaluation study  

PubMed Central

The study aims to introduce a hybrid optimization algorithm for anatomy-based intensity modulated radiotherapy (AB-IMRT). Our proposal is that by integrating an exact optimization algorithm with a heuristic optimization algorithm, the advantages of both the algorithms can be combined, which will lead to an efficient global optimizer solving the problem at a very fast rate. Our hybrid approach combines Gaussian elimination algorithm (exact optimizer) with fast simulated annealing algorithm (a heuristic global optimizer) for the optimization of beam weights in AB-IMRT. The algorithm has been implemented using MATLAB software. The optimization efficiency of the hybrid algorithm is clarified by (i) analysis of the numerical characteristics of the algorithm and (ii) analysis of the clinical capabilities of the algorithm. The numerical and clinical characteristics of the hybrid algorithm are compared with Gaussian elimination method (GEM) and fast simulated annealing (FSA). The numerical characteristics include convergence, consistency, number of iterations and overall optimization speed, which were analyzed for the respective cases of 8 patients. The clinical capabilities of the hybrid algorithm are demonstrated in cases of (a) prostate and (b) brain. The analyses reveal that (i) the convergence speed of the hybrid algorithm is approximately three times higher than that of FSA algorithm; (ii) the convergence (percentage reduction in the cost function) in hybrid algorithm is about 20% improved as compared to that in GEM algorithm; (iii) the hybrid algorithm is capable of producing relatively better treatment plans in terms of Conformity Index (CI) [~ 2% - 5% improvement] and Homogeneity Index (HI) [~ 4% - 10% improvement] as compared to GEM and FSA algorithms; (iv) the sparing of organs at risk in hybrid algorithm-based plans is better than that in GEM-based plans and comparable to that in FSA-based plans; and (v) the beam weights resulting from the hybrid algorithm are about 20% smoother than those obtained in GEM and FSA algorithms. In summary, the study demonstrates that hybrid algorithms can be effectively used for fast optimization of beam weights in AB-IMRT. PMID:21731224

Vaitheeswaran, Ranganathan; Sathiya, Narayanan V. K.; Bhangle, Janhavi R.; Nirhali, Amit; Kumar, Namita; Basu, Sumit; Maiya, Vikram

2011-01-01

26

An Effective Hybrid Firefly Algorithm with Harmony Search for Global Numerical Optimization  

PubMed Central

A hybrid metaheuristic approach by hybridizing harmony search (HS) and firefly algorithm (FA), namely, HS/FA, is proposed to solve function optimization. In HS/FA, the exploration of HS and the exploitation of FA are fully exerted, so HS/FA has a faster convergence speed than HS and FA. Also, top fireflies scheme is introduced to reduce running time, and HS is utilized to mutate between fireflies when updating fireflies. The HS/FA method is verified by various benchmarks. From the experiments, the implementation of HS/FA is better than the standard FA and other eight optimization methods. PMID:24348137

Guo, Lihong; Wang, Gai-Ge; Wang, Heqi; Wang, Dinan

2013-01-01

27

An effective hybrid firefly algorithm with harmony search for global numerical optimization.  

PubMed

A hybrid metaheuristic approach by hybridizing harmony search (HS) and firefly algorithm (FA), namely, HS/FA, is proposed to solve function optimization. In HS/FA, the exploration of HS and the exploitation of FA are fully exerted, so HS/FA has a faster convergence speed than HS and FA. Also, top fireflies scheme is introduced to reduce running time, and HS is utilized to mutate between fireflies when updating fireflies. The HS/FA method is verified by various benchmarks. From the experiments, the implementation of HS/FA is better than the standard FA and other eight optimization methods. PMID:24348137

Guo, Lihong; Wang, Gai-Ge; Wang, Heqi; Wang, Dinan

2013-01-01

28

Hybrid ant colony-genetic algorithm (GAAPI) for global continuous optimization.  

PubMed

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 optimization algorithms) 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

Ciornei, Irina; Kyriakides, Elias

2012-02-01

29

A Novel Hybrid Crossover based Artificial Bee Colony Algorithm for Optimization Problem  

NASA Astrophysics Data System (ADS)

Artificial bee colony (ABC) algorithm has proved its importance in solving a number of problems including engineering optimization problems. ABC algorithm is one of the most popular and youngest member of the family of population based nature inspired meta-heuristic swarm intelligence method. ABC has been proved its superiority over some other Nature Inspired Algorithms (NIA) when applied for both benchmark functions and real world problems. The performance of search process of ABC depends on a random value which tries to balance exploration and exploitation phase. In order to increase the performance it is required to balance the exploration of search space and exploitation of optimal solution of the ABC. This paper outlines a new hybrid of ABC algorithm with Genetic Algorithm. The proposed method integrates crossover operation from Genetic Algorithm (GA) with original ABC algorithm. The proposed method is named as Crossover based ABC (CbABC). The CbABC strengthens the exploitation phase of ABC as crossover enhances exploration of search space. The CbABC tested over four standard benchmark functions and a popular continuous optimization problem.

Kumar, Sandeep; Kumar Sharma, Vivek; Kumari, Rajani

2013-11-01

30

Multidisciplinary design and optimization of an air launched satellite launch vehicle using a hybrid heuristic search algorithm  

Microsoft Academic Search

A multidisciplinary design and optimization strategy for a multistage air launched satellite launch vehicle comprising of a solid propulsion system to low earth orbit with the implementation of a hybrid heuristic search algorithm is proposed in this article. The proposed approach integrated the search properties of a genetic algorithm and simulated annealing, thus achieving an optimal solution while satisfying the

A. F. Rafique; L. S. He; Q. Zeeshan; A. Kamran; K. Nisar

2011-01-01

31

Optimization of optics with micro diffractive optical element via a hybrid Taguchi genetic algorithm  

NASA Astrophysics Data System (ADS)

This paper proposes a new method for optimization optics with a diffractive optical element (DOE) via a Hybrid Taguchi Genetic Algorithm. A Diffractive Optical Element, based the theory of wave phase difference, takes advantage of the negative Abbe number which might significantly eliminate the axial chromatic aberrations of optics. Following the advanced technology applied to the micro lens and etching process, precisely-made micro DOEs can now be manufactured in large numbers. However, traditional least damping square has its limitations for the optimization of axial and chromatic aberrations with DOE. In this research, we adopted the genetic algorithm (GA) and incorporated the steady Taguchi method into GA. Combining the two methods produced a new hybrid Taguchi-genetic algorithm (HTGA). Suitable glass combinations and DOE positions were selected to minimize both axial and lateral chromatic aberration in the optical system. This new method carries out the task of eliminating both axial and lateral chromatic aberration, unlike DOE optimization by LDS, which works for axial aberration only and with less efficiency. Experiments show that the surface position of the DOE could be determined first; in addition, regardless of whether chromatic aberration was axial or longitudinal, issues concerning the optical lens's chromatic aberration could be significantly reduced, compared to results from the traditional least damping square (LDS) method.

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

2009-08-01

32

Hybrid Genetic Algorithms: A Review  

Microsoft Academic Search

Hybrid genetic algorithms have received significant interest in recent years and are being increasingly used to solve real-world problems. A genetic algorithm is able to incorporate other techniques within its framework to produce a hybrid that reaps the best from the combination. In this paper, different forms of integration between genetic algorithms and other search and optimization techniques are reviewed.

Tarek A. El-mihoub; Adrian A. Hopgood; Lars Nolle; Alan Battersby

2006-01-01

33

ON THE USE OF GENETIC ALGORITHM TO OPTIMIZE THE ON BOARD ENERGY MANAGEMENT OF A HYBRID SOLAR VEHICLE  

Microsoft Academic Search

ON THE USE OF GENETIC ALGORITHM TO OPTIMIZE THE ON-BOARD ENERGY MANAGEMENT OF A HYBRID SOLAR VEHICLE — This paper deals with the development of a prototype of Hybrid Solar Vehicle (HSV) with series structure. This activity has been also conducted in the framework of the EU funded Leonardo project \\

Ivan Arsie; Gianfranco Rizzo; Marco Sorrentino

2008-01-01

34

A hybrid multi-objective evolutionary algorithm for optimal groundwater management under variable density conditions  

NASA Astrophysics Data System (ADS)

In this study, a new hybrid multi-objective evolutionary algorithm (MOEA), the niched Pareto tabu search combined with a genetic algorithm (NPTSGA), is proposed for the management of groundwater resources under variable density conditions. Relatively few MOEAs can possess global search ability contenting with intensified search in local area. Moreover, the overall searching ability of tabu search (TS) based MOEAs is very sensitive to neighborhood step size. The NPTSGA is developed on the thought of integrating genetic algorithm (GA) with a TS based MOEA, niched Pareto tabu search (NPTS), which helps to alleviate both of the above difficulties. Here, the global search ability of the NPTS is improved by the diversification of candidate solutions arose from the evolving genetic algorithm population. Furthermore, the proposed methodology coupled with a density-dependent groundwater flow and solute transport simulator, SEAWAT, is developed and its performance is evaluated through a synthetic seawater intrusion management problem. Optimization results indicate that the NPTSGA offers a tradeoff between the two conflicting objectives. A key conclusion of this study is that the NPTSGA can balance the tradeoff between the intensification of nondomination and the diversification of near Pareto-optimal solutions and is a stable and robust method for implementing the multi-objective design of variable-density groundwater resources.

Wu, J.; Yang, Y.; Wu, J.

2011-12-01

35

Optimal design of segmented quasi-phase-matching SHG+DFG wavelength conversion structure based on hybrid genetic algorithm  

NASA Astrophysics Data System (ADS)

A hybrid genetic algorithm is proposed to optimal design the wavelength converter which using segmented grating structure and cascaded second-harmonic generation and difference-frequency generation process. Investigation of the influences of the structure parameters on conversion bandwidth and conversion response are carried out. High conversion efficiency, flat response and broad conversion bandwidth can be obtained simultaneously, by adding the segment number of QPM grating and optimizing the poling period of each segment. The utilizing of the hybrid genetic algorithm can not only make one obtain precise optimal results, but also shorten the simulation time significantly, so it is helpful to the practical design of the wavelength converters.

Liu, Lao; Cui, Jie

2014-08-01

36

[Research on and application of hybrid optimization algorithm in Brillouin scattering spectrum parameter extraction problem].  

PubMed

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

Zhang, Yan-jun; Zhang, Shu-guo; Fu, Guang-wei; Li, Da; Liu, Yin; Bi, Wei-hong

2012-04-01

37

A hybrid of genetic algorithm and particle swarm optimization for recurrent network design.  

PubMed

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

Juang, Chia-Feng

2004-04-01

38

A hybrid algorithm optimization approach for machine loading problem in flexible manufacturing system  

NASA Astrophysics Data System (ADS)

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.

Kumar, Vijay M.; Murthy, ANN; Chandrashekara, K.

2012-05-01

39

Optimization by hybridization of a genetic algorithm with constraint satisfaction techniques  

Microsoft Academic Search

The authors introduce a new optimization method based on a genetic algorithm (GA) mixed with constraint satisfaction problem (CSP) techniques. The approach is designed for combinatorial problems whose search spaces are too large and\\/or objective functions too complex for usual CSP techniques and whose constraints are too complex for conventional genetic algorithm. The main idea is the handling of sub-domains

Nicolas Barnier; Pascal Brisset

1998-01-01

40

A hybrid of particle swarm optimization and genetic algorithm for multicarrier Cognitive Radio  

Microsoft Academic Search

Cognitive radio (CR) has become a hotspot in recent research. We can think of a CR as having three main parts: the ability to sense, the capacity to learn, and the capability to adapt. Adaptation to the outside environment to optimize radio parameters has been previously proposed using genetic algorithms (GA) to select the optimal transmission parameters by scoring a

Said E. El-Khamy; Mohamed A. Aboul-Dahab; Mohamed M. Attia

2009-01-01

41

A Hybrid Dynamic/Quadratic Programming Algorithm for Interconnect Tree Optimization  

E-print Network

in determining the system performance of the 0.25 m technology. Even with the help of copper and low dielectric constant () materials, interconnect delay is still likely to dominate the chip performance beyond 0.18 m/Quadratic Programming (DQP) to refer to our hybrid algorithm. In addition, we present a constant reusing technique

Chu, Chris C.-N.

42

Optimal Design of Sewer Networks using hybrid cellular automata and genetic algorithm  

Microsoft Academic Search

Optimal sewer design aims to minimize capital investment on infrastructure whilst ensuring a good system performance under specific design criteria. One of the state-of-the-art optimization techniques for this problem is the Genetic Algorithm (GA), which is commonly combined with a sewer hydraulic simulator during the optimization. However, this approach can be prohibitively time-consuming especially for designing large networks. Firstly, GAs

Yufeng Guo; Godfrey Walters; Soon-Thiam Khu; Edward Keedwell

2006-01-01

43

Hybridization of genetic algorithm with immune system for optimization problems in structural engineering  

Microsoft Academic Search

Optimization is the task of getting the best solution among the feasible solutions. There are many methods available to obtain\\u000a an optimized solution. Genetic algorithm (GA), which is a heuristic type of optimization method, is discussed in this paper.\\u000a The focus of the paper is the use of GA for large dimensionality design problems, where computational efficiency is a major

S. Rajasekaran; S. Lavanya

2007-01-01

44

A hybrid discrete particle swarm optimization algorithm for the no-wait flow shop scheduling problem with makespan criterion  

Microsoft Academic Search

This paper proposes a novel hybrid discrete particle swarm optimization (HDPSO) algorithm to solve the no-wait flow shop scheduling\\u000a problems with the criterion to minimize the maximum completion time (makespan). Firstly, a simple approach is presented in\\u000a the paper to calculate the makespan of a job permutation. Secondly, a speed-up method is proposed to evaluate the similar\\u000a insert neighborhood solution.

Quan-Ke Pan; Ling Wang; M. Fatih Tasgetiren; Bao-Hua Zhao

2008-01-01

45

HS-LS-CD Hybrid Conjugate Gradient Algorithm for Unconstrained Optimization  

Microsoft Academic Search

Conjugate gradient methods are important for large-scale unconstrained optimization. In this paper, we propose anew formula ??k for unconstrained optimization, which is the hybrid from HS method, LS method and CD method. From the construction of the new formula ??k, we use a direction which is different from traditional dk. The direction satisfies descent conditions naturally. And dk Tgk=-¿gk¿2 depends

Hui Yan; Lanping Chen; Baocong Jiao

2009-01-01

46

A Hybrid Method of Genetic Algorithms and Ant Colony Optimization to Solve the Traveling Salesman Problem  

Microsoft Academic Search

A new hybrid method iterative extended changing crossover operators which can efficiently obtain the optimum solution of the traveling salesman problem through flexibly alternating ant colony optimization (ACO) which simulates process of learning swarm intelligence in ants' feeding behavior and edge assembly crossover (EAX) which has been recently noticed as an available method for efficient selection of optimum solution with

Ryouei Takahashi

2009-01-01

47

A hybrid evolutionary algorithm for multi-objective anatomy-based dose optimization in high-dose-rate brachytherapy  

NASA Astrophysics Data System (ADS)

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 optimization algorithm NSGA-II. This, in combination with the deterministic optimization algorithm, 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.

Lahanas, M.; Baltas, D.; Zamboglou, N.

2003-02-01

48

A hybrid evolutionary algorithm for multi-objective anatomy-based dose optimization in high-dose-rate brachytherapy.  

PubMed

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 optimization algorithm NSGA-II. This, in combination with the deterministic optimization algorithm, 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

Lahanas, M; Baltas, D; Zamboglou, N

2003-02-01

49

Optimization process planning using hybrid genetic algorithm and intelligent search for job shop machining  

PubMed Central

Optimization of process planning is considered as the key technology for computer-aided process planning which is a rather complex and difficult procedure. A good process plan of a part is built up based on two elements: (1) the optimized sequence of the operations of the part; and (2) the optimized selection of the machine, cutting tool and Tool Access Direction (TAD) for each operation. In the present work, the process planning is divided into preliminary planning, and secondary/detailed planning. In the preliminary stage, based on the analysis of order and clustering constraints as a compulsive constraint aggregation in operation sequencing and using an intelligent searching strategy, the feasible sequences are generated. Then, in the detailed planning stage, using the genetic algorithm which prunes the initial feasible sequences, the optimized operation sequence and the optimized selection of the machine, cutting tool and TAD for each operation based on optimization constraints as an additive constraint aggregation are obtained. The main contribution of this work is the optimization of sequence of the operations of the part, and optimization of machine selection, cutting tool and TAD for each operation using the intelligent search and genetic algorithm simultaneously. PMID:21845020

Salehi, Mojtaba

2010-01-01

50

Hybrid-optimization algorithm for the management of a conjunctive-use project and well field design  

USGS Publications Warehouse

Hi-Desert Water District (HDWD), the primary water-management agency in the Warren Groundwater Basin, California, plans to construct a waste water treatment plant to reduce future septic-tank effluent from reaching the groundwater system. The treated waste water will be reclaimed by recharging the groundwater basin via recharge ponds as part of a larger conjunctive-use strategy. HDWD wishes to identify the least-cost conjunctive-use strategies for managing imported surface water, reclaimed water, and local groundwater. As formulated, the mixed-integer nonlinear programming (MINLP) groundwater-management problem seeks to minimize water-delivery costs subject to constraints including potential locations of the new pumping wells, California State regulations, groundwater-level constraints, water-supply demand, available imported water, and pump/recharge capacities. In this study, a hybrid-optimization algorithm, which couples a genetic algorithm and successive-linear programming, is developed to solve the MINLP problem. The algorithm was tested by comparing results to the enumerative solution for a simplified version of the HDWD groundwater-management problem. The results indicate that the hybrid-optimization algorithm can identify the global optimum. The hybrid-optimization algorithm is then applied to solve a complex groundwater-management problem. Sensitivity analyses were also performed to assess the impact of varying the new recharge pond orientation, varying the mixing ratio of reclaimed water and pumped water, and varying the amount of imported water available. The developed conjunctive management model can provide HDWD water managers with information that will improve their ability to manage their surface water, reclaimed water, and groundwater resources. ?? 2011, National Ground Water Association.

Chiu, Y.-C.; Nishikawa, T.; Martin, P.

2012-01-01

51

Broadband and broad-angle low-scattering metasurface based on hybrid optimization algorithm.  

PubMed

A broadband and broad-angle low-scattering metasurface is designed, fabricated, and characterized. Based on the optimization algorithm and far-field scattering pattern analysis, we propose a rapid and efficient method to design metasurfaces, which avoids the large amount of time-consuming electromagnetic simulations. Full-wave simulation and measurement results show that the proposed metasurface is insensitive to the polarization of incident waves, and presents good scattering-reduction properties for oblique incident waves. PMID:25089367

Wang, Ke; Zhao, Jie; Cheng, Qiang; Dong, Di Sha; Cui, Tie Jun

2014-01-01

52

Extended optimization of chromatic aberrations via a hybrid Taguchi-genetic algorithm for zoom optics with a diffractive optical element  

NASA Astrophysics Data System (ADS)

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

2009-04-01

53

GAHC: Hybrid Genetic Algorithm  

NASA Astrophysics Data System (ADS)

This paper introduces a novel improved evolutionary algorithm, which combines genetic algorithms and hill climbing. Genetic Algorithms (GA) belong to a class of well established optimization meta-heuristics and their behavior are studied and analyzed in great detail. Various modifications were proposed by different researchers, for example modifications to the mutation operator. These modifications usually change the overall behavior of the algorithm. This paper presents a binary GA with a modified mutation operator, which is based on the well-known Hill Climbing Algorithm (HCA). The resulting algorithm, referred to as GAHC, also uses an elite tournament selection operator. This selection operator preserves the best individual from the GA population during the selection process while maintaining the positive characteristics of the standard tournament selection. This paper discusses the GAHC algorithm and compares its performance with standard GA.

Matousek, Radomil

54

Protein Tertiary Structure Prediction Based on Main Chain Angle Using a Hybrid Bees Colony Optimization Algorithm  

NASA Astrophysics Data System (ADS)

Encoding proteins of amino acid sequence to predict classified into their respective families and subfamilies is important research area. However for a given protein, knowing the exact action whether hormonal, enzymatic, transmembranal or nuclear receptors does not depend solely on amino acid sequence but on the way the amino acid thread folds as well. This study provides a prototype system that able to predict a protein tertiary structure. Several methods are used to develop and evaluate the system to produce better accuracy in protein 3D structure prediction. The Bees Optimization algorithm which inspired from the honey bees food foraging method, is used in the searching phase. In this study, the experiment is conducted on short sequence proteins that have been used by the previous researches using well-known tools. The proposed approach shows a promising result.

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

55

Gradient, Non-Gradient and Hybrid Algorithms for Optimizing 3D Forging Sequences with Uncertainties  

E-print Network

Evolutionary Algorithms, such as Genetic Algorithms, or is sequentially improved until finding a satisfactory limited here to 50. Under these circumstances, several authors have used gradient algorithms. The computational cost of raw "global" algorithms, such as Genetic Algorithms (GA), is generally a curb on their use

Paris-Sud XI, Université de

56

A new fuzzy adaptive hybrid particle swarm optimization algorithm for non-linear, non-smooth and non-convex economic dispatch problem  

Microsoft Academic Search

Economic dispatch (ED) plays an important role in power system operation. ED problem is a non-smooth and non-convex problem when valve-point effects of generation units are taken into account. This paper presents an efficient hybrid evolutionary approach for solving the ED problem considering the valve-point effect. The proposed algorithm combines a fuzzy adaptive particle swarm optimization (FAPSO) algorithm with Nelder–Mead

Taher Niknam

2010-01-01

57

Optimization process planning using hybrid genetic algorithm and intelligent search for job shop machining  

Microsoft Academic Search

Optimization of process planning is considered as the key technology for computer-aided process planning which is a rather\\u000a complex and difficult procedure. A good process plan of a part is built up based on two elements: (1) the optimized sequence\\u000a of the operations of the part; and (2) the optimized selection of the machine, cutting tool and Tool Access Direction

Mojtaba Salehi; Ardeshir Bahreininejad

58

Application of a hybrid genetic algorithm to airline crew scheduling  

Microsoft Academic Search

This paper discusses the development and application of a hybrid genetic algorithmto airline crew scheduling problems. The hybrid algorithm consists of a steady-stategenetic algorithm and a local search heuristic. The hybrid algorithm was tested on a set of fortyreal-world problems. It found the optimal solution for half the problems, and good solutionsfor nine others. The results were compared to those

David Levine

1996-01-01

59

Application of Evolutionary and Hybrid Algorithms to Optimize Investments Strategies in Large Power Plants  

Microsoft Academic Search

This article is an extension of the work presented earlier, which compared and analyzed the economics of alternative maintenance plans. The proposed model combines genetic algorithms with Monte Carlo simulation to arrive at the most economic investment timing. The approach described earlier was characterized by a very long computing time making it difficult to use. This paper addresses several issues

Bartosz Sakowicz; George J. Anders; M. Kaminski; Andrzej Napieralski

2008-01-01

60

The Hybrid System of Genetic Algorithms for The Group Optimization Problem  

Microsoft Academic Search

Genetic algorithms are group optimisation techniques which can handle multiple objective functions simultaneously. Their concept derives from an analogy with the natural process of evolution and they can achieve global optimisation in most situations. Mixed-production is a complicated production method which handles multiple products in the same process simultaneously. Multiple control charts are used in order to control the quality

Chen-Fang Tsai

2000-01-01

61

Firefly Algorithms for Multimodal Optimization  

Microsoft Academic Search

Nature-inspired algorithms are among the most powerful algorithms for optimization. This paper intends to provide a detailed description of a new Firefly Algorithm (FA) for multimodal optimization applications. We will compare the proposed firefly algorithm with other metaheuristic algorithms such as particle swarm optimization (PSO). Simulations and results indicate that the proposed firefly algorithm is superior to existing metaheuristic algorithms.

Xin-She Yang

2010-01-01

62

HOPSPACK: Hybrid Optimization Parallel Search Package.  

SciTech Connect

In this paper, we describe the technical details of HOPSPACK (Hybrid Optimization Parallel SearchPackage), a new software platform which facilitates combining multiple optimization routines into asingle, tightly-coupled, hybrid algorithm that supports parallel function evaluations. The frameworkis designed such that existing optimization source code can be easily incorporated with minimalcode modification. By maintaining the integrity of each individual solver, the strengths and codesophistication of the original optimization package are retained and exploited.4

Gray, Genetha A.; Kolda, Tamara G.; Griffin, Joshua; Taddy, Matt; Martinez-Canales, Monica

2008-12-01

63

Memetic firefly algorithm for combinatorial optimization  

E-print Network

Firefly algorithms belong to modern meta-heuristic algorithms inspired by nature that can be successfully applied to continuous optimization problems. In this paper, we have been applied the firefly algorithm, hybridized with local search heuristic, to combinatorial optimization problems, where we use graph 3-coloring problems as test benchmarks. The results of the proposed memetic firefly algorithm (MFFA) were compared with the results of the Hybrid Evolutionary Algorithm (HEA), Tabucol, and the evolutionary algorithm with SAW method (EA-SAW) by coloring the suite of medium-scaled random graphs (graphs with 500 vertices) generated using the Culberson random graph generator. The results of firefly algorithm were very promising and showed a potential that this algorithm could successfully be applied in near future to the other combinatorial optimization problems as well.

Fister, Iztok; Fister, Iztok; Brest, Janez

2012-01-01

64

GPU-based Parallel Hybrid Genetic Algorithms  

E-print Network

Abstract—Over the last years, interest in hybrid metaheuristics has risen considerably in the field of optimization. Combinations of algorithms such as genetic algorithms (GAs) and local search (LS) methods have provided very powerful search algorithms. However, due to their complexity, the computational time of the solution search exploration remains exorbitant when large problem instances are to be solved. Therefore, the use of GPU-based parallel computing is required as a complementary way to speed up the search. This paper presents a new methodology to design and implement efficiently and effectively hybrid genetic algorithms on GPU accelerators. I. SCHEME OF PARALLELIZATION The adaptation of hybrid GAs on GPU requires to take into account at the same time the characteristics and underlined issues of the GPU architecture and the metaheuristics parallel models. Since the evaluation of the neighborhood is generally the time-consuming

Thé Van Luong; Nouredine Melab; El-ghazali Talbi

65

Firefly Algorithms for Multimodal Optimization  

E-print Network

Nature-inspired algorithms are among the most powerful algorithms for optimization. This paper intends to provide a detailed description of a new Firefly Algorithm (FA) for multimodal optimization applications. We will compare the proposed firefly algorithm with other metaheuristic algorithms such as particle swarm optimization (PSO). Simulations and results indicate that the proposed firefly algorithm is superior to existing metaheuristic algorithms. Finally we will discuss its applications and implications for further research.

Yang, Xin-She

2010-01-01

66

Study on the Convergence of Hybrid Ant Colony Algorithm for Job Shop Scheduling Problems  

Microsoft Academic Search

To improve the performance of intelligence optimization algorithm for solving Job Shop Scheduling Problem ,a hybrid ant colony algorithm called tabu search and ant (TSANT) algorithm with global convergence was proposed. In the hybrid ant colony algorithm, the MMAS algorithm was applied to search in the global solution space, and the tabu search algorithm was utilized as the local algorithm.

Xiaoyu Song; Lihua Sun

2010-01-01

67

Firefly Algorithms for Multimodal Optimization  

Microsoft Academic Search

Nature-inspired algorithms are among the most powerful algorithms for\\u000aoptimization. This paper intends to provide a detailed description of a new\\u000aFirefly Algorithm (FA) for multimodal optimization applications. We will\\u000acompare the proposed firefly algorithm with other metaheuristic algorithms such\\u000aas particle swarm optimization (PSO). Simulations and results indicate that the\\u000aproposed firefly algorithm is superior to existing metaheuristic algorithms.

Xin-she Yang

2009-01-01

68

Hybrid particle swarm optimization and convergence analysis for scheduling problems  

Microsoft Academic Search

This paper proposes a hybrid particle swarm optimization algorithm and for solving Flow Shop Scheduling Problems (FSSP) and Job Shop Scheduling Problems (JSSP) to minimize the maximum makespan. A new hybrid heuristic, based on Particle Swarm Optimization (PSO), Tabu Search (TS) and Simulated Annealing (SA), is presented. By reasonably combining these three different search algorithms, we develop a robust, fast

Xue-Feng Zhang; Miyuki Koshimura; Hiroshi Fujita; Ryuzo Hasegawa

2012-01-01

69

A hybrid algorithm for the design of diffractive optical element for beam shaping  

NASA Astrophysics Data System (ADS)

A hybrid algorithm based on the simulated annealing algorithm and the iterative algorithm is proposed for the design of diffractive optical element (DOE) to shape the laser beams. The algorithm has the global optimization ability of simulated annealing algorithm as well as the local optimization ability of iterative algorithm. Comparisons between the hybrid algorithm and other two optimization algorithms show that the hybrid algorithm has satisfactory convergence property and design accuracy. Numerical simulation results demonstrate that the diffraction efficiency of the DOE is higher than 94% and non-uniformity is less than 1%. Therefore, this algorithm can be well applied in the field of beam shaping.

Yin, Kewei; Huang, Zhiqiang; Lin, Wumei; Xing, Tingwen

2014-08-01

70

An Algorithmic Framework for Multiobjective Optimization  

PubMed Central

Multiobjective (MO) optimization is an emerging field which is increasingly being encountered in many fields globally. Various metaheuristic techniques such as differential evolution (DE), genetic algorithm (GA), gravitational search algorithm (GSA), and particle swarm optimization (PSO) have been used in conjunction with scalarization techniques such as weighted sum approach and the normal-boundary intersection (NBI) method to solve MO problems. Nevertheless, many challenges still arise especially when dealing with problems with multiple objectives (especially in cases more than two). In addition, problems with extensive computational overhead emerge when dealing with hybrid algorithms. This paper discusses these issues by proposing an alternative framework that utilizes algorithmic concepts related to the problem structure for generating efficient and effective algorithms. This paper proposes a framework to generate new high-performance algorithms with minimal computational overhead for MO optimization. PMID:24470795

Ganesan, T.; Elamvazuthi, I.; Shaari, Ku Zilati Ku; Vasant, P.

2013-01-01

71

An algorithmic framework for multiobjective optimization.  

PubMed

Multiobjective (MO) optimization is an emerging field which is increasingly being encountered in many fields globally. Various metaheuristic techniques such as differential evolution (DE), genetic algorithm (GA), gravitational search algorithm (GSA), and particle swarm optimization (PSO) have been used in conjunction with scalarization techniques such as weighted sum approach and the normal-boundary intersection (NBI) method to solve MO problems. Nevertheless, many challenges still arise especially when dealing with problems with multiple objectives (especially in cases more than two). In addition, problems with extensive computational overhead emerge when dealing with hybrid algorithms. This paper discusses these issues by proposing an alternative framework that utilizes algorithmic concepts related to the problem structure for generating efficient and effective algorithms. This paper proposes a framework to generate new high-performance algorithms with minimal computational overhead for MO optimization. PMID:24470795

Ganesan, T; Elamvazuthi, I; Shaari, Ku Zilati Ku; Vasant, P

2013-01-01

72

A hybrid genetic algorithm for synthesis of heat exchanger networks  

Microsoft Academic Search

A new hybrid genetic algorithm for optimal design of heat exchanger networks is developed. The mathematical model used in the algorithm is based on an explicit solution of stream temperatures of heat exchanger networks with the stage-wise superstructure. By taking heat transfer areas and heat capacity flow rates as genes in the genetic algorithm, the thermal performance and total cost

Xing Luo; Qing-Yun Wen; Georg Fieg

2009-01-01

73

A hybrid genetic algorithm for resolving closely spaced objects  

NASA Technical Reports Server (NTRS)

A hybrid genetic algorithm is described for performing the difficult optimization task of resolving closely spaced objects appearing in space based and ground based surveillance data. This application of genetic algorithms is unusual in that it uses a powerful domain-specific operation as a genetic operator. Results of applying the algorithm to real data from telescopic observations of a star field are presented.

Abbott, R. J.; Lillo, W. E.; Schulenburg, N.

1995-01-01

74

Innovative phase unwrapping algorithm: hybrid approach  

Microsoft Academic Search

We present a novel algorithm based on a hybrid of the global and local treatment of a wrapped map. The proposed algorithm is especially effective for the unwrapping of speckle-coded interferogram contour maps. In contrast to earlier unwrapping algorithms by region, we propose a local discontinuity-restoring criterion to serve as the preprocessor or postprocessor of our hybrid algorithm, which makes

Min J. Huang; Cian-Jhih Lai

2002-01-01

75

A Hybrid Differential Invasive Weed Algorithm for Congestion Management  

NASA Astrophysics Data System (ADS)

This work is dedicated to solve the problem of congestion management in restructured power systems. Nowadays we have open access market which pushes the power system operation to their limits for maximum economic benefits but at the same time making the system more susceptible to congestion. In this regard congestion management is absolutely vital. In this paper we try to remove congestion by generation rescheduling where the cost involved in the rescheduling process is minimized. The proposed algorithm is a hybrid of Invasive Weed Optimization (IWO) and Differential Evolution (DE). The resultant hybrid algorithm was applied on standard IEEE 30 bus system and observed to beat existing algorithms like Simple Bacterial foraging (SBF), Genetic Algorithm (GA), Invasive Weed Optimization (IWO), Differential Evolution (DE) and hybrid algorithms like Hybrid Bacterial Foraging and Differential Evolution (HBFDE) and Adaptive Bacterial Foraging with Nelder Mead (ABFNM).

Basak, Aniruddha; Pal, Siddharth; Pandi, V. Ravikumar; Panigrahi, B. K.; Das, Swagatam

76

Hybrid real coded genetic algorithm solution to economic dispatch problem  

Microsoft Academic Search

This paper presents a new, two-phase hybrid real coded genetic algorithm (GA) based technique to solve economic dispatch (ED) problem with multiple fuel options. The proposed hybrid scheme is developed in such a way that a simple real coded GA is acting as a base level search, which makes a quick decision to direct the search towards the optimal region,

S. Baskar; P. Subbaraj; M. V. C. Rao

2003-01-01

77

A hybrid of bees algorithm and flux balance analysis with OptKnock as a platform for in silico optimization of microbial strains.  

PubMed

Microbial strain optimization focuses on improving technological properties of the strain of microorganisms. However, the complexities of the metabolic networks, which lead to data ambiguity, often cause genetic modification on the desirable phenotypes difficult to predict. Furthermore, vast number of reactions in cellular metabolism lead to the combinatorial problem in obtaining optimal gene deletion strategy. Consequently, the computation time increases exponentially with the increase in the size of the problem. Hence, we propose an extension of a hybrid of Bees Algorithm and Flux Balance Analysis (BAFBA) by integrating OptKnock into BAFBA to validate the result. This paper presents a number of computational experiments to test on the performance and capability of BAFBA. Escherichia coli, Bacillus subtilis and Clostridium thermocellum are the model organisms in this paper. Also included is the identification of potential reactions to improve the production of succinic acid, lactic acid and ethanol, plus the discussion on the changes in the flux distribution of the predicted mutants. BAFBA shows potential in suggesting the non-intuitive gene knockout strategies and a low variability among the several runs. The results show that BAFBA is suitable, reliable and applicable in predicting optimal gene knockout strategy. PMID:23892659

Choon, Yee Wen; Mohamad, Mohd Saberi; Deris, Safaai; Illias, Rosli Md; Chong, Chuii Khim; Chai, Lian En

2014-03-01

78

Ant Algorithms for Discrete Optimization  

E-print Network

. Ants can smell pheromone and, when choosing their way, they tend to choose, in probability, pathsAnt Algorithms for Discrete Optimization Marco Dorigo and Gianni Di Caro IRIDIA, Universit#19;e, Switzerland luca@idsia.ch Abstract This paper overviews recent work on ant algorithms, that is, algorithms

Ducatelle, Frederick

79

Ant Algorithms for Discrete Optimization  

E-print Network

called pheromone, forming in this way a pheromone trail. Ants can smell pheromone and, when choosingAnt Algorithms for Discrete Optimization Marco Dorigo and Gianni Di Caro IRIDIA, Universit´e Libre, Switzerland luca@idsia.ch Abstract This paper overviews recent work on ant algorithms, that is, algorithms

Gambardella, Luca Maria

80

Strength Pareto particle swarm optimization and hybrid EA-PSO for multi-objective optimization.  

PubMed

This paper proposes an efficient particle swarm optimization (PSO) technique that can handle multi-objective optimization problems. It is based on the strength Pareto approach originally used in evolutionary algorithms (EA). The proposed modified particle swarm algorithm is used to build three hybrid EA-PSO algorithms to solve different multi-objective optimization problems. This algorithm and its hybrid forms are tested using seven benchmarks from the literature and the results are compared to the strength Pareto evolutionary algorithm (SPEA2) and a competitive multi-objective PSO using several metrics. The proposed algorithm shows a slower convergence, compared to the other algorithms, but requires less CPU time. Combining PSO and evolutionary algorithms leads to superior hybrid algorithms that outperform SPEA2, the competitive multi-objective PSO (MO-PSO), and the proposed strength Pareto PSO based on different metrics. PMID:20064026

Elhossini, Ahmed; Areibi, Shawki; Dony, Robert

2010-01-01

81

Hybrid Ant Algorithm and Applications for Vehicle Routing Problem  

NASA Astrophysics Data System (ADS)

Ant colony optimization (ACO) is a metaheuristic method that inspired by the behavior of real ant colonies. ACO has been successfully applied to several combinatorial optimization problems, but it has some short-comings like its slow computing speed and local-convergence. For solving Vehicle Routing Problem, we proposed Hybrid Ant Algorithm (HAA) in order to improve both the performance of the algorithm and the quality of solutions. The proposed algorithm took the advantages of Nearest Neighbor (NN) heuristic and ACO for solving VRP, it also expanded the scope of solution space and improves the global ability of the algorithm through importing mutation operation, combining 2-opt heuristics and adjusting the configuration of parameters dynamically. Computational results indicate that the hybrid ant algorithm can get optimal resolution of VRP effectively.

Xiao, Zhang; Jiang-qing, Wang

82

Multi-swingby optimization of mission to Saturn using global optimization algorithms  

NASA Astrophysics Data System (ADS)

Based on the trajectory design of a mission to Saturn, this paper discusses four different trajectories in various swingby cases. We assume a single impulse to be applied in each case when the spacecraft approaches a celestial body. Some optimal trajectories of EJS, EMS, EVEJS and EVVEJS flying sequences are obtained using five global optimization algorithms: DE, PSO, DP, the hybrid algorithm PSODE and another hybrid algorithm, DPDE. DE is proved to be superior to other non-hybrid algorithms in the trajectory optimization problem. The hybrid algorithm of PSO and DE can improve the optimization performance of DE, which is validated by the mission to Saturn with given swingby sequences. Finally, the optimization results of four different swingby sequences are compared with those of the ACT of ESA.

Zhu, Kaijian; Li, Junfeng; Baoyin, Hexi

2009-12-01

83

Improved Reservoir Operation Using Hybrid Genetic Algorithm and Neurofuzzy Computing  

Microsoft Academic Search

A hybrid genetic and neurofuzzy computing algorithm was developed to enhance efficiency of water management for a multipurpose\\u000a reservoir system. The genetic algorithm was applied to search for the optimal input combination of a neurofuzzy system. The\\u000a optimal model structure is modified using the selection index (SI) criterion expressed as the weighted combination of normalized\\u000a values of root mean square

Panuwat Pinthong; Ashim Das Gupta; Mukand Singh Babel; Sutat Weesakul

2009-01-01

84

A study of hybrid parallel genetic algorithm model  

Microsoft Academic Search

Genetic algorithms is facing the low evolution rate and difficulties to meet real-time requirements when handing large-scale combinatorial optimization problems. In this paper, we propose a coarse-grained-master-slave hybrid parallel genetic algorithm model based on multi-core cluster systems. This model integrates the message-passing model and the shared-memory model. We use message-passing model—MPI among nodes which correspond to coarse-grained Parallel Genetic Algorithm

Zhu-rong Wang; Tao Ju; Du-wu Cui; Xin-hong Hei

2011-01-01

85

5. Greedy and other efficient optimization algorithms  

E-print Network

5. Greedy and other efficient optimization algorithms David Keil Analysis of Algorithms 1/12 David algorithms 1. Optimal-substructure property 2. Greedy graph algorithms 1David Keil Analysis of Algorithms 5. Greedy algorithms 1/12 2. Greedy graph algorithms 3. Compression and packing 4. Space/time tradeoffs

Keil, David M.

86

Firefly Algorithm, Levy Flights and Global Optimization  

Microsoft Academic Search

Nature-inspired algorithms such as Particle Swarm Optimization and Firefly Algorithm are among the most powerful algorithms for optimization. In this paper, we intend to formulate a new metaheuristic algorithm by combining Levy flights with the search strategy via the Firefly Algorithm. Numerical studies and results suggest that the proposed Levy-flight firefly algorithm is superior to existing metaheuristic algorithms. Finally implications

Xin-She Yang

2010-01-01

87

Algorithms for bilevel optimization  

NASA Technical Reports Server (NTRS)

General multilevel nonlinear optimization problems arise in design of complex systems and can be used as a means of regularization for multi-criteria optimization problems. Here, for clarity in displaying our ideas, we restrict ourselves to general bi-level optimization problems, and we present two solution approaches. Both approaches use a trust-region globalization strategy, and they can be easily extended to handle the general multilevel problem. We make no convexity assumptions, but we do assume that the problem has a nondegenerate feasible set. We consider necessary optimality conditions for the bi-level problem formulations and discuss results that can be extended to obtain multilevel optimization formulations with constraints at each level.

Alexandrov, Natalia; Dennis, J. E., Jr.

1994-01-01

88

Hybrid Genetic Algorithms for Feature Selection  

Microsoft Academic Search

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

Il-Seok Oh; Jin-Seon Lee; Byung-Ro Moon

2004-01-01

89

Ant Algorithms for Discrete Optimization  

E-print Network

ant-based al- gorithms to many different discrete optimization problems [5, 21]. Recent applications. Ants can smell pheromone, and when choosing their way, they tend to choose, in probability, pathsAnt Algorithms for Discrete Optimization Marco Dorigo Gianni Di Caro IRIDIA CP 194/6 Universit

Libre de Bruxelles, Université

90

The Rational Hybrid Monte Carlo Algorithm  

E-print Network

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.

M. A. Clark

2006-10-06

91

HYBRID FAST HANKEL TRANSFORM ALGORITHM FOR ELECTROMAGNETIC MODELING  

EPA Science Inventory

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 ...

92

Constrained multiobjective biogeography optimization algorithm.  

PubMed

Multiobjective optimization involves minimizing or maximizing multiple objective functions subject to a set of constraints. In this study, a novel constrained multiobjective biogeography optimization algorithm (CMBOA) is proposed. It is the first biogeography optimization algorithm for constrained multiobjective optimization. In CMBOA, a disturbance migration operator is designed to generate diverse feasible individuals in order to promote the diversity of individuals on Pareto front. Infeasible individuals nearby feasible region are evolved to feasibility by recombining with their nearest nondominated feasible individuals. The convergence of CMBOA is proved by using probability theory. The performance of CMBOA is evaluated on a set of 6 benchmark problems and experimental results show that the CMBOA performs better than or similar to the classical NSGA-II and IS-MOEA. PMID:25006591

Mo, Hongwei; Xu, Zhidan; Xu, Lifang; Wu, Zhou; Ma, Haiping

2014-01-01

93

Fairness in optimal routing algorithms  

E-print Network

. Tsei Dr. Pierce E. Cantrell A study of fairness in multiple path optimal routing algorithms is discussed. Fair- ness measures are developed to evaluate multiple path routing in virtual circuit and datagram implementations. Several objective.... One objective function is shown to have perfect fairness for virtual circuits. The objective function optimized was shown to have little effect on the average packet delay. To my parents and my brother ACKNOWLEDGMENTS I wish to express my...

Goos, Jeffrey Alan

2012-06-07

94

Phylogenetically Acquired Representations and Hybrid Evolutionary Algorithms  

E-print Network

information by introducing inheritance of acquired traits and Horizontal Gene Transfer, a good tool for handlePhylogenetically Acquired Representations and Hybrid Evolutionary Algorithms Adrianna Wozniak to avoid the Symbol Grounding Problem. We give a definition of Phylogenetically Acquired Representations

Paris-Sud XI, Université de

95

A Winner Determination Algorithm for Combinatorial Auctions Based on Hybrid Artificial Fish Swarm Algorithm  

NASA Astrophysics Data System (ADS)

The problem of winner determination in combinatorial auctions is a hotspot electronic business, and a NP hard problem. A Hybrid Artificial Fish Swarm Algorithm(HAFSA), which is combined with First Suite Heuristic Algorithm (FSHA) and Artificial Fish Swarm Algorithm (AFSA), is proposed to solve the problem after probing it base on the theories of AFSA. Experiment results show that the HAFSA is a rapidly and efficient algorithm for The problem of winner determining. Compared with Ant colony Optimization Algorithm, it has a good performance with broad and prosperous application.

Zheng, Genrang; Lin, ZhengChun

96

Hybrid-genetic algorithm based descriptor optimization and QSAR models for predicting the biological activity of Tipranavir analogs for HIV protease inhibition.  

PubMed

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 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. PMID:20399695

Reddy, A Srinivas; Kumar, Sunil; Garg, Rajni

2010-06-01

97

Multilevel algorithms for nonlinear optimization  

NASA Technical Reports Server (NTRS)

Multidisciplinary design optimization (MDO) gives rise to nonlinear optimization problems characterized by a large number of constraints that naturally occur in blocks. We propose a class of multilevel optimization methods motivated by the structure and number of constraints and by the expense of the derivative computations for MDO. The algorithms are an extension to the nonlinear programming problem of the successful class of local Brown-Brent algorithms for nonlinear equations. Our extensions allow the user to partition constraints into arbitrary blocks to fit the application, and they separately process each block and the objective function, restricted to certain subspaces. The methods use trust regions as a globalization strategy, and they have been shown to be globally convergent under reasonable assumptions. The multilevel algorithms can be applied to all classes of MDO formulations. Multilevel algorithms for solving nonlinear systems of equations are a special case of the multilevel optimization methods. In this case, they can be viewed as a trust-region globalization of the Brown-Brent class.

Alexandrov, Natalia; Dennis, J. E., Jr.

1994-01-01

98

Ant Algorithms for Discrete Optimization  

E-print Network

a substance called pheromone, forming in this way a pheromone trail. Ants can smell pheromone, and whenAnt Algorithms for Discrete Optimization Marco Dorigo Gianni Di Caro IRIDIA CP 194/6 Universit@iridia.ulb.ac.be Luca M. Gambardella IDSIA Corso Elvezia 36 CH-6900 Lugano Switzerland luca@idsia.ch Keywords ant

Hutter, Frank

99

A new hybrid optimization method for loading pattern search  

SciTech Connect

A new hybrid optimization 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 hybrid optimization 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)

Tao, Wang [Shanghai Jiao Tong University, Shanghai 200030 (China); Zhongsheng, Xie [Xi'an Jiao Tong University, Xi'an 710049 (China)

2006-07-01

100

Hybridization of evolutionary algorithms and local search by means of a clustering method  

Microsoft Academic Search

This paper presents a hybrid evolutionary algorithm (EA) to solve nonlinear-regression problems. Although EAs have proven their ability to explore large search spaces, they are comparatively inefficient in fine tuning the solution. This drawback is usually avoided by means of local optimization algorithms that are applied to the individuals of the population. The algorithms that use local optimization procedures are

Alfonso C. Martínez-Estudillo; César Hervás-Martínez; Francisco J. Martínez-Estudillo; Nicolás García-Pedrajas

2006-01-01

101

Firefly Algorithm, Lévy Flights and Global Optimization  

Microsoft Academic Search

Nature-inspired algorithms such as Particle Swarm Optimization and Firefly\\u000aAlgorithm are among the most powerful algorithms for optimization. In this\\u000apaper, we intend to formulate a new metaheuristic algorithm by combining Levy\\u000aflights with the search strategy via the Firefly Algorithm. Numerical studies\\u000aand results suggest that the proposed Levy-flight firefly algorithm is superior\\u000ato existing metaheuristic algorithms. Finally implications

Xin-She Yang

2009-01-01

102

Firefly Algorithm, Levy Flights and Global Optimization  

E-print Network

Nature-inspired algorithms such as Particle Swarm Optimization and Firefly Algorithm are among the most powerful algorithms for optimization. In this paper, we intend to formulate a new metaheuristic algorithm by combining Levy flights with the search strategy via the Firefly Algorithm. Numerical studies and results suggest that the proposed Levy-flight firefly algorithm is superior to existing metaheuristic algorithms. Finally implications for further research and wider applications will be discussed.

Yang, Xin-She

2010-01-01

103

Firefly Algorithm, Lévy Flights and Global Optimization  

NASA Astrophysics Data System (ADS)

Nature-inspired algorithms such as Particle Swarm Optimization and Firefly Algorithm are among the most powerful algorithms for optimization. In this paper, we intend to formulate a new metaheuristic algorithm by combining Lévy flights with the search strategy via the Firefly Algorithm. Numerical studies and results suggest that the proposed Lévy-flight firefly algorithm is superior to existing metaheuristic algorithms. Finally implications for further research and wider applications will be discussed.

Yang, Xin-She

104

Optimal control for a parallel hybrid hydraulic excavator using particle swarm optimization.  

PubMed

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

Wang, Dong-yun; Guan, Chen

2013-01-01

105

Optimal Control for a Parallel Hybrid Hydraulic Excavator Using Particle Swarm Optimization  

PubMed Central

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

Wang, Dong-yun; Guan, Chen

2013-01-01

106

RH+: A Hybrid Localization Algorithm for Wireless Sensor Networks  

NASA Astrophysics Data System (ADS)

Today, localization of nodes in Wireless Sensor Networks (WSNs) is a challenging problem. Especially, it is almost impossible to guarantee that one algorithm giving optimal results for one topology will give optimal results for any other random topology. In this study, we propose a centralized, range- and anchor-based, hybrid algorithm called RH+ that aims to combine the powerful features of two orthogonal techniques: Classical Multi-Dimensional Scaling (CMDS) and Particle Spring Optimization (PSO). As a result, we find that our hybrid approach gives a fast-converging solution which is resilient to range-errors and very robust to topology changes. Across all topologies we studied, the average estimation error is less than 0.5m. when the average node density is 10 and only 2.5% of the nodes are beacons.

Basaran, Can; Baydere, Sebnem; Kucuk, Gurhan

107

A multi-objective hybrid genetic based optimization for external beam radiation.  

PubMed

A multi-objective hybrid genetic based optimization algorithm is proposed according to the multi-objective character of inverse planning. It is based on hybrid adaptive genetic algorithm, which combines the simulated annealing, uses adaptive crossover and mutation, and adopts niched tournament selection. The result of the test calculation demonstrates that excellent converge speed can be achieved using this approach. Key words- Inverse Planning Multi-objective optimization Genetic algorithm Hybrid. PMID:17282074

Guoli, Li; Gang, Song; Yican, Wu; Jian, Zhang; Qunjing, Wang

2005-01-01

108

Hybrid particle swarm optimization for hybrid flowshop scheduling problem with maintenance activities.  

PubMed

A hybrid algorithm which combines particle swarm optimization (PSO) and iterated local search (ILS) is proposed for solving the hybrid flowshop scheduling (HFS) problem with preventive maintenance (PM) activities. In the proposed algorithm, different crossover operators and mutation operators are investigated. In addition, an efficient multiple insert mutation operator is developed for enhancing the searching ability of the algorithm. Furthermore, an ILS-based local search procedure is embedded in the algorithm to improve the exploitation ability of the proposed algorithm. The detailed experimental parameter for the canonical PSO is tuning. The proposed algorithm is tested on the variation of 77 Carlier and Néron's benchmark problems. Detailed comparisons with the present efficient algorithms, including hGA, ILS, PSO, and IG, verify the efficiency and effectiveness of the proposed algorithm. PMID:24883414

Li, Jun-qing; Pan, Quan-ke; Mao, Kun

2014-01-01

109

Hybrid Particle Swarm Optimization for Hybrid Flowshop Scheduling Problem with Maintenance Activities  

PubMed Central

A hybrid algorithm which combines particle swarm optimization (PSO) and iterated local search (ILS) is proposed for solving the hybrid flowshop scheduling (HFS) problem with preventive maintenance (PM) activities. In the proposed algorithm, different crossover operators and mutation operators are investigated. In addition, an efficient multiple insert mutation operator is developed for enhancing the searching ability of the algorithm. Furthermore, an ILS-based local search procedure is embedded in the algorithm to improve the exploitation ability of the proposed algorithm. The detailed experimental parameter for the canonical PSO is tuning. The proposed algorithm is tested on the variation of 77 Carlier and Néron's benchmark problems. Detailed comparisons with the present efficient algorithms, including hGA, ILS, PSO, and IG, verify the efficiency and effectiveness of the proposed algorithm. PMID:24883414

Li, Jun-qing; Pan, Quan-ke; Mao, Kun

2014-01-01

110

PMSM Driver Based on Hybrid Particle Swarm Optimization and CMAC  

NASA Astrophysics Data System (ADS)

A novel hybrid particle swarm optimization (PSO) and cerebellar model articulation controller (CMAC) is introduced to the permanent magnet synchronous motor (PMSM) driver. PSO can simulate the random learning among the individuals of population and CMAC can simulate the self-learning of an individual. To validate the ability and superiority of the novel algorithm, experiments and comparisons have been done in MATLAB/SIMULINK. Analysis among PSO, hybrid PSO-CMAC and CMAC feed-forward control is also given. The results prove that the electric torque ripple and torque disturbance of the PMSM driver can be reduced by using the hybrid PSO-CMAC algorithm.

Tu, Ji; Cao, Shaozhong

111

Mixed variable structural optimization using Firefly Algorithm  

Microsoft Academic Search

In this study, a recently developed metaheuristic optimization algorithm, the Firefly Algorithm (FA), is used for solving mixed continuous\\/discrete structural optimization problems. FA mimics the social behavior of fireflies based on their flashing characteristics. The results of a trade study carried out on six classical structural optimization problems taken from literature confirm the validity of the proposed algorithm. The unique

Amir Hossein Gandomi; Xin-She Yang; Amir Hossein Alavi

2011-01-01

112

Exploring chemical space with discrete, gradient, and hybrid optimization methods  

NASA Astrophysics Data System (ADS)

Discrete, gradient, and hybrid optimization 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.

Balamurugan, D.; Yang, Weitao; Beratan, David N.

2008-11-01

113

Fuzzy Adaptive Swarm Optimization Algorithm for Discrete Environments  

NASA Astrophysics Data System (ADS)

The heuristic methods have been widely developed for solution of complicated optimization methods. Recently hybrid methods that are based on combination of different approaches have shown more potential in this regard. Fuzzy simulation and Particle Swarm Optimization algorithm are integrated to design a hybrid intelligent algorithm to solve the np-hard problem such as travelling salesman problem in efficient and faster way of solutions. The results obtained with the proposed method show its potential in achieving both accuracy and speed in small and medium size problems, compared to many advanced methods.

Zahedi, M. Hadi; S. Haghighi, M. Mehdi

114

Parametric Optimization of Hybrid Car Engines  

Microsoft Academic Search

We consider the problem of optimal design of hybrid car engines which combine thermal and electric power. The optimal configuration of the different motors composing the hybrid system involves the choice of certain design parameters. For a given configuration, the goal is to minimize the fuel consumption along a trajectory. This is an optimal control problem with one state variable.

Joseph Frédéric Bonnans; Thérèse Guilbaud; Ahmed Ketfi-Cherif; Dirk von Wissel; Claudia Sagastizábal; Housnaa Zidani

2004-01-01

115

A generalized hybrid algorithm for bioluminescence tomography  

PubMed Central

Bioluminescence tomography (BLT) is a promising optical molecular imaging technique on the frontier of biomedical optics. In this paper, a generalized hybrid algorithm 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. PMID:23667787

Shi, Shengkun; Mao, Heng

2013-01-01

116

Synergy of evolutionary algorithm and socio-political process for global optimization  

Microsoft Academic Search

This paper proposes a hybrid approach by combining the evolutionary optimization based genetic algorithm (GA) and socio-political process based colonial competitive algorithm (CCA). The performance of hybrid algorithm is illustrated using standard test functions in comparison to basic CCA method. Since the CCA method is newly developed, very little research work has been undertaken to deal with curse of dimensionality

Tushar Jain; M. J. Nigam

2010-01-01

117

A New Optimized GA-RBF Neural Network Algorithm  

PubMed Central

When confronting the complex problems, radial basis function (RBF) neural network has the advantages of adaptive and self-learning ability, but it is difficult to determine the number of hidden layer neurons, and the weights learning ability from hidden layer to the output layer is low; these deficiencies easily lead to decreasing learning ability and recognition precision. Aiming at this problem, we propose a new optimized RBF neural network algorithm based on genetic algorithm (GA-RBF algorithm), which uses genetic algorithm to optimize the weights and structure of RBF neural network; it chooses new ways of hybrid encoding and optimizing simultaneously. Using the binary encoding encodes the number of the hidden layer's neurons and using real encoding encodes the connection weights. Hidden layer neurons number and connection weights are optimized simultaneously in the new algorithm. However, the connection weights optimization is not complete; we need to use least mean square (LMS) algorithm for further leaning, and finally get a new algorithm model. Using two UCI standard data sets to test the new algorithm, the results show that the new algorithm improves the operating efficiency in dealing with complex problems and also improves the recognition precision, which proves that the new algorithm is valid. PMID:25371666

Zhao, Dean; Su, Chunyang; Hu, Chanli; Zhao, Yuyan

2014-01-01

118

Size optimization of space trusses using Big Bang–Big Crunch algorithm  

Microsoft Academic Search

A Hybrid Big Bang–Big Crunch (HBB–BC) optimization algorithm is employed for optimal design of truss structures. HBB–BC is compared to Big Bang–Big Crunch (BB–BC) method and other optimization methods including Genetic Algorithm, Ant Colony Optimization, Particle Swarm Optimization and Harmony Search. Numerical results demonstrate the efficiency and robustness of the HBB–BC method compared to other heuristic algorithms.

A. Kaveh; S. Talatahari

2009-01-01

119

An efficient algorithm for function optimization: modified stem cells algorithm  

NASA Astrophysics Data System (ADS)

In this paper, we propose an optimization algorithm based on the intelligent behavior of stem cell swarms in reproduction and self-organization. Optimization algorithms, such as the Genetic Algorithm (GA), Particle Swarm Optimization (PSO) algorithm, Ant Colony Optimization (ACO) algorithm and Artificial Bee Colony (ABC) algorithm, can give solutions to linear and non-linear problems near to the optimum for many applications; however, in some case, they can suffer from becoming trapped in local optima. The Stem Cells Algorithm (SCA) is an optimization algorithm inspired by the natural behavior of stem cells in evolving themselves into new and improved cells. The SCA avoids the local optima problem successfully. In this paper, we have made small changes in the implementation of this algorithm to obtain improved performance over previous versions. Using a series of benchmark functions, we assess the performance of the proposed algorithm and compare it with that of the other aforementioned optimization algorithms. The obtained results prove the superiority of the Modified Stem Cells Algorithm (MSCA).

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

2013-03-01

120

An Effective PSO-Based Hybrid Algorithm for Multiobjective Permutation Flow Shop Scheduling  

Microsoft Academic Search

This paper proposes a hybrid algorithm 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

Bin-bin Li; Ling Wang; Bo Liu

2008-01-01

121

A novel hybrid algorithm for scheduling steel-making continuous casting production  

Microsoft Academic Search

In this paper, steel-making continuous casting (SCC) scheduling problem (SCCSP) is investigated. This problem is a specific case of hybrid flow shop scheduling problem accompanied by technological constraints of steel-making. Since classic optimization methods fail to obtain an optimal solution for this problem over a suitable time, a novel iterative algorithm is developed. The proposed algorithm, named HANO, is based

Arezoo Atighehchian; Mehdi Bijari; Hamed Tarkesh

2009-01-01

122

A hybrid cooperative-comprehensive learning based PSO algorithm for image segmentation using multilevel thresholding  

Microsoft Academic Search

A novel optimal multilevel thresholding algorithm for histogram-based image segmentation is presented in this paper. The proposed algorithm presents an improved variant of PSO, a relatively recently introduced stochastic optimization strategy. This hybrid approach employs both cooperative learning and comprehensive learning along with some additional modifications. Cooperative learning is employed to overcome the “curse of dimensionality” by decomposing a high-dimensional

Madhubanti Maitra; Amitava Chatterjee

2008-01-01

123

An hybrid real genetic algorithm to detect structural damage using modal properties  

Microsoft Academic Search

An hybrid real-coded Genetic Algorithm with damage penalization is implemented to locate and quantify structural damage. Genetic Algorithms provide a powerful tool to solved optimization problems. With an appropriate selection of their operators and parameters they can potentially explore the entire solution space and reach the global optimum. Here, the set-up of the Genetic Algorithm operators and parameters is addressed,

V. Meruane; W. Heylen

2011-01-01

124

Hybrid genetic algorithms for parameter identification of a hysteresis model of magnetostrictive actuators  

Microsoft Academic Search

In this paper, we present an improved hysteresis model for magnetostrictive actuators. To obtain optimal parameters of the model, we study two distinct hybrid strategies: namely, employing a gradient algorithm as a local search operation of a genetic algorithm (GA), and taking the best individual of a GA as the initial value of a gradient algorithm. Here, two different gradient

Jiaju Zheng; Shuying Cao; Hongli Wang; Wenmei Huang

2007-01-01

125

On convergence and optimality of genetic algorithms  

Microsoft Academic Search

An action of genetic algorithm could be represented in the search space as a random Markovian process. The question concerning its asymptotic stability properties is discussed. Conditions under which genetic algorithm is convergent, are formulated. Then the existence of an operator to which infinite long iterations of the genetic algorithms tend, is shown. This operator describes optimal genetic algorithm in

Witold Kosinski; Stefan Kotowski; Zbyszek Michalewicz

2010-01-01

126

Optimal brake torque distribution for a four-wheeldrive hybrid electric vehicle stability enhancement  

Microsoft Academic Search

Vehicle stability control logic for a four-wheel-drive hybrid electric vehicle is proposed using the regenerative braking of the rear motor and an electrohydraulic brake (EHB). To obtain the optimal brake torque distribution between the regenerative braking and the EHB torque, a genetic algorithm is used. The genetic algorithm calculates the optimal regenerative braking torque and the optimal EHB torque for

D-H Kim; J-M Kim; S-H Hwang; H-S Kim

2007-01-01

127

Metaheuristic Optimization: Algorithm Analysis and Open Problems  

Microsoft Academic Search

\\u000a Metaheuristic algorithms are becoming an important part of modern optimization. A wide range of metaheuristic algorithms have\\u000a emerged over the last two decades, and many metaheuristics such as particle swarm optimization are becoming increasingly popular.\\u000a Despite their popularity, mathematical analysis of these algorithms lacks behind. Convergence analysis still remains unsolved\\u000a for the majority of metaheuristic algorithms, while efficiency analysis is

Xin-She Yang

2011-01-01

128

A hybrid evolutionary search scheduling algorithm to solve the job shop scheduling problem  

Microsoft Academic Search

This paper describes an evolutionary search scheduling algorithm (ESSA) developed to solve the most difficult job shop scheduling problems (JSSP) that are known to be NP-hard combinatorial optimization problems. The ESSA proposed is a hybrid approach that focuses on optimization of locally optimized solutions. The differences versus other ESSA strategies are the new proposed encoding, decoding and forcing scheme, the

P. Van Bael; D. Devogelaere; M. Rijckaert

1999-01-01

129

HyGLEAM - An Approach to Generally Applicable Hybridization of Evolutionary Algorithms  

Microsoft Academic Search

Most successful applications of Evolutionary Algorithms to real world problems employ some sort of hybridization, thus speeding\\u000a up the optimization process but turning the general applicable Evolutionary Algorithm into a problem-specific tool. This paper\\u000a proposes to combine Evolutionary Algorithms and generally applicable local searchers to get the best of both approaches: A\\u000a fast, but robust tool for global optimization. The

Wilfried Jakob

2002-01-01

130

Optimal Control of Hybrid Systems in Air Traffic Applications  

NASA Astrophysics Data System (ADS)

Growing concerns over the scalability of air traffic operations, air transportation fuel emissions and prices, as well as the advent of communication and sensing technologies motivate improvements to the air traffic management system. To address such improvements, in this thesis a hybrid dynamical model as an abstraction of the air traffic system is considered. Wind and hazardous weather impacts are included using a stochastic model. This thesis focuses on the design of algorithms for verification and control of hybrid and stochastic dynamical systems and the application of these algorithms to air traffic management problems. In the deterministic setting, a numerically efficient algorithm for optimal control of hybrid systems is proposed based on extensions of classical optimal control techniques. This algorithm is applied to optimize the trajectory of an Airbus 320 aircraft in the presence of wind and storms. In the stochastic setting, the verification problem of reaching a target set while avoiding obstacles (reach-avoid) is formulated as a two-player game to account for external agents' influence on system dynamics. The solution approach is applied to air traffic conflict prediction in the presence of stochastic wind. Due to the uncertainty in forecasts of the hazardous weather, and hence the unsafe regions of airspace for aircraft flight, the reach-avoid framework is extended to account for stochastic target and safe sets. This methodology is used to maximize the probability of the safety of aircraft paths through hazardous weather. Finally, the problem of modeling and optimization of arrival air traffic and runway configuration in dense airspace subject to stochastic weather data is addressed. This problem is formulated as a hybrid optimal control problem and is solved with a hierarchical approach that decouples safety and performance. As illustrated with this problem, the large scale of air traffic operations motivates future work on the efficient implementation of the proposed algorithms.

Kamgarpour, Maryam

131

A Multi-Objective Hybrid Genetic Based Optimization for External Beam Radiation  

NASA Astrophysics Data System (ADS)

A multi-objective hybrid genetic based optimization algorithm is proposed according to the multi-objective property of inverse planning. It is based on hybrid adaptive genetic algorithm which combines the simulated annealing, uses adaptive crossover and mutation, and adopts niched tournament selection. The result of the test calculation demonstrates that an excellent converging speed can be achieved using this approach.

Li, Guoli; Song, Gang; Wu, Yican; Zhang, Jian; Wang, Qunjing

2006-03-01

132

Genetic Algorithms for Optimal Reservoir Dispatching  

Microsoft Academic Search

The fundamental guidelines for genetic algorithm to optimal reservoir dispatching have been introduced. It is concluded that with three basic generators selection, crossover and mutation genetic algorithm could search the optimum solution or near-optimal solution to a complex water resources problem. Alternative formulation schemes of a GA are considered. The real-value coding is proved significantly faster than binary coding, and

Chang Jian-Xia; Huang Qiang; Wang Yi-min

2005-01-01

133

Genetic algorithms approach to voltage optimization  

Microsoft Academic Search

The authors consider the use of genetic algorithms as a measure of voltage optimization of electric power system. Genetic algorithms are optimization and learning techniques based on natural selection and natural population genetics. A formation of a power system is encoded to a string of characters called an artificial chromosome the initial population of strings are generated at random, and

Takeshi Haida; Yoshiakira Akimoto

1991-01-01

134

Optimization of Transform Coefficients via Genetic Algorithm  

E-print Network

Optimization of Transform Coefficients via Genetic Algorithm Steven Becke CS 470 ­Project Write.................................................................................................................... 22 #12;1 Optimization of Transform Coefficients via Genetic Algorithm Steven Becke Abstract discovered in recent years for image compression is Wavelet Transforms. Wavelet transforms can dramatically

Mock, Kenrick

135

An Optimal Class Association Rule Algorithm  

NASA Astrophysics Data System (ADS)

Classification and association rule mining algorithms are two important aspects of data mining. Class association rule mining algorithm is a promising approach for it involves the use of association rule mining algorithm to discover classification rules. This paper introduces an optimal class association rule mining algorithm known as OCARA. It uses optimal association rule mining algorithm and the rule set is sorted by priority of rules resulting into a more accurate classifier. It outperforms the C4.5, CBA, RMR on UCI eight data sets, which is proved by experimental results.

Jean Claude, Turiho; Sheng, Yang; Chuang, Li; Kaia, Xie

136

A Hybrid PSO\\/GA Algorithm for Job Shop Scheduling Problem  

Microsoft Academic Search

\\u000a The job shop scheduling problem is a well-known NP hard problem, on which genetic algorithm is widely used. However, due to\\u000a the lack of the major evolution direction, the effectiveness of the regular genetic algorithm is restricted. In this paper,\\u000a we propose a new hybrid genetic algorithm to solve the job shop scheduling problem. The particle swarm optimization algorithm\\u000a is

Jianchao Tang; Guoji Zhang; Binbin Lin; Bixi Zhang

2010-01-01

137

Enhanced hybrid search algorithm for protein structure prediction using the 3D-HP lattice model.  

PubMed

The problem of protein structure prediction in the hydrophobic-polar (HP) lattice model is the prediction of protein tertiary structure. This problem is usually referred to as the protein folding problem. This paper presents a method for the application of an enhanced hybrid search algorithm to the problem of protein folding prediction, using the three dimensional (3D) HP lattice model. The enhanced hybrid search algorithm is a combination of the particle swarm optimizer (PSO) and tabu search (TS) algorithms. Since the PSO algorithm entraps local minimum in later evolution extremely easily, we combined PSO with the TS algorithm, which has properties of global optimization. Since the technologies of crossover and mutation are applied many times to PSO and TS algorithms, so enhanced hybrid search algorithm is called the MCMPSO-TS (multiple crossover and mutation PSO-TS) algorithm. Experimental results show that the MCMPSO-TS algorithm can find the best solutions so far for the listed benchmarks, which will help comparison with any future paper approach. Moreover, real protein sequences and Fibonacci sequences are verified in the 3D HP lattice model for the first time. Compared with the previous evolutionary algorithms, the new hybrid search algorithm is novel, and can be used effectively to predict 3D protein folding structure. With continuous development and changes in amino acids sequences, the new algorithm will also make a contribution to the study of new protein sequences. PMID:23824509

Zhou, Changjun; Hou, Caixia; Zhang, Qiang; Wei, Xiaopeng

2013-09-01

138

Intelligent perturbation algorithms to space scheduling optimization  

NASA Technical Reports Server (NTRS)

The limited availability and high cost of crew time and scarce resources make optimization of space operations critical. Advances in computer technology coupled with new iterative search techniques permit the near optimization of complex scheduling problems that were previously considered computationally intractable. Described here is a class of search techniques called Intelligent Perturbation Algorithms. Several scheduling systems which use these algorithms to optimize the scheduling of space crew, payload, and resource operations are also discussed.

Kurtzman, Clifford R.

1991-01-01

139

Path planning using a hybrid evolutionary algorithm based on tree structure encoding.  

PubMed

A hybrid evolutionary algorithm using scalable encoding method for path planning is proposed in this paper. The scalable representation is based on binary tree structure encoding. To solve the problem of hybrid genetic algorithm and particle swarm optimization, the "dummy node" is added into the binary trees to deal with the different lengths of representations. The experimental results show that the proposed hybrid method demonstrates using fewer turning points than traditional evolutionary algorithms to generate shorter collision-free paths for mobile robot navigation. PMID:24971389

Ju, Ming-Yi; Wang, Siao-En; Guo, Jian-Horn

2014-01-01

140

Hybrid Evolutionary Algorithms: Methodologies, Architectures, and Reviews  

Microsoft Academic Search

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

Crina Grosan; Ajith Abraham

141

Parameter estimation for chaotic systems using a hybrid adaptive cuckoo search with simulated annealing algorithm  

NASA Astrophysics Data System (ADS)

This paper introduces a novel hybrid optimization algorithm to establish the parameters of chaotic systems. In order to deal with the weaknesses of the traditional cuckoo search algorithm, the proposed adaptive cuckoo search with simulated annealing algorithm is presented, which incorporates the adaptive parameters adjusting operation and the simulated annealing operation in the cuckoo search algorithm. Normally, the parameters of the cuckoo search algorithm are kept constant that may result in decreasing the efficiency of the algorithm. For the purpose of balancing and enhancing the accuracy and convergence rate of the cuckoo search algorithm, the adaptive operation is presented to tune the parameters properly. Besides, the local search capability of cuckoo search algorithm is relatively weak that may decrease the quality of optimization. So the simulated annealing operation is merged into the cuckoo search algorithm to enhance the local search ability and improve the accuracy and reliability of the results. The functionality of the proposed hybrid algorithm is investigated through the Lorenz chaotic system under the noiseless and noise condition, respectively. The numerical results demonstrate that the method can estimate parameters efficiently and accurately in the noiseless and noise condition. Finally, the results are compared with the traditional cuckoo search algorithm, genetic algorithm, and particle swarm optimization algorithm. Simulation results demonstrate the effectiveness and superior performance of the proposed algorithm.

Sheng, Zheng; Wang, Jun; Zhou, Shudao; Zhou, Bihua

2014-03-01

142

Parameter estimation for chaotic systems using a hybrid adaptive cuckoo search with simulated annealing algorithm.  

PubMed

This paper introduces a novel hybrid optimization algorithm to establish the parameters of chaotic systems. In order to deal with the weaknesses of the traditional cuckoo search algorithm, the proposed adaptive cuckoo search with simulated annealing algorithm is presented, which incorporates the adaptive parameters adjusting operation and the simulated annealing operation in the cuckoo search algorithm. Normally, the parameters of the cuckoo search algorithm are kept constant that may result in decreasing the efficiency of the algorithm. For the purpose of balancing and enhancing the accuracy and convergence rate of the cuckoo search algorithm, the adaptive operation is presented to tune the parameters properly. Besides, the local search capability of cuckoo search algorithm is relatively weak that may decrease the quality of optimization. So the simulated annealing operation is merged into the cuckoo search algorithm to enhance the local search ability and improve the accuracy and reliability of the results. The functionality of the proposed hybrid algorithm is investigated through the Lorenz chaotic system under the noiseless and noise condition, respectively. The numerical results demonstrate that the method can estimate parameters efficiently and accurately in the noiseless and noise condition. Finally, the results are compared with the traditional cuckoo search algorithm, genetic algorithm, and particle swarm optimization algorithm. Simulation results demonstrate the effectiveness and superior performance of the proposed algorithm. PMID:24697395

Sheng, Zheng; Wang, Jun; Zhou, Shudao; Zhou, Bihua

2014-03-01

143

Modeling and Prediction for Discharge Lifetime of Battery Systems using Hybrid Evolutionary Algorithms  

Microsoft Academic Search

A hybrid evolutionary modeling algorithm (HEMA) is proposed to build the discharge lifetime models with multiple impact factors for battery systems as well as make predictions. The main idea of the HEMA is to embed a genetic algorithm (GA) into genetic programming (GP), where GP is employed to optimize the structure of a model, while a GA is employed to

Hongqing Cao; Jingxian Yu; Lishan Kang; Hanxi Yang; Xinping Ai

2001-01-01

144

Swarm algorithms for single- and multi-objective optimization problems incorporating sensitivity analysis  

NASA Astrophysics Data System (ADS)

Swarm algorithms such as particle swarm optimization (PSO) are non-gradient probabilistic optimization algorithms that have been successfully applied for global searches in complex problems such as multi-peak problems. However, application of these algorithms to structural and mechanical optimization problems still remains a complex matter since local optimization capability is still inferior to general numerical optimization methods. This article discusses new swarm metaphors that incorporate design sensitivities concerning objective and constraint functions and are applicable to structural and mechanical design optimization problems. Single- and multi-objective optimization techniques using swarm algorithms are combined with a gradient-based method. In the proposed techniques, swarm optimization algorithms and a sequential linear programming (SLP) method are conducted simultaneously. Finally, truss structure design optimization problems are solved by the proposed hybrid method to verify the optimization efficiency.

Izui, K.; Nishiwaki, S.; Yoshimura, M.

2007-12-01

145

Hybrid Simulated Annealing and Its Application to Optimization of Hidden Markov Models for Visual Speech Recognition  

Microsoft Academic Search

We propose a novel stochastic optimization algorithm, 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

Jong-Seok Lee; Cheol Hoon Park

2010-01-01

146

Global optimization of hybrid systems  

E-print Network

Systems that exhibit both discrete state and continuous state dynamics are called hybrid systems. In most nontrivial cases, these two aspects of system behavior interact to such a significant extent that they cannot be ...

Lee, Cha Kun

2006-01-01

147

MODELING, VERIFICATION AND OPTIMIZATION OF HYBRID GROUND SOURCE HEAT PUMP  

E-print Network

MODELING, VERIFICATION AND OPTIMIZATION OF HYBRID GROUND SOURCE HEAT PUMP SYSTEMS IN ENERGYPLUS, VERIFICATION AND OPTIMIZATION OF HYBRID GROUND SOURCE HEAT PUMP SYSTEMS IN ENERGYPLUS Thesis Approved by: Dr.................................................................................................................... 16 MODELING OF HYBRID GROUND SOURCE HEAT PUMP SYSTEMS IN ENERGYPLUS

148

Hybrid vehicle optimization: lead acid battery modellization  

Microsoft Academic Search

This work is part of a larger project aiming at the optimization of an hybrid electric vehicle with series design. The final objective of the optimization process is the identification of the most efficient vehicle management strategy in order to reduce the emissions, fuel consumption without deteriorating the batteries. In order to achieve this objective the whole system modellization is

Enrico Bertolazzi; Francesco Biral; Mauro Da Lio; Massimo Matteotti; Ahmed Masmoudi; Ahmed Elantably

2009-01-01

149

Adaptable optimization : theory and algorithms  

E-print Network

Optimization under uncertainty is a central ingredient for analyzing and designing systems with incomplete information. This thesis addresses uncertainty in optimization, in a dynamic framework where information is revealed ...

Caramanis, Constantine (Constantine Michael), 1977-

2006-01-01

150

Acoustic Radiation Optimization Using the Particle Swarm Optimization Algorithm  

NASA Astrophysics Data System (ADS)

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 optimization algorithm (PSOA). The particle swarm optimization algorithm 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.

Jeon, Jin-Young; Okuma, Masaaki

151

Genetic\\/quadratic search algorithm for plant economic optimizations using a process simulator  

Microsoft Academic Search

The genetic\\/quadratic search algorithm (GQSA) is a hybrid genetic algorithms (GA) for optimizing plant economics when a process simulator models the plant. By coupling a regular GA with an algorithm based upon a quadratic search, the required number of objective function evaluations for obtaining an acceptable solution decreases significantly in most cases. The GQSA combines advantages of GA and quadratic

Won-hyouk Jang; Juergen Hahn; Kenneth R. Hall

2005-01-01

152

A data locality optimizing algorithm  

Microsoft Academic Search

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

Michael E. Wolf; Monica S. Lam

1991-01-01

153

Algorithms for Optimizing Hydropower System Operation  

Microsoft Academic Search

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

Jan C. Grygier; Jery R. Stedinger

1985-01-01

154

Adaptive cuckoo search algorithm for unconstrained optimization.  

PubMed

Modification of the intensification and diversification approaches in the recently developed cuckoo search algorithm (CSA) is performed. The alteration involves the implementation of adaptive step size adjustment strategy, and thus enabling faster convergence to the global optimal solutions. The feasibility of the proposed algorithm is validated against benchmark optimization functions, where the obtained results demonstrate a marked improvement over the standard CSA, in all the cases. PMID:25298971

Ong, Pauline

2014-01-01

155

Adaptive Cuckoo Search Algorithm for Unconstrained Optimization  

PubMed Central

Modification of the intensification and diversification approaches in the recently developed cuckoo search algorithm (CSA) is performed. The alteration involves the implementation of adaptive step size adjustment strategy, and thus enabling faster convergence to the global optimal solutions. The feasibility of the proposed algorithm is validated against benchmark optimization functions, where the obtained results demonstrate a marked improvement over the standard CSA, in all the cases.

2014-01-01

156

Functional Sized Population Magnetic Optimization Algorithm  

NASA Astrophysics Data System (ADS)

Magnetic Optimization Algorithm (MOA) is a recently novel optimization algorithm inspired by the principles of magnetic field theory whose possible solutions are magnetic particles scattered in the search space. In order improve the performance of MOA, a Functional Size population MOA (FSMOA) is proposed here. To find the best function for the size of the population, several functions for MOA are considered and investigated and the best parameters for the functions will be derived. In order to test the proposed algorithm and operators, the proposed algorithm will be compared with GA, PSO, QEA and saw-tooth GA on 14 numerical benchmark functions. Experimental results show that the proposed algorithm consistently has a better performance than those of other algorithms in most benchmark function.

Torshizi, Mehdi; Tayarani-N., M.

157

Traffic sharing algorithms for hybrid mobile networks  

NASA Technical Reports Server (NTRS)

In a hybrid (terrestrial + satellite) mobile personal communications networks environment, a large size satellite footprint (supercell) overlays on a large number of smaller size, contiguous terrestrial cells. We assume that the users have either a terrestrial only single mode terminal (SMT) or a terrestrial/satellite dual mode terminal (DMT) and the ratio of DMT to the total terminals is defined gamma. It is assumed that the call assignments to and handovers between terrestrial cells and satellite supercells take place in a dynamic fashion when necessary. The objectives of this paper are twofold, (1) to propose and define a class of traffic sharing algorithms to manage terrestrial and satellite network resources efficiently by handling call handovers dynamically, and (2) to analyze and evaluate the algorithms by maximizing the traffic load handling capability (defined in erl/cell) over a wide range of terminal ratios (gamma) given an acceptable range of blocking probabilities. Two of the algorithms (G & S) in the proposed class perform extremely well for a wide range of gamma.

Arcand, S.; Murthy, K. M. S.; Hafez, R.

1995-01-01

158

Finding Tradeoffs by Using Multiobjective Optimization Algorithms  

Microsoft Academic Search

The objective of the present study is to demonstrate performances of Evolutionary Algorithms (EAs) and conventional gradient-based methods for finding Pareto fronts. The multiobjective optimization algorithms are applied to analytical test problems as well as to the real-world problems of a compressor design. The comparison results clearly indicate the superiority of EAs in finding tradeoffs.

Shigeru Obayashi; Daisuke Sasaki; Akira Oyama

2005-01-01

159

An Emotional Particle Swarm Optimization Algorithm  

Microsoft Academic Search

\\u000a This paper presents a modification of the particle swarm optimization algorithm (PSO) intended to introduce some psychology\\u000a factor of emotion into the algorithm. In the new algorithm, which is based on a simple perception and emotion psychology model,\\u000a each particle has its own feeling and reaction to the current position, and it also has specified emotional factor towards\\u000a the sense

Yang Ge; Zhang Rubo

2005-01-01

160

Optimizing alphabet using genetic algorithms  

Microsoft Academic Search

Data compression algorithms were usually designed for data processing symbol by symbol. The input symbols of these algorithms are usually taken from the ASCII table, i.e. the size of the input alphabet is 256 symbols which are representable by 8-bit numbers. Several other techniques were developed-syllable-based compression, which uses the syllable as a basic compression symbol, and word-based compression, which

Jan Platos; Pavel Kromer

2011-01-01

161

Generation of Compliant Mechanisms using Hybrid Genetic Algorithm  

NASA Astrophysics Data System (ADS)

Compliant mechanism is a single piece elastic structure which can deform to perform the assigned task. In this work, compliant mechanisms are evolved using a constraint based bi-objective optimization formulation which requires one user defined parameter ( ?). This user defined parameter limits a gap between a desired path and an actual path traced by the compliant mechanism. The non-linear and discrete optimization problems are solved using the hybrid Genetic Algorithm (GA) wherein domain specific initialization, two-dimensional crossover operator and repairing techniques are adopted. A bit-wise local search method is used with elitist non-dominated sorting genetic algorithm to further refine the compliant mechanisms. Parallel computations are performed on the master-slave architecture to reduce the computation time. A parametric study is carried out for ? value which suggests a range to evolve topologically different compliant mechanisms. The applied and boundary conditions to the compliant mechanisms are considered the variables that are evolved by the hybrid GA. The post-analysis of results unveils that the complaint mechanisms are always supported at unique location that can evolve the non-dominated solutions.

Sharma, D.; Deb, K.

2014-10-01

162

OPTIMAL STEEPEST DESCENT ALGORITHMS FOR ...  

E-print Network

for the first time methods for differentiable optimization had their practical efficiency motivated by .... method ensures ?k = O(1/k2), and this means optimal complexity: an error of ? > 0 is achieved in ...... codes are written in Matlab. We solved 60 ...

2008-08-02

163

Social Emotional Optimization Algorithm for Nonlinear Constrained Optimization Problems  

NASA Astrophysics Data System (ADS)

Nonlinear programming problem is one important branch in operational research, and has been successfully applied to various real-life problems. In this paper, a new approach called Social emotional optimization algorithm (SEOA) is used to solve this problem which is a new swarm intelligent technique by simulating the human behavior guided by emotion. Simulation results show that the social emotional optimization algorithm proposed in this paper is effective and efficiency for the nonlinear constrained programming problems.

Xu, Yuechun; Cui, Zhihua; Zeng, Jianchao

164

Algorithms for optimal dyadic decision trees  

SciTech Connect

A new algorithm for constructing optimal dyadic decision trees was recently introduced, analyzed, and shown to be very effective for low dimensional data sets. This paper enhances and extends this algorithm by: introducing an adaptive grid search for the regularization parameter that guarantees optimal solutions for all relevant trees sizes, revising the core tree-building algorithm so that its run time is substantially smaller for most regularization parameter values on the grid, and incorporating new data structures and data pre-processing steps that provide significant run time enhancement in practice.

Hush, Don [Los Alamos National Laboratory; Porter, Reid [Los Alamos National Laboratory

2009-01-01

165

A multi-local optimization algorithm  

Microsoft Academic Search

The development of efficient algorithms that provide all the local minima of a function is crucial to solve certain subproblems\\u000a in many optimization methods. A “multi-local” optimization procedure using inexact line searches is presented, and numerical\\u000a experiments are also reported. An application of the method to a semi-infinite programming procedure is included.

Teresa León; Susana Sanmatías; Enriqueta Vercher

1998-01-01

166

Variable-Metric Algorithm For Constrained Optimization  

NASA Technical Reports Server (NTRS)

Variable Metric Algorithm for Constrained Optimization (VMACO) is nonlinear computer program developed to calculate least value of function of n variables subject to general constraints, both equality and inequality. First set of constraints equality and remaining constraints inequalities. Program utilizes iterative method in seeking optimal solution. Written in ANSI Standard FORTRAN 77.

Frick, James D.

1989-01-01

167

Hybrid Harmony Search Algorithm and Interior Point Method for Economic Dispatch with Valve-Point Effect  

NASA Astrophysics Data System (ADS)

This paper proposes a new hybrid algorithm combining harmony search (HS) algorithm and interior point method (IPM) for economic dispatch (ED) problem with valve-point effect. ED problem with valve-point effect is modeled as a non-linear, constrained and non-convex optimization problem having several local minima. IPM is a best non-linear optimization method for convex optimization problems. Since ED problem with valve-point effect has multiple local minima, IPM results in a local optimum solution. In order to avoid IPM getting trapped in a local optimum, a new evolutionary algorithm HS, which is good in global exploration, has been combined. In the hybrid method, HS is used for global search and IPM for local search. The hybrid method has been tested on three different test systems to prove its effectiveness. Finally, the simulation results are also compared with other methods reported in the literature.

Sivasubramani, S.; Ahmad, Md. Samar

2014-06-01

168

Algorithm selection in structural optimization  

E-print Network

Structural optimization is largely unused as a practical design tool, despite an extensive academic literature which demonstrates its potential to dramatically improve design processes and outcomes. Many factors inhibit ...

Clune, Rory P. (Rory Patrick)

2013-01-01

169

Optimal design of a PV-diesel hybrid system for electrification of an isolated island—Sandwip in Bangladesh using genetic algorithm  

Microsoft Academic Search

It is not cost effective or feasible to extend a centralized power grid to islands and other isolated communities. Decentralized renewable energy sources are alternatives. Among these alternatives are hybrid photovoltaic systems which combine solar photovoltaic energy with other renewable energy sources like wind. A diesel backup system can be used when PV system fails to satisfy the load and

BK Bala; Saiful Azam Siddique

2009-01-01

170

An Efficient Hybrid Classification Algorithm -an Example from Palliative Care  

E-print Network

An Efficient Hybrid Classification Algorithm - an Example from Palliative Care Tor Gunnar Houeland this hybrid classification algorithm to predict the pain classification for palliative care patients for pain classification in palliative care [9]. Our domain is open and changing, which is why we study

Aamodt, Agnar

171

A Hybrid Algorithm for the Examination Timetabling Problem  

E-print Network

A Hybrid Algorithm for the Examination Timetabling Problem Liam T.G. Merlot1 , Natashia Boland1 3010, Australia pjs@cs.mu.oz.au Abstract. Examination timetabling is a well-studied combinatorial op- timization problem. We present a new hybrid algorithm for examination timetabling, consisting of three phases

Stuckey, Peter J.

172

A novel bee swarm optimization algorithm for numerical function optimization  

NASA Astrophysics Data System (ADS)

The optimization algorithms which are inspired from intelligent behavior of honey bees are among the most recently introduced population based techniques. In this paper, a novel algorithm called bee swarm optimization, or BSO, and its two extensions for improving its performance are presented. The BSO is a population based optimization technique which is inspired from foraging behavior of honey bees. The proposed approach provides different patterns which are used by the bees to adjust their flying trajectories. As the first extension, the BSO algorithm introduces different approaches such as repulsion factor and penalizing fitness (RP) to mitigate the stagnation problem. Second, to maintain efficiently the balance between exploration and exploitation, time-varying weights (TVW) are introduced into the BSO algorithm. The proposed algorithm (BSO) and its two extensions (BSO-RP and BSO-RPTVW) are compared with existing algorithms which are based on intelligent behavior of honey bees, on a set of well known numerical test functions. The experimental results show that the BSO algorithms are effective and robust; produce excellent results, and outperform other algorithms investigated in this consideration.

Akbari, Reza; Mohammadi, Alireza; Ziarati, Koorush

2010-10-01

173

A cuckoo search algorithm for multimodal optimization.  

PubMed

Interest in multimodal optimization is expanding rapidly, since many practical engineering problems demand the localization of multiple optima within a search space. On the other hand, the cuckoo search (CS) algorithm is a simple and effective global optimization algorithm which can not be directly applied to solve multimodal optimization problems. This paper proposes a new multimodal optimization algorithm called the multimodal cuckoo search (MCS). Under MCS, the original CS is enhanced with multimodal capacities by means of (1) the incorporation of a memory mechanism to efficiently register potential local optima according to their fitness value and the distance to other potential solutions, (2) the modification of the original CS individual selection strategy to accelerate the detection process of new local minima, and (3) the inclusion of a depuration procedure to cyclically eliminate duplicated memory elements. The performance of the proposed approach is compared to several state-of-the-art multimodal optimization algorithms considering a benchmark suite of fourteen multimodal problems. Experimental results indicate that the proposed strategy is capable of providing better and even a more consistent performance over existing well-known multimodal algorithms for the majority of test problems yet avoiding any serious computational deterioration. PMID:25147850

Cuevas, Erik; Reyna-Orta, Adolfo

2014-01-01

174

A Cuckoo Search Algorithm for Multimodal Optimization  

PubMed Central

Interest in multimodal optimization is expanding rapidly, since many practical engineering problems demand the localization of multiple optima within a search space. On the other hand, the cuckoo search (CS) algorithm is a simple and effective global optimization algorithm which can not be directly applied to solve multimodal optimization problems. This paper proposes a new multimodal optimization algorithm called the multimodal cuckoo search (MCS). Under MCS, the original CS is enhanced with multimodal capacities by means of (1) the incorporation of a memory mechanism to efficiently register potential local optima according to their fitness value and the distance to other potential solutions, (2) the modification of the original CS individual selection strategy to accelerate the detection process of new local minima, and (3) the inclusion of a depuration procedure to cyclically eliminate duplicated memory elements. The performance of the proposed approach is compared to several state-of-the-art multimodal optimization algorithms considering a benchmark suite of fourteen multimodal problems. Experimental results indicate that the proposed strategy is capable of providing better and even a more consistent performance over existing well-known multimodal algorithms for the majority of test problems yet avoiding any serious computational deterioration. PMID:25147850

2014-01-01

175

Hybrid Artificial Fish Swarm Algorithm for Solving Ill-Conditioned Linear Systems of Equations  

NASA Astrophysics Data System (ADS)

Based on particle swarm optimization (PSO) and artificial fish swarm algorithm (AFSA), this paper proposes a hybrid artificial fish swarm algorithm (HAFSA). The method makes full use of the fast local convergence performance of PSO and the global convergence performance of AFSA, and then is used for solving ill-conditioned linear systems of equations. Finally, the numerical experiment results show that hybrid artificial fish swarm algorithm owns a better global convergence performance with a faster convergence rate. It is a new way to solve ill-conditioned linear systems of equations.

Zhou, Yongquan; Huang, Huajuan; Zhang, Junli

176

Full Glowworm Swarm Optimization Algorithm for Whole-Set Orders Scheduling in Single Machine  

PubMed Central

By analyzing the characteristics of whole-set orders problem and combining the theory of glowworm swarm optimization, a new glowworm swarm optimization algorithm for scheduling is proposed. A new hybrid-encoding schema combining with two-dimensional encoding and random-key encoding is given. In order to enhance the capability of optimal searching and speed up the convergence rate, the dynamical changed step strategy is integrated into this algorithm. Furthermore, experimental results prove its feasibility and efficiency. PMID:24294135

Yu, Zhang; Yang, Xiaomei

2013-01-01

177

Hybridization of decomposition and local search for multiobjective optimization.  

PubMed

Combining ideas from evolutionary algorithms, decomposition approaches, and Pareto local search, this paper suggests a simple yet efficient memetic algorithm for combinatorial multiobjective optimization problems: memetic algorithm based on decomposition (MOMAD). It decomposes a combinatorial multiobjective problem into a number of single objective optimization problems using an aggregation method. MOMAD evolves three populations: 1) population P(L) for recording the current solution to each subproblem; 2) population P(P) for storing starting solutions for Pareto local search; and 3) an external population P(E) for maintaining all the nondominated solutions found so far during the search. A problem-specific single objective heuristic can be applied to these subproblems to initialize the three populations. At each generation, a Pareto local search method is first applied to search a neighborhood of each solution in P(P) to update P(L) and P(E). Then a single objective local search is applied to each perturbed solution in P(L) for improving P(L) and P(E), and reinitializing P(P). The procedure is repeated until a stopping condition is met. MOMAD provides a generic hybrid multiobjective algorithmic framework in which problem specific knowledge, well developed single objective local search and heuristics and Pareto local search methods can be hybridized. It is a population based iterative method and thus an anytime algorithm. Extensive experiments have been conducted in this paper to study MOMAD and compare it with some other state-of-the-art algorithms on the multiobjective traveling salesman problem and the multiobjective knapsack problem. The experimental results show that our proposed algorithm outperforms or performs similarly to the best so far heuristics on these two problems. PMID:25222724

Ke, Liangjun; Zhang, Qingfu; Battiti, Roberto

2014-10-01

178

Firefly Algorithm for Continuous Constrained Optimization Tasks  

Microsoft Academic Search

The paper provides an insight into the improved novel metaheuristics of the Firefly Algorithm for constrained continuous optimization\\u000a tasks. The presented technique is inspired by social behavior of fireflies and the phenomenon of bioluminescent communication.\\u000a The first part of the paper is devoted to the detailed description of the existing algorithm. Then some suggestions for extending\\u000a the simple scheme of

Szymon ?ukasik; S?awomir ?ak

179

Comparison of optimization algorithms for piecewise linear discriminant analysis: application to Fourier transform infrared remote sensing measurements  

Microsoft Academic Search

Simplex optimization, simulated annealing, generalized simulated annealing, genetic algorithms, and a Simplex-genetic algorithm hybrid are compared for their ability to optimize piecewise linear discriminants. Nonparametric piecewise linear discriminant analysis (PLDA) is employed here to develop an automated detection scheme for Fourier transform infrared remote sensing interferogram data. Piecewise linear discriminants are computed and optimized for interferograms collected when sulfur hexafluoride,

Ronald E. Shaffer; Gary W. Small

1996-01-01

180

Optimization methods applied to hybrid vehicle design  

NASA Technical Reports Server (NTRS)

The use of optimization methods as an effective design tool in the design of hybrid vehicle propulsion systems is demonstrated. Optimization techniques were used to select values for three design parameters (battery weight, heat engine power rating and power split between the two on-board energy sources) such that various measures of vehicle performance (acquisition cost, life cycle cost and petroleum consumption) were optimized. The apporach produced designs which were often significant improvements over hybrid designs already reported on in the literature. The principal conclusions are as follows. First, it was found that the strategy used to split the required power between the two on-board energy sources can have a significant effect on life cycle cost and petroleum consumption. Second, the optimization program should be constructed so that performance measures and design variables can be easily changed. Third, the vehicle simulation program has a significant effect on the computer run time of the overall optimization program; run time can be significantly reduced by proper design of the types of trips the vehicle takes in a one year period. Fourth, care must be taken in designing the cost and constraint expressions which are used in the optimization so that they are relatively smooth functions of the design variables. Fifth, proper handling of constraints on battery weight and heat engine rating, variables which must be large enough to meet power demands, is particularly important for the success of an optimization study. Finally, the principal conclusion is that optimization methods provide a practical tool for carrying out the design of a hybrid vehicle propulsion system.

Donoghue, J. F.; Burghart, J. H.

1983-01-01

181

Algorithms with conic termination for nonlinear optimization  

SciTech Connect

This paper describes algorithms for unconstrained optimization which have the property of minimizing conic objective functions in a finite number of steps, when line searches are exact. This work extends the algorithms of Davidon and Gourgeon and Nocedal to general nonlinear objective functions, paying much attention to the practical behavior of the new methods. Three types of algorithms are described; they are extensions of the conjugate gradient method, the BFGS method and a limited memory BFGS method. The numerical results show that new methods are very effective in solving practical problems. 19 refs., 4 tabs.

Liu, D.C.; Nocedal, J.

1987-12-01

182

Algorithm Optimally Allocates Actuation of a Spacecraft  

NASA Technical Reports Server (NTRS)

A report presents an algorithm that solves the following problem: Allocate the force and/or torque to be exerted by each thruster and reaction-wheel assembly on a spacecraft for best performance, defined as minimizing the error between (1) the total force and torque commanded by the spacecraft control system and (2) the total of forces and torques actually exerted by all the thrusters and reaction wheels. The algorithm incorporates the matrix vector relationship between (1) the total applied force and torque and (2) the individual actuator force and torque values. It takes account of such constraints as lower and upper limits on the force or torque that can be applied by a given actuator. The algorithm divides the aforementioned problem into two optimization problems that it solves sequentially. These problems are of a type, known in the art as semi-definite programming problems, that involve linear matrix inequalities. The algorithm incorporates, as sub-algorithms, prior algorithms that solve such optimization problems very efficiently. The algorithm affords the additional advantage that the solution requires the minimum rate of consumption of fuel for the given best performance.

Motaghedi, Shi

2007-01-01

183

Hybrid response surface methodology-genetic algorithm optimization of ultrasound-assisted transesterification of waste oil catalysed by immobilized lipase on mesoporous silica/iron oxide magnetic core-shell nanoparticles.  

PubMed

The production ofbiodiesel by transesterification of waste cooking oil (WCO) to partially substitute petroleum diesel is one of the measures for solving the twin problems of environment pollution and energy demand. An environmentally benign process for the enzymatic transesterification using immobilized lipase has attracted considerable attention for biodiesel production. Here, a superparamagnetic, high surface area substrate for lipase immobilization is evaluated. These immobilization substrates are composed of mesoporous silica/superparamagnetic iron oxide core-shell nanoparticles. The effects of methanol ratio to WCO, lipase concentration, water content and reaction time on the synthesis of biodiesel were analysed by utilizing the response surface methodology (RSM). A quadratic response surface equation for calculating fatty acid methyl ester (FAME) content as the objective function was established based on experimental data obtained in accordance with the central composite design. The RSM-based model was then used as the fitness function for genetic algorithm (GA) to optimize its input space. Hybrid RSM-GA predicted the maximum FAME content (91%) at the optimum level of medium variables: methanol ratio to WCO, 4.34; lipase content, 43.6%; water content, 10.22%; and reaction time, 6h. Moreover, the immobilized lipase could be used for four times without considerable loss of the activity. PMID:24350474

Karimi, Mahmoud; Keyhani, Alireza; Akram, Asadolah; Rahman, Masoud; Jenkins, Bryan; Stroeve, Pieter

2013-01-01

184

Design of optimal correlation filters for hybrid vision systems  

NASA Technical Reports Server (NTRS)

Research is underway at the NASA Johnson Space Center on the development of vision systems that recognize objects and estimate their position by processing their images. This is a crucial task in many space applications such as autonomous landing on Mars sites, satellite inspection and repair, and docking of space shuttle and space station. Currently available algorithms and hardware are too slow to be suitable for these tasks. Electronic digital hardware exhibits superior performance in computing and control; however, they take too much time to carry out important signal processing operations such as Fourier transformation of image data and calculation of correlation between two images. Fortunately, because of the inherent parallelism, optical devices can carry out these operations very fast, although they are not quite suitable for computation and control type operations. Hence, investigations are currently being conducted on the development of hybrid vision systems that utilize both optical techniques and digital processing jointly to carry out the object recognition tasks in real time. Algorithms for the design of optimal filters for use in hybrid vision systems were developed. Specifically, an algorithm was developed for the design of real-valued frequency plane correlation filters. Furthermore, research was also conducted on designing correlation filters optimal in the sense of providing maximum signal-to-nose ratio when noise is present in the detectors in the correlation plane. Algorithms were developed for the design of different types of optimal filters: complex filters, real-value filters, phase-only filters, ternary-valued filters, coupled filters. This report presents some of these algorithms in detail along with their derivations.

Rajan, Periasamy K.

1990-01-01

185

Parallel Algorithms for Graph Optimization using Tree Decompositions  

SciTech Connect

Although many $\\cal{NP}$-hard graph optimization problems can be solved in polynomial time on graphs of bounded tree-width, the adoption of these techniques into mainstream scientific computation has been limited due to the high memory requirements of the necessary dynamic programming tables and excessive runtimes of sequential implementations. This work addresses both challenges by proposing a set of new parallel algorithms for all steps of a tree decomposition-based approach to solve the maximum weighted independent set problem. A hybrid OpenMP/MPI implementation includes a highly scalable parallel dynamic programming algorithm leveraging the MADNESS task-based runtime, and computational results demonstrate scaling. This work enables a significant expansion of the scale of graphs on which exact solutions to maximum weighted independent set can be obtained, and forms a framework for solving additional graph optimization problems with similar techniques.

Sullivan, Blair D [ORNL; Weerapurage, Dinesh P [ORNL; Groer, Christopher S [ORNL

2012-06-01

186

an optimized rendering algorithm for hardware implementation of openVG 2D vector graphics  

Microsoft Academic Search

An optimized rendering algorithm of the OpenVG 2D vector graphics for hardware implementation is presented in this paper. In the rendering algorithm we adopted a hybrid of raster and vector rendering, which uses vector rendering only within each scanline, to reduce both the number of external memory accesses and the computational complexity. We implemented a hardware accelerator with the proposed

Kilhyung Cha; Daewoong Kim; Soo-Ik Chae

2008-01-01

187

Two effective hybrid conjugate gradient algorithms based on modified BFGS updates  

Microsoft Academic Search

Based on two modified secant equations proposed by Yuan, and Li and Fukushima, we extend the approach proposed by Andrei,\\u000a and introduce two hybrid conjugate gradient methods for unconstrained optimization problems. Our methods are hybridizations\\u000a of Hestenes-Stiefel and Dai-Yuan conjugate gradient methods. Under proper conditions, we show that one of the proposed algorithms\\u000a is globally convergent for uniformly convex functions

Saman Babaie-Kafaki; Masoud Fatemi; Nezam Mahdavi-Amiri

188

GENERALIZED OPTIMIZATION ALGORITHM FOR SPEECH RECOGNITION TRANSDUCERS  

E-print Network

GENERALIZED OPTIMIZATION ALGORITHM FOR SPEECH RECOGNITION TRANSDUCERS Cyril Allauzen and Mehryar provide a common representation for the components of a speech recognition system. In previous work, we, determinization. However, not all weighted automata and transducers used in large- vocabulary speech recognition

Allauzen, Cyril

189

GENERALIZED OPTIMIZATION ALGORITHM FOR SPEECH RECOGNITION TRANSDUCERS  

E-print Network

GENERALIZED OPTIMIZATION ALGORITHM FOR SPEECH RECOGNITION TRANSDUCERS Cyril Allauzen and Mehryar provide a common representation for the components of a speech recognition system. In previous work, we, determinization. However, not all weighted automata and transducers used in large­ vocabulary speech recognition

Mohri, Mehryar

190

Lightweight telescope structure optimized by genetic algorithm  

Microsoft Academic Search

We designed the optics supporting structure (OSS) of a 3.8 m segmented mirror telescope by applying genetic algorithm optimization. The telescope is the first segmented mirror telescope in Japan whose primary mirror consists of 18 petal shaped segment mirrors. The whole mirror is supported by 54 actuators (3 actuators per each segment). In order to realize light-weight and stiff telescope

Mikio Kurita; Hiroshi Ohmori; Masashi Kunda; Hiroaki Kawamura; Noriaki Noda; Takayuki Seki; Yuji Nishimura; Michitoshi Yoshida; Shuji Sato; Tetsuya Nagata

2010-01-01

191

Algorithm for fixed-range optimal trajectories  

NASA Technical Reports Server (NTRS)

An algorithm for synthesizing optimal aircraft trajectories for specified range was developed and implemented in a computer program written in FORTRAN IV. The algorithm, its computer implementation, and a set of example optimum trajectories for the Boeing 727-100 aircraft are described. The algorithm optimizes trajectories with respect to a cost function that is the weighted sum of fuel cost and time cost. The optimum trajectory consists at most of a three segments: climb, cruise, and descent. The climb and descent profiles are generated by integrating a simplified set of kinematic and dynamic equations wherein the total energy of the aircraft is the independent or time like variable. At each energy level the optimum airspeeds and thrust settings are obtained as the values that minimize the variational Hamiltonian. Although the emphasis is on an off-line, open-loop computation, eventually the most important application will be in an on-board flight management system.

Lee, H. Q.; Erzberger, H.

1980-01-01

192

A Simulative Bionic Intelligent Optimization Algorithm: Artificial Searching Swarm Algorithm and Its Performance Analysis  

Microsoft Academic Search

In this paper, a novel optimization algorithm - artificial searching swarm algorithm (ASSA) is presented by analyzing the operating principle and uniform framework of the bionic intelligent optimization algorithm. 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

Tanggong Chen

2009-01-01

193

Fuzzy Logic for Combining Particle Swarm Optimization and Genetic Algorithms: Preliminary Results  

NASA Astrophysics Data System (ADS)

We describe in this paper a new hybrid approach for mathematical function optimization combining Particle Swarm Optimization (PSO) and Genetic Algorithms (GAs) using Fuzzy Logic to integrate the results. The new evolutionary method combines the advantages of PSO and GA to give us an improved PSO+GA hybrid method. Fuzzy Logic is used to combine the results of the PSO and GA in the best way possible. The new hybrid PSO+GA approach is compared with the PSO and GA methods with a set of benchmark mathematical functions. The new hybrid PSO +GA method is shown to be superior than the individual evolutionary methods.

Valdez, Fevrier; Melin, Patricia; Castillo, Oscar

194

A hybrid of the genetic algorithm and concurrent simplex  

E-print Network

the reason why it ran too slowly for our particular problem, and will touch upon some of the earlier attempts at faster ? converging hybrid genetic algorithms. Chapter III will propose a new hybrid algorithm which is better suited to speeding up... the convergence time on the metabolic modeling problem than past solutions. A. The Innards of the Genetic Algorithm A genetic algorithm is a kind of search; it can be put to most of the same problems to which breadth-first or depth-first searches are applied...

Randolph, David Ethan

2012-06-07

195

Library design using genetic algorithms for catalyst discovery and optimization  

NASA Astrophysics Data System (ADS)

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 hybrid algorithm 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

2005-06-01

196

Hybrid Learning Using Genetic Algorithms and Decision Trees for Pattern Classification  

Microsoft Academic Search

This paper introduces a hybrid learning methodology that integrates genetic algorithms (GAs) and decision tree learning (ID3) in order to evolve optimal subsets of discriminatory features for robust pattern classification. A GA is used to search the space of all possible subsets of a large set of candidate discrimination features. For a given feature subset, ID3 is invoked to produce

Jerzy W. Bala; Jeffrey Huang; Haleh Vafaie; Kenneth Dejong; Harry Wechsler

1995-01-01

197

Desktop and HIL Validation of Hybrid-Electric-Vehicle Battery-Management-System Algorithms  

Microsoft Academic Search

The battery management system (BMS) of a hybrid- electric-vehicle (HEV) battery pack comprises hardware and software to monitor pack status and optimize performance. One of its important functions is to execute algorithms that continuously estimate battery state-of-charge (SOC), state-of-health (SOH), and available power. The primary difficulty when validating these algorithms is that there are no sensors that can measure SOC,

Gregory L. Plett; Robert Billings; Martin J. Klein

198

An effective hybrid genetic algorithm for the job shop scheduling problem  

Microsoft Academic Search

From the computational point of view, the job shop scheduling problem (JSP) is one of the most notoriously intractable NP-hard\\u000a optimization problems. This paper applies an effective hybrid genetic algorithm for the JSP. We proposed three novel features\\u000a for this algorithm to solve the JSP. Firstly, a new full active schedule (FAS) procedure based on the operation-based representation\\u000a is presented

Chaoyong Zhang; Yunqing Rao; Peigen Li

2008-01-01

199

CPU-GPU hybrid accelerating the Zuker algorithm for RNA secondary structure prediction applications  

PubMed Central

Background Prediction of ribonucleic acid (RNA) secondary structure remains one of the most important research areas in bioinformatics. The Zuker algorithm is one of the most popular methods of free energy minimization for RNA secondary structure prediction. Thus far, few studies have been reported on the acceleration of the Zuker algorithm on general-purpose processors or on extra accelerators such as Field Programmable Gate-Array (FPGA) and Graphics Processing Units (GPU). To the best of our knowledge, no implementation combines both CPU and extra accelerators, such as GPUs, to accelerate the Zuker algorithm applications. Results In this paper, a CPU-GPU hybrid computing system that accelerates Zuker algorithm applications for RNA secondary structure prediction is proposed. The computing tasks are allocated between CPU and GPU for parallel cooperate execution. Performance differences between the CPU and the GPU in the task-allocation scheme are considered to obtain workload balance. To improve the hybrid system performance, the Zuker algorithm is optimally implemented with special methods for CPU and GPU architecture. Conclusions Speedup of 15.93× over optimized multi-core SIMD CPU implementation and performance advantage of 16% over optimized GPU implementation are shown in the experimental results. More than 14% of the sequences are executed on CPU in the hybrid system. The system combining CPU and GPU to accelerate the Zuker algorithm is proven to be promising and can be applied to other bioinformatics applications. PMID:22369626

2012-01-01

200

?minimax: An Optimally Randomized MINIMAX Algorithm.  

PubMed

This paper proposes a simple extension of the celebrated MINIMAX algorithm used in zero-sum two-player games, called ?minimax. The ?minimax algorithm allows controlling the strength of an artificial rival by randomizing its strategy in an optimal way. In particular, the randomized shortest-path framework is applied for biasing the artificial intelligence (AI) adversary toward worse or better solutions, therefore controlling its strength. In other words, our model aims at introducing/implementing bounded rationality to the MINIMAX algorithm. This framework takes into account all possible strategies by computing an optimal tradeoff between exploration (quantified by the entropy spread in the tree) and exploitation (quantified by the expected cost to an end game) of the game tree. As opposed to other tree-exploration techniques, this new algorithm considers complete paths of a tree (strategies) where a given entropy is spread. The optimal randomized strategy is efficiently computed by means of a simple recurrence relation while keeping the same complexity as the original MINIMAX. As a result, the ?minimax implements a nondeterministic strength-adapted AI opponent for board games in a principled way, thus avoiding the assumption of complete rationality. Simulations on two common games show that ?minimax behaves as expected. PMID:22893439

García Díez, Silvia; Laforge, Jérôme; Saerens, Marco

2012-08-01

201

Spacecraft long-duration phasing maneuver optimization using hybrid approach  

NASA Astrophysics Data System (ADS)

Before a manned orbital rendezvous mission, the target spacecraft usually performs several maneuvers to adjust the initial phase angle of the orbital rendezvous and to coordinate the injection of the chaser. This maneuvering process is referred to as the "target phasing mission". This target phasing presents an orbital long-duration two-point boundary value problem. Further, when the maneuver revolution numbers are used as design variables, the target phasing maneuver's optimization becomes a mixed integer nonlinear programming problem. This paper presents a new optimization method for this phasing maneuver mission, employing a hybrid approach. First, we provide an approximate phasing optimization problem that considers the phase angle influences of node drift and orbital altitude decay. This problem is then optimized using a hybrid approach that integrates branch-and-bound and sequential quadratic programming. Second, a shooting iteration method is adopted to improve the solution to the approximate problem in order to satisfy the terminal constraints of high-precision numerical integration. The proposed method is then applied to an operational target phasing maneuver problem. The results lead to four major conclusions: (1) The proposed approximate phasing optimization model presents a good approximation of the operational mission. (2) The hybrid optimization approach can solve the approximate problem effectively, and the shooting iteration used to arrive at a high-precision solution converges steadily and rapidly. (3) Compared with mixed-code genetic algorithm, the proposed method can obtain a similar result with a lower computation cost and, compared with the approximate model that does not consider node drift and orbital altitude decay, the proposed method has better convergence efficiency. (4) The terminal time of target phasing remains almost constant when the initial semi-major axis increases in a limited interval, and the transition appears only when there is a change in the terminal revolution number.

Zhang, Jin; Wang, Xiang; Ma, Xiao-bing; Tang, Yi; Huang, Hai-bing

2012-03-01

202

Solving the Course Timetabling Problem with a Hybrid Heuristic Algorithm  

E-print Network

Solving the Course Timetabling Problem with a Hybrid Heuristic Algorithm Zhipeng L�u1,2 and Jin zhipeng.lui@gmai.com, hao@info.univ-angers.fr Abstract. The problem of curriculum-based course timetabling known results on two problem formulations. Keywords: Timetabling, hybrid heuristic, tabu search

Hao, Jin-Kao

203

Hybrid evolutionary algorithm with Hermite radial basis function interpolants for computationally expensive adjoint solvers  

Microsoft Academic Search

In this paper, we present an evolutionary algorithm hybridized with a gradient-based optimization technique in the spirit\\u000a of Lamarckian learning for efficient design optimization. In order to expedite gradient search, we employ local surrogate\\u000a models that approximate the outputs of a computationally expensive Euler solver. Our focus is on the case when an adjoint\\u000a Euler solver is available for efficiently

Y. S. Ong; K. Y. Lum; P. B. Nair

2008-01-01

204

Hybrid Genetic Algorithm for Flow Shop Scheduling Problem  

Microsoft Academic Search

The flow shop scheduling problem (FSSP) is a NP-HARD combinatorial problem with strong industrial background. Among the meta-heuristics, genetic algorithms attracted a lot of attention. However, lacking the major evolution direction, the effectiveness of regular genetic algorithm is restricted. In this paper, the particle swarm optimization algorithm (PSO) is introduced for better initial group. By combining PSO with GA, a

Jianchao Tang; Guoji Zhang; Binbin Lin; Bixi Zhang

2010-01-01

205

Intelligent perturbation algorithms for space scheduling optimization  

NASA Technical Reports Server (NTRS)

The optimization of space operations is examined in the light of optimization heuristics for computer algorithms and iterative search techniques. Specific attention is given to the search concepts known collectively as intelligent perturbation algorithms (IPAs) and their application to crew/resource allocation problems. IPAs iteratively examine successive schedules which become progressively more efficient, and the characteristics of good perturbation operators are listed. IPAs can be applied to aerospace systems to efficiently utilize crews, payloads, and resources in the context of systems such as Space-Station scheduling. A program is presented called the MFIVE Space Station Scheduling Worksheet which generates task assignments and resource usage structures. The IPAs can be used to develop flexible manifesting and scheduling for the Industrial Space Facility.

Kurtzman, Clifford R.

1990-01-01

206

Hybrid Learning Using Multi-objective Genetic Algorithms and Decision Trees for Power Quality Disturbance Pattern Recognition  

Microsoft Academic Search

The objective of this work is to exploit the potential of latest pattern recognition techniques in power quality applications. This paper presents a novel hybrid pattern recognizer for classification of power quality disturbances. The hybrid learning methodology integrates a multiobjective genetic algorithm (GA) and decision trees (CART) in order to evolve optimal subsets of discriminatory features for robust pattern classification.

B. V. Krishna; K. Baskaran

2007-01-01

207

A Hybrid Monkey Search Algorithm for Clustering Analysis  

PubMed Central

Clustering is a popular data analysis and data mining technique. The k-means clustering algorithm is one of the most commonly used methods. However, it highly depends on the initial solution and is easy to fall into local optimum solution. In view of the disadvantages of the k-means method, this paper proposed a hybrid monkey algorithm based on search operator of artificial bee colony algorithm for clustering analysis and experiment on synthetic and real life datasets to show that the algorithm has a good performance than that of the basic monkey algorithm for clustering analysis. PMID:24772039

Chen, Xin; Zhou, Yongquan; Luo, Qifang

2014-01-01

208

A hybrid monkey search algorithm for clustering analysis.  

PubMed

Clustering is a popular data analysis and data mining technique. The k-means clustering algorithm is one of the most commonly used methods. However, it highly depends on the initial solution and is easy to fall into local optimum solution. In view of the disadvantages of the k-means method, this paper proposed a hybrid monkey algorithm based on search operator of artificial bee colony algorithm for clustering analysis and experiment on synthetic and real life datasets to show that the algorithm has a good performance than that of the basic monkey algorithm for clustering analysis. PMID:24772039

Chen, Xin; Zhou, Yongquan; Luo, Qifang

2014-01-01

209

EVALUATING THE QUALITY OF PIPELINE OPTIMIZATION ALGORITHMS  

Microsoft Academic Search

Abstract This paper,discusses,how,to,generate,good,lower,bounds,for the fuel cost minimization problem,arising from,the steady-state,gas pipeline,network,flows. These lower,bounds,may be wed,to evaluate,the quality of the solutions,provided,by the present,generation,of pipeline optimization,algorithms. Mathematical,models,of steady-state gas pipeline network,flows are complicated,by the existence

E. Andrew Boyd; L. Ridgway Scott; Suming Wu

210

Optimal design of structures for earthquake loads by a hybrid RBF-BPSO method  

NASA Astrophysics Data System (ADS)

The optimal seismic design of structures requires that time history analyses (THA) be carried out repeatedly. This makes the optimal design process inefficient, in particular, if an evolutionary algorithm is used. To reduce the overall time required for structural optimization, two artificial intelligence strategies are employed. In the first strategy, radial basis function (RBF) neural networks are used to predict the time history responses of structures in the optimization flow. In the second strategy, a binary particle swarm optimization (BPSO) is used to find the optimum design. Combining the RBF and BPSO, a hybrid RBF-BPSO optimization method is proposed in this paper, which achieves fast optimization with high computational performance. Two examples are presented and compared to determine the optimal weight of structures under earthquake loadings using both exact and approximate analyses. The numerical results demonstrate the computational advantages and effectiveness of the proposed hybrid RBF-BPSO optimization method for the seismic design of structures.

Salajegheh, Eysa; Gholizadeh, Saeed; Khatibinia, Mohsen

2008-03-01

211

Optical flow optimization using parallel genetic algorithm  

NASA Astrophysics Data System (ADS)

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.

Zavala-Romero, Olmo; Botella, Guillermo; Meyer-Bäse, Anke; Meyer Base, Uwe

2011-06-01

212

A fast hybrid algorithm for exoplanetary transit searches  

Microsoft Academic Search

We present a fast and efficient hybrid algorithm for selecting exoplanetary candidates from wide-field transit surveys. Our method is based on the widely used SysRem and Box Least-Squares (BLS) algorithms. Patterns of systematic error that are common to all stars on the frame are mapped and eliminated using the SysRem algorithm. The remaining systematic errors caused by spatially localized flat-fielding

A. Collier Cameron; D. Pollacco; T. A. Lister; R. G. West; D. M. Wilson; F. Pont; D. J. Christian; W. I. Clarkson; B. Enoch; A. Evans; A. Fitzsimmons; C. A. Haswell; C. Hellier; S. T. Hodgkin; K. Horne; J. Irwin; S. R. Kane; F. P. Keenan; A. J. Norton; N. R. Parley; J. Osborne; R. Ryans; I. Skillen; P. J. Wheatley

2006-01-01

213

Global optimization: Algorithms, complexity, and applications  

SciTech Connect

Global optimization problems appear in many diverse areas of operations research, management science, economics and engineering. Typical applications include allocation and location problems, economies of scale, transportation problems, engineering design and control chip design and database problems. Standard nonlinear optimization methods will usually obtain a local solution or a stationary point when applied to a global optimization problem. The problem of designing algorithms that compute global solutions is in general very difficult because of the lack of criteria in deciding whether a local solution is global or not. Moreover, nonlinear problems may have an exponential number of local solutions, which are not global. Active research in the past two decades has produced many deterministic and stochastic methods for computing global solutions. In this talk, we will focus on deterministic methods which include branch and bound algorithms, homotopy methods, path following techniques, interval analysis methods, and a variety of approximate techniques. In addition, we are going to discuss related complexity questions and implementation issues regarding many of the proposed algorithms.

Pardalos, P.; Gibbons, L.; Hearn, D.

1994-12-31

214

Analysis and optimization of hybrid electric vehicle thermal management systems  

NASA Astrophysics Data System (ADS)

In this study, the thermal management system of a hybrid electric vehicle is optimized using single and multi-objective evolutionary algorithms in order to maximize the exergy efficiency and minimize the cost and environmental impact of the system. The objective functions are defined and decision variables, along with their respective system constraints, are selected for the analysis. In the multi-objective optimization, a Pareto frontier is obtained and a single desirable optimal solution is selected based on LINMAP decision-making process. The corresponding solutions are compared against the exergetic, exergoeconomic and exergoenvironmental single objective optimization results. The results show that the exergy efficiency, total cost rate and environmental impact rate for the baseline system are determined to be 0.29, ¢28 h-1 and 77.3 mPts h-1 respectively. Moreover, based on the exergoeconomic optimization, 14% higher exergy efficiency and 5% lower cost can be achieved, compared to baseline parameters at an expense of a 14% increase in the environmental impact. Based on the exergoenvironmental optimization, a 13% higher exergy efficiency and 5% lower environmental impact can be achieved at the expense of a 27% increase in the total cost.

Hamut, H. S.; Dincer, I.; Naterer, G. F.

2014-02-01

215

Crowding clustering genetic algorithm for multimodal function optimization  

Microsoft Academic Search

Interest in multimodal function optimization is expanding rapidly since real-world optimization problems often require location of multiple optima in a search space. In this paper, we propose a novel genetic algorithm which combines crowding and clustering for multimodal function optimization, and analyze convergence properties of the algorithm. The crowding clustering genetic algorithm employs standard crowding strategy to form multiple niches

Qing Ling; Gang Wu; Zaiyue Yang; Qiuping Wang

2008-01-01

216

A New Active Set Algorithm for Box Constrained Optimization  

Microsoft Academic Search

An active set algorithm (ASA) for box constrained optimization is developed. The algorithm consists ofa nonmonotone gradient projection step, an unconstrained optimization step, and a set ofrules f or branching between the two steps. Global convergence to a stationary point is established. For a nondegenerate stationary point, the algorithm eventually reduces to uncon- strained optimization without restarts. Similarly, for a

William W. Hager; Hongchao Zhang

2006-01-01

217

Optimal hydrogenerator governor tuning with a genetic algorithm  

Microsoft Academic Search

The authors investigate the application of a genetic algorithm for optimizing the gains of a proportional-plus-integral controller for a hydrogenerator plant. The genetic algorithm was 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 algorithm optimization process. It is shown that analog plant simulation

J. E. Lansberry; L. Wozniak; D. E. Goldberg

1992-01-01

218

Improved Genetic Algorithms to Solving Constrained Optimization Problems  

Microsoft Academic Search

The slow convergence speed and the lack of effective constraint handling strategies are the major concerns when applying genetic algorithms (Gas) to constrained optimization problem. An improved genetic algorithm was proposed by dividing population into three parts: optimal subpopulation, elitists subpopulation and spare subpopulation. We applied genetic algorithm on three subpopulations with different evolutionary strategies. Isolation of optimal subpopulation was

Zhu Can; Liang Xi-Ming; Zhou Shu-renhu

2009-01-01

219

A Hybrid Grouping Genetic Algorithm for Multiprocessor Scheduling  

NASA Astrophysics Data System (ADS)

This paper describes a hybrid grouping genetic algorithm for a multiprocessor scheduling problem, where a list of tasks has to be scheduled on identical parallel processors. Each task in the list is defined by a release date, a due date and a processing time. The objective is to minimize the number of processors used while respecting the constraints imposed by release dates and due dates. We have compared our hybrid approach with two heuristic methods reported in the literature. Computational results show the superiority of our hybrid approach over these two approaches. Our hybrid approach obtained better quality solutions in shorter time.

Singh, Alok; Sevaux, Marc; Rossi, André

220

Bell-Curve Based Evolutionary Optimization Algorithm  

NASA Technical Reports Server (NTRS)

The paper presents an optimization algorithm that falls in the category of genetic, or evolutionary algorithms. While the bit exchange is the basis of most of the Genetic Algorithms (GA) in research and applications in America, some alternatives, also in the category of evolutionary algorithms, but use a direct, geometrical approach have gained popularity in Europe and Asia. The Bell-Curve Based Evolutionary Algorithm (BCB) is in this alternative category and is distinguished by the use of a combination of n-dimensional geometry and the normal distribution, the bell-curve, in the generation of the offspring. The tool for creating a child is a geometrical construct comprising a line connecting two parents and a weighted point on that line. The point that defines the child deviates from the weighted point in two directions: parallel and orthogonal to the connecting line, the deviation in each direction obeying a probabilistic distribution. Tests showed satisfactory performance of BCB. The principal advantage of BCB is its controllability via the normal distribution parameters and the geometrical construct variables.

Sobieszczanski-Sobieski, J.; Laba, K.; Kincaid, R.

1998-01-01

221

Optimizing the specificity of nucleic acid hybridization  

PubMed Central

The specific hybridization of complementary sequences is an essential property of nucleic acids, enabling diverse biological and biotechnological reactions and functions. However, the specificity of nucleic acid hybridization is compromised for long strands, except near the melting temperature. Here, we analytically derived the thermodynamic properties of a hybridization probe that would enable near-optimal single-base discrimination and perform robustly across diverse temperature, salt and concentration conditions. We rationally designed ‘toehold exchange’ probes that approximate these properties, and comprehensively tested them against five different DNA targets and 55 spurious analogues with energetically representative single-base changes (replacements, deletions and insertions). These probes produced discrimination factors between 3 and 100+ (median, 26). Without retuning, our probes function robustly from 10 °C to 37 °C, from 1 mM Mg2+ to 47 mM Mg2+, and with nucleic acid concentrations from 1 nM to 5 ?M. Experiments with RNA also showed effective single-base change discrimination. PMID:22354435

Zhang, David Yu; Chen, Sherry Xi; Yin, Peng

2014-01-01

222

A hybrid algorithm and its applications to fuzzy logic modeling of nonlinear systems  

NASA Astrophysics Data System (ADS)

System models allow us to simulate and analyze system dynamics efficiently. Most importantly, system models allow us to make prediction about system behaviors and to perform system parametric variation analysis without having to build the actual systems. The fuzzy logic modeling technique has been successfully applied in complex nonlinear system modeling such as unsteady aerodynamics modeling etc. recently. However, the current forward search algorithm to identify fuzzy logic model structures is very time-consuming. It is not unusual to spend several days or even a few weeks in computer CPU time to obtain better nonlinear system model structures by this forward search. Moreover, how to speed up the fuzzy logic model parameter identification process is also challenging when the number of influencing variables of nonlinear systems is large. To solve these problems, a hybrid algorithm for the nonlinear system modeling is proposed, formalized, implemented, and evaluated in this dissertation. By combining the fuzzy logic modeling technique with genetic algorithms, the developed hybrid algorithm is applied to both fuzzy logic model structure identification and model parameter identification. In the model structure identification process, the hybrid algorithm has the ability to find feasible structures more efficiently and effectively than the forward search. In the model parameter identification process (by using Newton gradient descent algorithm), the proposed hybrid algorithm incorporates genetic search algorithm to dynamically select convergence factors. It has the advantages of quick search yet maintains the monotonically convergent properties of the Newton gradient descent algorithm. To evaluate the properties of the developed hybrid algorithm, a nonlinear, unsteady aerodynamic normal force model with a complex system involving fourteen influencing variables is established from flight data. The results show that this hybrid algorithm can identify the aerodynamic model structures much quicker than the forward search. In addition, the results also show that this hybrid algorithm can identify model parameters much quicker than the one with fixed and arbitrary convergence factors. Finally, an application of the fuzzy logic modeling technique to Kansas Arbuckle oil well performance analysis is performed. It gives oil operators a powerful decision-making tool for candidate-well selection and treatment to optimize performance.

Wang, Zhongjun

223

MIP Models and Hybrid Algorithms for Simultaneous Job Splitting and Scheduling on Unrelated Parallel Machines  

PubMed Central

We developed mixed integer programming (MIP) models and hybrid genetic-local search algorithms for the scheduling problem of unrelated parallel machines with job sequence and machine-dependent setup times and with job splitting property. The first contribution of this paper is to introduce novel algorithms which make splitting and scheduling simultaneously with variable number of subjobs. We proposed simple chromosome structure which is constituted by random key numbers in hybrid genetic-local search algorithm (GAspLA). Random key numbers are used frequently in genetic algorithms, but it creates additional difficulty when hybrid factors in local search are implemented. We developed algorithms that satisfy the adaptation of results of local search into the genetic algorithms with minimum relocation operation of genes' random key numbers. This is the second contribution of the paper. The third contribution of this paper is three developed new MIP models which are making splitting and scheduling simultaneously. The fourth contribution of this paper is implementation of the GAspLAMIP. This implementation let us verify the optimality of GAspLA for the studied combinations. The proposed methods are tested on a set of problems taken from the literature and the results validate the effectiveness of the proposed algorithms. PMID:24977204

Ozmutlu, H. Cenk

2014-01-01

224

MIP models and hybrid algorithms for simultaneous job splitting and scheduling on unrelated parallel machines.  

PubMed

We developed mixed integer programming (MIP) models and hybrid genetic-local search algorithms for the scheduling problem of unrelated parallel machines with job sequence and machine-dependent setup times and with job splitting property. The first contribution of this paper is to introduce novel algorithms which make splitting and scheduling simultaneously with variable number of subjobs. We proposed simple chromosome structure which is constituted by random key numbers in hybrid genetic-local search algorithm (GAspLA). Random key numbers are used frequently in genetic algorithms, but it creates additional difficulty when hybrid factors in local search are implemented. We developed algorithms that satisfy the adaptation of results of local search into the genetic algorithms with minimum relocation operation of genes' random key numbers. This is the second contribution of the paper. The third contribution of this paper is three developed new MIP models which are making splitting and scheduling simultaneously. The fourth contribution of this paper is implementation of the GAspLAMIP. This implementation let us verify the optimality of GAspLA for the studied combinations. The proposed methods are tested on a set of problems taken from the literature and the results validate the effectiveness of the proposed algorithms. PMID:24977204

Eroglu, Duygu Yilmaz; Ozmutlu, H Cenk

2014-01-01

225

Towards enhancement of performance of K-means clustering using nature-inspired optimization algorithms.  

PubMed

Traditional K-means clustering algorithms have the drawback of getting stuck at local optima that depend on the random values of initial centroids. Optimization algorithms have their advantages in guiding iterative computation to search for global optima while avoiding local optima. The algorithms help speed up the clustering process by converging into a global optimum early with multiple search agents in action. Inspired by nature, some contemporary optimization algorithms which include Ant, Bat, Cuckoo, Firefly, and Wolf search algorithms mimic the swarming behavior allowing them to cooperatively steer towards an optimal objective within a reasonable time. It is known that these so-called nature-inspired optimization algorithms have their own characteristics as well as pros and cons in different applications. When these algorithms are combined with K-means clustering mechanism for the sake of enhancing its clustering quality by avoiding local optima and finding global optima, the new hybrids are anticipated to produce unprecedented performance. In this paper, we report the results of our evaluation experiments on the integration of nature-inspired optimization methods into K-means algorithms. In addition to the standard evaluation metrics in evaluating clustering quality, the extended K-means algorithms that are empowered by nature-inspired optimization methods are applied on image segmentation as a case study of application scenario. PMID:25202730

Fong, Simon; Deb, Suash; Yang, Xin-She; Zhuang, Yan

2014-01-01

226

Application of hybrid evolutionary algorithms to low exhaust emission diesel engine design  

NASA Astrophysics Data System (ADS)

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 hybrid algorithm was validated using standard test functions. The results showed that the hybrid algorithm 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.

Jeong, S.; Obayashi, S.; Minemura, Y.

2008-01-01

227

SFC Optimization for Aero Engine Based on Hybrid GA-SQP Method  

NASA Astrophysics Data System (ADS)

This study focuses on on-line specific fuel consumption (SFC) optimization of aero engines. For solving this optimization problem, a nonlinear pneumatic and thermodynamics model of the aero engine is built and a hybrid optimization technique which is formed by combining the genetic algorithm (GA) and the sequential quadratic programming (SQP) is presented. The ability of standard GA and standard SQP in solving this type of problem is investigated. It has been found that, although the SQP is fast, very little SFC reductions can be obtained. The GA is able to solve the problem well but a lot of computational time is needed. The presented hybrid GA-SQP gives a good SFC optimization effect and saves 76.6% computational time when compared to the standard GA. It has been shown that the hybrid GA-SQP is a more effective and higher real-time method for SFC on-line optimization of the aero engine.

Li, Jie; Fan, Ding; Sreeram, Victor

2013-12-01

228

Casting riser design optimization using genetic algorithms  

SciTech Connect

The design of rigging systems for castings in the foundry is largely based on past experience and empirical rules. Recent literature shows that attempts are being made to adopt a more scientific approach towards rigging design (location and size of risers, proper orientation of the casting, determination of parting plane, etc.) through the use of rule-based expert systems, process simulation and other tools. Riser design is a key element in the optimization of the overall rigging system since riser designs with large safety margins reduce yield and increase cost. This paper describes a methodology to optimize the riser design. A genetic algorithm is used for simplicity as well as robustness. Values of selected riser design parameters are examined using a modulus based approach to optimize the riser yield while achieving functional performance (i.e. effectiveness of the riser to adequately feed regions where shrinkage-type defects have a tendency of forming). Since the optimization is carried out on the solid model of the riser, the resultant design of the riser, together with the casting can be directly sent to a rapid prototyping system for production of a pattern.

Guleyupoglu, S.; Upadhya, G.; Paul, A.J.; Yu, K.O. [Concurrent Technologies Corp., Johnstown, PA (United States); Hill, J.L. [Univ. of Alabama, Tuscaloosa, AL (United States). Engineering Science and Mechanics Dept.

1995-12-31

229

Hybrid methods for interplanetary low-thrust trajectory optimization  

E-print Network

Hybrid methods for interplanetary low-thrust trajectory optimization are proposed. These methods are combinations of selected, existing methods for trajectory optimization. The focus of this thesis is to obtain solutions to a class of trajectories...

Aroonwilairut, Krisada

2012-06-07

230

Parametric optimization of hybrid car engines (Final version, published in Optimization and  

E-print Network

the problem of optimal design of hybrid car engines which combine thermal and electric power. The optimal of a car using thermal and electrical energy, which can be combined in different ways. More preciselyParametric optimization of hybrid car engines (Final version, published in Optimization

Bonnans, Frédéric

231

Method of mechanism synthesis by hybrid genetic algorithm  

E-print Network

algorithm was utilized to search for the global minima. A benefit of the genetic algorithm method is that in this search for the global minima it will also locate a family of alternative solutions that possess near optimal outputs. Having these alternative...

O'Neil, Robert Anthony

2012-06-07

232

A Hybrid Adaptive Search Algorithm for Fast Block Motion Estimation  

Microsoft Academic Search

This paper proposes a hybrid adaptive search algorithm (HASA) for block-based motion estimation. The proposed algorithm exploits the correlation between the block distortion measure (BDM) of the search origin (0,O) and its displacement from the motion vector to predict the range of motion. Based on the predicted motion type and the center-biased statistical distribution of motion vectors in low bit

Chok-Kwan Cheung; Lai-Man Po

1996-01-01

233

Instrument design and optimization using genetic algorithms  

SciTech Connect

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.

Hoelzel, Robert; Bentley, Phillip M.; Fouquet, Peter [Institut Laue-Langevin, BP 156, F-38042 Grenoble Cedex 9 (France); Hahn-Meitner Institut, Glienicker Strasse 100, D-14109 Berlin (Germany); Institut Laue-Langevin, BP 156, F-38042, Grenoble Cedex 9 (France)

2006-10-15

234

The Leap-Frog Algorithm and Optimal Control: Theoretical Aspects  

E-print Network

The Leap-Frog Algorithm and Optimal Control: Theoretical Aspects C. Yal#24;c#16;n Kaya School@maths.uwa.edu.au Abstract The Leap-Frog Algorithm was originally devised to #12;nd geodesics in connected complete with generalizing the mathematical rigour of the leap-frog algorithm to a class of optimal control problems

Noakes, Lyle

235

Search the Optimal Preference of Affinity Propagation Algorithm  

Microsoft Academic Search

In order to improve the clustering quality of the Affinity Propagation algorithm further and get more accurate number of clusters, this paper proposed a novel algorithm based on the Particles Swarm Optimization, which used In-Group Proportion index as fitness function to search the optimal preference of Affinity Propagation algorithm. Experimental results show that the predicted results had been tested with

Yi Zhong; Ming Zheng; Jianan Wu; Wei Shen; You Zhou; Chunguang Zhou

2012-01-01

236

Experimental Comparisons of Derivative Free Optimization Algorithms1  

E-print Network

-Box Optimization (BBO). 1 Invited Paper at the 8th International Symposium on Experimental Algorithms, June 3 International Symposium on Experimental Algorithms, Dortmund : Germany (2009)" #12;Because BBO is a frequent situation, many optimization methods (aka search algorithms) have been proposed to tackle BBO problems

Paris-Sud XI, Université de

237

GADO: A GENETIC ALGORITHM FOR CONTINUOUS DESIGN OPTIMIZATION  

E-print Network

of the Seventh International Conference on Genetic Algorithms, with Haym Hirsh as co­author [ Rasheed and HirshGADO: A GENETIC ALGORITHM FOR CONTINUOUS DESIGN OPTIMIZATION BY KHALED MOHAMED RASHEED GADO: A Genetic Algorithm for Continuous Design Optimization by Khaled Mohamed Rasheed Dissertation

Rasheed, Khaled

238

Real Coded Genetic Algorithm Optimization of Long Term Reservoir Operation  

Microsoft Academic Search

An optimization and simulation model holds promise as an efficient and robust method for long term reservoir operation, an increasingly important facet of managing water resources. Recently, genetic algorithms have been demonstrated to be highly effective optimization methods. According to previous studies, a real coded genetic algorithm (RGA) has many advantages over a binary coded genetic algorithm. Accordingly, this work

Li Chen

2003-01-01

239

Doherty Amplifier Optimization Using Robust Genetic Algorithm and Unscented Transform  

E-print Network

Doherty Amplifier Optimization Using Robust Genetic Algorithm and Unscented Transform Marcos L@eee.ufg.br, granato@eee.ufg.br Abstract-- A robust genetic circuit optimizer using Unscented Transform and Non-dominated Sorting Genetic Algorithm-II is presented. The algorithm provides significant decrease in compu- tational

Paris-Sud XI, Université de

240

Constrained Multi-Level Algorithm for Trajectory Optimization  

NASA Astrophysics Data System (ADS)

The emphasis on low cost access to space inspired many recent developments in the methodology of trajectory optimization. Ref.1 uses a spectral patching method for optimization, where global orthogonal polynomials are used to describe the dynamical constraints. A two-tier approach of optimization is used in Ref.2 for a missile mid-course trajectory optimization. A hybrid analytical/numerical approach is described in Ref.3, where an initial analytical vacuum solution is taken and gradually atmospheric effects are introduced. Ref.4 emphasizes the fact that the nonlinear constraints which occur in the initial and middle portions of the trajectory behave very nonlinearly with respect the variables making the optimization very difficult to solve in the direct and indirect shooting methods. The problem is further made complex when different phases of the trajectory have different objectives of optimization and also have different path constraints. Such problems can be effectively addressed by multi-level optimization. In the multi-level methods reported so far, optimization is first done in identified sub-level problems, where some coordination variables are kept fixed for global iteration. After all the sub optimizations are completed, higher-level optimization iteration with all the coordination and main variables is done. This is followed by further sub system optimizations with new coordination variables. This process is continued until convergence. In this paper we use a multi-level constrained optimization algorithm which avoids the repeated local sub system optimizations and which also removes the problem of non-linear sensitivity inherent in the single step approaches. Fall-zone constraints, structural load constraints and thermal constraints are considered. In this algorithm, there is only a single multi-level sequence of state and multiplier updates in a framework of an augmented Lagrangian. Han Tapia multiplier updates are used in view of their special role in diagonalised methods, being the only single update with quadratic convergence. For a single level, the diagonalised multiplier method (DMM) is described in Ref.5. The main advantage of the two-level analogue of the DMM approach is that it avoids the inner loop optimizations required in the other methods. The scheme also introduces a gradient change measure to reduce the computational time needed to calculate the gradients. It is demonstrated that the new multi-level scheme leads to a robust procedure to handle the sensitivity of the constraints, and the multiple objectives of different trajectory phases. Ref. 1. Fahroo, F and Ross, M., " A Spectral Patching Method for Direct Trajectory Optimization" The Journal of the Astronautical Sciences, Vol.48, 2000, pp.269-286 Ref. 2. Phililps, C.A. and Drake, J.C., "Trajectory Optimization for a Missile using a Multitier Approach" Journal of Spacecraft and Rockets, Vol.37, 2000, pp.663-669 Ref. 3. Gath, P.F., and Calise, A.J., " Optimization of Launch Vehicle Ascent Trajectories with Path Constraints and Coast Arcs", Journal of Guidance, Control, and Dynamics, Vol. 24, 2001, pp.296-304 Ref. 4. Betts, J.T., " Survey of Numerical Methods for Trajectory Optimization", Journal of Guidance, Control, and Dynamics, Vol.21, 1998, pp. 193-207 Ref. 5. Adimurthy, V., " Launch Vehicle Trajectory Optimization", Acta Astronautica, Vol.15, 1987, pp.845-850.

Adimurthy, V.; Tandon, S. R.; Jessy, Antony; Kumar, C. Ravi

241

Orbital optimized double-hybrid density functionals  

NASA Astrophysics Data System (ADS)

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.

Peverati, Roberto; Head-Gordon, Martin

2013-07-01

242

Optimizing and evaluating algorithms for replicated data concurrency control  

SciTech Connect

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.

Kumar, A.; Segev, A.

1989-02-01

243

A Hybrid Metaheuristic DE/CS Algorithm for UCAV Three-Dimension Path Planning  

PubMed Central

Three-dimension path planning for uninhabited combat air vehicle (UCAV) is a complicated high-dimension optimization problem, which primarily centralizes on optimizing the flight route considering the different kinds of constrains under complicated battle field environments. A new hybrid metaheuristic differential evolution (DE) and cuckoo search (CS) algorithm is proposed to solve the UCAV three-dimension path planning problem. DE is applied to optimize the process of selecting cuckoos of the improved CS model during the process of cuckoo updating in nest. The cuckoos can act as an agent in searching the optimal UCAV path. And then, the UCAV can find the safe path by connecting the chosen nodes of the coordinates while avoiding the threat areas and costing minimum fuel. This new approach can accelerate the global convergence speed while preserving the strong robustness of the basic CS. The realization procedure for this hybrid metaheuristic approach DE/CS is also presented. In order to make the optimized UCAV path more feasible, the B-Spline curve is adopted for smoothing the path. To prove the performance of this proposed hybrid metaheuristic method, it is compared with basic CS algorithm. The experiment shows that the proposed approach is more effective and feasible in UCAV three-dimension path planning than the basic CS model. PMID:23193383

Wang, Gaige; Guo, Lihong; Duan, Hong; Wang, Heqi; Liu, Luo; Shao, Mingzhen

2012-01-01

244

A hybrid metaheuristic DE/CS algorithm for UCAV three-dimension path planning.  

PubMed

Three-dimension path planning for uninhabited combat air vehicle (UCAV) is a complicated high-dimension optimization problem, which primarily centralizes on optimizing the flight route considering the different kinds of constrains under complicated battle field environments. A new hybrid metaheuristic differential evolution (DE) and cuckoo search (CS) algorithm is proposed to solve the UCAV three-dimension path planning problem. DE is applied to optimize the process of selecting cuckoos of the improved CS model during the process of cuckoo updating in nest. The cuckoos can act as an agent in searching the optimal UCAV path. And then, the UCAV can find the safe path by connecting the chosen nodes of the coordinates while avoiding the threat areas and costing minimum fuel. This new approach can accelerate the global convergence speed while preserving the strong robustness of the basic CS. The realization procedure for this hybrid metaheuristic approach DE/CS is also presented. In order to make the optimized UCAV path more feasible, the B-Spline curve is adopted for smoothing the path. To prove the performance of this proposed hybrid metaheuristic method, it is compared with basic CS algorithm. The experiment shows that the proposed approach is more effective and feasible in UCAV three-dimension path planning than the basic CS model. PMID:23193383

Wang, Gaige; Guo, Lihong; Duan, Hong; Wang, Heqi; Liu, Luo; Shao, Mingzhen

2012-01-01

245

Near-Optimal Control Strategies for Hybrid Cooling Plants  

Microsoft Academic Search

This paper develops and evaluates a set of operating strategies that when implemented together provide near-optimal performance for hybrid cooling plants in terms of operating costs. A hybrid chiller plant employs a combination of chillers that are “powered” by electricity and natural gas. Operating cost minimization for hybrid plants must account for effects of electrical and gas energy costs, electrical

James E. Braun

2007-01-01

246

Niching genetic algorithms for optimization in electromagnetics. I. Fundamentals  

Microsoft Academic Search

Niching methods extend genetic algorithms and permit the investigation of multiple optimal solutions in the search space. In this paper, we review and discuss various strategies of niching for optimization in electromagnetics. Traditional mathematical problems and an electromagnetic benchmark are solved using niching genetic algorithms to show their interest in real world optimization

B. Sareni; L. Krahenbuhl; A. Nicolas

1998-01-01

247

A Hybrid Quantum-Inspired Genetic Algorithm for Multiobjective Flow Shop Scheduling  

Microsoft Academic Search

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

Bin-Bin Li; Ling Wang

2007-01-01

248

An effective hybrid DEbased algorithm for flow shop scheduling with limited buffers  

Microsoft Academic Search

The permutation flow-shop scheduling problem (PFSSP) is a typical combinational optimization problem, which is of wide engineering background and has been proved to be strongly NP-hard. In this paper, an effective hybrid algorithm based on differential evolution (DE), namely HDE, is proposed for permutation flow-shop scheduling with limited buffers between consecutive machines to minimize the maximum completion time (i.e. makespan).

B. Qian; L. Wang; D. X. Huang; X. Wang

2009-01-01

249

Modelling, Simulation, Testing, and Optimization of Advanced Hybrid Vehicle Powertrains  

E-print Network

Modelling, Simulation, Testing, and Optimization of Advanced Hybrid Vehicle Powertrains By Jeffrey in the Department of Mechanical Engineering ©Jeffrey Wishart, 2008 University of Victoria All rights reserved of the author. #12;ii Modelling, Simulation, Testing and Optimization of Advanced Hybrid Vehicle Powertrains

Victoria, University of

250

A Hybrid Genetic Algorithm for Highly Constrained Timetabling Problems  

Microsoft Academic Search

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.

Edmund K. Burke; Dave Elliman; Rupert F. Weare

1995-01-01

251

A Hybrid Algorithm for the Examination Timetabling Problem  

Microsoft Academic Search

Examination timetabling is a well-studied combinatorial op- timization problem. We present a new hybrid algorithm for examination timetabling, consisting of three phases: a constraint programming phase to develop an initial solution, a simulated annealing phase to improve the quality of solution, and a hill climbing phase for further improvement. The examination timetabling problem at the University of Melbourne is introduced,

Liam T. G. Merlot; Natashia Boland; Barry D. Hughes; Peter J. Stuckey

2002-01-01

252

Solving the Course Timetabling Problem with a Hybrid Heuristic Algorithm  

Microsoft Academic Search

The problem of curriculum-based course timetabling is stud- ied in this work. In addition to formally defining the problem, we present a hybrid solution algorithm (Adaptive Tabu Search-ATS), which is aimed at minimizing violations of soft constraints. Within ATS, a new neighborhood and a mechanism for dynamically integrating Tabu Search with perturbation (from Iterated Local Search) are proposed to ensure

Lü Zhipeng; Jin-kao Hao

2008-01-01

253

Location Aware Reduced Diffusion Hybrid Routing Algorithm (LARDHR)  

Microsoft Academic Search

Mobile ad-hoc network (MANET) is a multihop wireless network formed by a collection of mobile nodes wherein the nodes move arbitrarily thus making the topology dynamic. MANETs have low bandwidth wireless links hence the routing protocols with less consumption of bandwidth are of great importance. This paper presents a new location aware hybrid routing algorithm with reduced diffusion of routing

Pariza Kamboj; A. K. Sharma

2009-01-01

254

A new hybrid algorithm for fire vision recognition  

Microsoft Academic Search

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 hybrid algorithm that depends on

Magy Kandil; May Salama

2009-01-01

255

A hybrid genetic algorithm for manufacturing cell formation1  

E-print Network

manufacturing is the formation of product families and machine cells. This paper presents a new approach product and machine cells simultaneously, so additional methods must be employed to complete the designA hybrid genetic algorithm for manufacturing cell formation1 José Fernando Gonçalves Faculdade de

Fisher, Kathleen

256

An Overview of Evolutionary Algorithms for Parameter Optimization  

E-print Network

), and Genetic Algorithms (GAs). The comparison is performed with respect to certain characteristic components, unifying basic algorithm, and the fundamental theoretical results on the algorithms are presented. FinallyAn Overview of Evolutionary Algorithms for Parameter Optimization Thomas BË?ack \\Lambda Hans

Hoffmann, Frank

257

A cross-layer optimization algorithm for wireless sensor network  

NASA Astrophysics Data System (ADS)

Energy is critical for typical wireless sensor networks (WSN) and how to energy consumption and maximize network lifetime are big challenges for Wireless sensor networks; cross layer algorithm is main method to solve this problem. In this paper, firstly, we analyze current layer-based optimal methods in wireless sensor network and summarize the physical, link and routing optimization techniques. Secondly we compare some strategies in cross-layer optimization algorithms. According to the analysis and summary of the current lifetime algorithms in wireless sensor network A cross layer optimization algorithm is proposed,. Then this optimization algorithm proposed in the paper is adopted to improve the traditional Leach routing protocol. Simulation results show that this algorithm is an excellent cross layer algorithm for reducing energy consumption.

Wang, Yan; Liu, Le Qing

2010-07-01

258

A Survey on Evolutionary Algorithm Based Hybrid Intelligence in Bioinformatics  

PubMed Central

With the rapid advance in genomics, proteomics, metabolomics, and other types of omics technologies during the past decades, a tremendous amount of data related to molecular biology has been produced. It is becoming a big challenge for the bioinformatists to analyze and interpret these data with conventional intelligent techniques, for example, support vector machines. Recently, the hybrid intelligent methods, which integrate several standard intelligent approaches, are becoming more and more popular due to their robustness and efficiency. Specifically, the hybrid intelligent approaches based on evolutionary algorithms (EAs) are widely used in various fields due to the efficiency and robustness of EAs. In this review, we give an introduction about the applications of hybrid intelligent methods, in particular those based on evolutionary algorithm, in bioinformatics. In particular, we focus on their applications to three common problems that arise in bioinformatics, that is, feature selection, parameter estimation, and reconstruction of biological networks. PMID:24729969

Li, Shan; Zhao, Xing-Ming

2014-01-01

259

A survey on evolutionary algorithm based hybrid intelligence in bioinformatics.  

PubMed

With the rapid advance in genomics, proteomics, metabolomics, and other types of omics technologies during the past decades, a tremendous amount of data related to molecular biology has been produced. It is becoming a big challenge for the bioinformatists to analyze and interpret these data with conventional intelligent techniques, for example, support vector machines. Recently, the hybrid intelligent methods, which integrate several standard intelligent approaches, are becoming more and more popular due to their robustness and efficiency. Specifically, the hybrid intelligent approaches based on evolutionary algorithms (EAs) are widely used in various fields due to the efficiency and robustness of EAs. In this review, we give an introduction about the applications of hybrid intelligent methods, in particular those based on evolutionary algorithm, in bioinformatics. In particular, we focus on their applications to three common problems that arise in bioinformatics, that is, feature selection, parameter estimation, and reconstruction of biological networks. PMID:24729969

Li, Shan; Kang, Liying; Zhao, Xing-Ming

2014-01-01

260

Effectiveness evaluation of heuristic algorithms applied in hybrid models for water distribution networks design  

NASA Astrophysics Data System (ADS)

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.

Cisty, Milan; Bajtek, Zbynek

2010-05-01

261

Optimizing System Performance Through Dynamic Disk Scheduling Algorithm Selection  

E-print Network

Optimizing System Performance Through Dynamic Disk Scheduling Algorithm Selection DANIEL L performance. New approaches and algorithms for disk scheduling have been developed in recent years scheduling of disk requests. Unfortunately, there has yet to be developed a single universal disk

Katchabaw, Michael James

262

Adaptive Hybrid Optimal Quantum Control for Imprecisely Characterized Systems  

NASA Astrophysics Data System (ADS)

Optimal quantum control theory carries a huge promise for quantum technology. Its experimental application, however, is often hindered by imprecise knowledge of the input variables, the quantum system's parameters. We show how to overcome this by adaptive hybrid optimal control, using a protocol named Ad-HOC. This protocol combines open- and closed-loop optimal control by first performing a gradient search towards a near-optimal control pulse and then an experimental fidelity estimation with a gradient-free method. For typical settings in solid-state quantum information processing, adaptive hybrid optimal control enhances gate fidelities by an order of magnitude, making optimal control theory applicable and useful.

Egger, D. J.; Wilhelm, F. K.

2014-06-01

263

An optimal adiabatic quantum query algorithm  

E-print Network

Quantum query complexity is known to be characterized by the so-called quantum adversary bound. While this result has been proved in the standard discrete-time model of quantum computation, it also holds for continuous-time (or Hamiltonian-based) quantum computation, due to a known equivalence between these two query complexity models. In this work, we revisit this result by providing a direct proof in the continuous-time model. One originality of our proof is that it draws new connections between the adversary bound, a modern theoretical computer science technique, and early theorems of quantum mechanics. Indeed, the proof of the lower bound is based on Ehrenfest's theorem, while the upper bound relies on the Adiabatic theorem, as we construct an optimal adiabatic quantum query algorithm.

Mathieu Brandeho; Jérémie Roland

2014-09-11

264

PDE Nozzle Optimization Using a Genetic Algorithm  

NASA Technical Reports Server (NTRS)

Genetic algorithms, which simulate evolution in natural systems, have been used to find solutions to optimization problems that seem intractable to standard approaches. In this study, the feasibility of using a GA to find an optimum, fixed profile nozzle for a pulse detonation engine (PDE) is demonstrated. The objective was to maximize impulse during the detonation wave passage and blow-down phases of operation. Impulse of each profile variant was obtained by using the CFD code Mozart/2.0 to simulate the transient flow. After 7 generations, the method has identified a nozzle profile that certainly is a candidate for optimum solution. The constraints on the generality of this possible solution remain to be clarified.

Billings, Dana; Turner, James E. (Technical Monitor)

2000-01-01

265

Optimizing vehicle-to-grid charging strategies using genetic algorithms under the consideration of battery aging  

Microsoft Academic Search

Lithium-ion battery aging tests show that battery lifetime can be strongly influenced by the operating conditions, particularly by the state of charge and the cycle depth. Therefore a genetic optimization algorithm is applied to optimize the charging behavior of a plug-in hybrid electric vehicle (PHEV) connected to the grid with respect to maximizing energy trading profits in a vehicle-to-grid (V2G)

Benedikt Lunz; Hannes Walz; Dirk Uwe Sauer

2011-01-01

266

Eagle Strategy Using Lévy Walk and Firefly Algorithms for Stochastic Optimization  

Microsoft Academic Search

Most global optimization problems are nonlinear and thus difficult to solve,\\u000aand they become even more challenging when uncertainties are present in\\u000aobjective functions and constraints. This paper provides a new two-stage hybrid\\u000asearch method, called Eagle Strategy, for stochastic optimization. This\\u000astrategy intends to combine the random search using L\\\\'evy walk with the\\u000afirefly algorithm in an iterative manner.

Xin-She Yang; Suash Deb

2010-01-01

267

Hybrid Training Method for MLP: Optimization of Architecture and Training.  

PubMed

The performance of an artificial neural network (ANN) depends upon the selection of proper connection weights, network architecture, and cost function during network training. This paper presents a hybrid approach (GaTSa) to optimize the performance of the ANN in terms of architecture and weights. GaTSa is an extension of a previous method (TSa) proposed by the authors. GaTSa is based on the integration of the heuristic simulated annealing (SA), tabu search (TS), genetic algorithms (GA), and backpropagation, whereas TSa does not use GA. The main advantages of GaTSa are the following: a constructive process to add new nodes in the architecture based on GA, the ability to escape from local minima with uphill moves (SA feature), and faster convergence by the evaluation of a set of solutions (TS feature). The performance of GaTSa is investigated through an empirical evaluation of 11 public-domain data sets using different cost functions in the simultaneous optimization of the multilayer perceptron ANN architecture and weights. Experiments demonstrated that GaTSa can also be used for relevant feature selection. GaTSa presented statistically relevant results in comparison with other global and local optimization techniques. PMID:21317085

Zanchettin, Cleber; Ludermir, Teresa B; Almeida, Leandro Maciel

2011-02-10

268

Optimal multiple-objective resource allocation using hybrid particle swarm optimization and adaptive resource bounds technique  

NASA Astrophysics Data System (ADS)

The multiple-objective resource allocation problem (MORAP) seeks for an allocation of resource to a number of activities such that a set of objectives are optimized simultaneously and the resource constraints are satisfied. MORAP has many applications, such as resource distribution, project budgeting, software testing, health care resource allocation, etc. This paper addresses the nonlinear MORAP with integer decision variable constraint. To guarantee that all the resource constraints are satisfied, we devise an adaptive-resource-bound technique to construct feasible solutions. The proposed method employs the particle swarm optimization (PSO) paradigm and presents a hybrid execution plan which embeds a hill-climbing heuristic into the PSO for expediting the convergence. To cope with the optimization problem with multiple objectives, we evaluate the candidate solutions based on dominance relationship and a score function. Experimental results manifest that the hybrid PSO derives solution sets which are very close to the exact Pareto sets. The proposed method also outperforms several representatives of the state-of-the-art algorithms on a simulation data set of the MORAP.

Yin, Peng-Yeng; Wang, Jing-Yu

2008-06-01

269

A Hybrid Approach for Process Mining: Using From-to Chart Arranged by Genetic Algorithms  

NASA Astrophysics Data System (ADS)

In the scope of this study, a hybrid data analysis methodology to business process modeling is proposed in such a way that; From-to Chart, which is basically used as the front-end to figure out the observed patterns among the activities at realistic event logs, is rearranged by Genetic Algorithms to convert these derived raw relations into activity sequence. According to experimental results, acceptably good (sub-optimal or optimal) solutions are obtained for relatively complex business processes at a reasonable processing time period.

Esgin, Eren; Senkul, Pinar; Cimenbicer, Cem

270

Greedy algorithms Algorithms for solving (optimization) problems typically go through a  

E-print Network

Greedy algorithms Algorithms for solving (optimization) problems typically go through a sequence of steps, with a set of choices at each step. A greedy algorithm always makes the choice that looks best at the moment, without regard for future consequence "take what you can get now" strategy Greedy algorithms do

Bai, Zhaojun

271

The Ordered Clustered Travelling Salesman Problem: A Hybrid Genetic Algorithm  

PubMed Central

The ordered clustered travelling salesman problem is a variation of the usual travelling salesman problem in which a set of vertices (except the starting vertex) of the network is divided into some prespecified clusters. The objective is to find the least cost Hamiltonian tour in which vertices of any cluster are visited contiguously and the clusters are visited in the prespecified order. The problem is NP-hard, and it arises in practical transportation and sequencing problems. This paper develops a hybrid genetic algorithm using sequential constructive crossover, 2-opt search, and a local search for obtaining heuristic solution to the problem. The efficiency of the algorithm has been examined against two existing algorithms for some asymmetric and symmetric TSPLIB instances of various sizes. The computational results show that the proposed algorithm is very effective in terms of solution quality and computational time. Finally, we present solution to some more symmetric TSPLIB instances. PMID:24701148

Ahmed, Zakir Hussain

2014-01-01

272

Automated design of multiphase space missions using hybrid optimal control  

NASA Astrophysics Data System (ADS)

A modern space mission is assembled from multiple phases or events such as impulsive maneuvers, coast arcs, thrust arcs and planetary flybys. Traditionally, a mission planner would resort to intuition and experience to develop a sequence of events for the multiphase mission and to find the space trajectory that minimizes propellant use by solving the associated continuous optimal control problem. This strategy, however, will most likely yield a sub-optimal solution, as the problem is sophisticated for several reasons. For example, the number of events in the optimal mission structure is not known a priori and the system equations of motion change depending on what event is current. In this work a framework for the automated design of multiphase space missions is presented using hybrid optimal control (HOC). The method developed uses two nested loops: an outer-loop that handles the discrete dynamics and finds the optimal mission structure in terms of the categorical variables, and an inner-loop that performs the optimization of the corresponding continuous-time dynamical system and obtains the required control history. Genetic algorithms (GA) and direct transcription with nonlinear programming (NLP) are introduced as methods of solution for the outer-loop and inner-loop problems, respectively. Automation of the inner-loop, continuous optimal control problem solver, required two new technologies. The first is a method for the automated construction of the NLP problems resulting from the use of a direct solver for systems with different structures, including different numbers of categorical events. The method assembles modules, consisting of parameters and constraints appropriate to each event, sequentially according to the given mission structure. The other new technology is for a robust initial guess generator required by the inner-loop NLP problem solver. Two new methods were developed for cases including low-thrust trajectories. The first method, based on GA, approximates optimal control histories by incorporating boundary conditions explicitly using a conditional penalty function. The second method, feasible region analysis, is based on GA and NLP; the GA approximates the optimal boundary points of low-thrust arcs while NLP finds the required control histories. The solution of two representative multiphase space mission design problems shows the effectiveness of the methods developed.

Chilan, Christian Miguel

273

Inverse Parametric Optimization with an Application to Hybrid ...  

E-print Network

... has received funding from the Swiss Secretariat for Education and Research under ... state feedback control law to be optimal for a linear plant in [8]. ... by finding a generating problem (1) that has the state transition dynamics as its optimal solution. The resulting inverse optimization model of the hybrid system is a special.

2014-06-13

274

Optimization of computer vision algorithms for real time platforms  

Microsoft Academic Search

Real time computer vision applications like video streaming on cell phones, remote surveillance and virtual reality have stringent performance requirements but can be severely restrained by limited resources. The use of optimized algorithms is vital to meet real-time requirements especially on popular mobile platforms. This paper presents work on performance optimization of common computer vision algorithms such as correlation on

Pramod Poudel; Mukul Shirvaikar

2010-01-01

275

An Improved Genetic Algorithm for Reactive Power Optimization  

Microsoft Academic Search

In this paper, an Improve Genetic Algorithm (IGA) is applied to solve reactive power optimization (RPO) problem. The PRO problem is a highly nonlinear complex optimization problem and can be solved by enumeration method if without the advantages of evolutionary algorithms. The IGA modifies chromosomes based on the fundamentals of virtual population method. Stochastic crossover schemes are also employed in

Guang Ya Yang; Zhao Yang Dong

276

A Recursive Random Search Algorithm for Network Parameter Optimization  

E-print Network

considered a black art and is normally performed based on network administrators' experience, trial and error Simulator Network Network Model Optimization Black-box Algorithm Experiment Parameters Performance MetricA Recursive Random Search Algorithm for Network Parameter Optimization Tao Ye 1 Shivkumar

Kalyanaraman, Shivkumar

277

Hybrid regularization image restoration algorithm based on total variation  

NASA Astrophysics Data System (ADS)

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.

Zhang, Hongmin; Wang, Yan

2013-09-01

278

Study on Improvement of Memory Cell Control in Hybridization of Immune Algorithm and Gradient Search for Multiple Solution Search  

NASA Astrophysics Data System (ADS)

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 hybrid algorithm 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 hybrid algorithm 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 hybrid algorithm involving the proposed improvements can find optimal and semi-optimal solutions with high accuracy and efficiency even in high-dimensional multimodal functions involving epistasis.

Hirotani, Yusuke; Ono, Satoshi; Nakayama, Shigeru

279

Particle swarm optimization-based algorithm for lightning location estimation  

Microsoft Academic Search

Lightning early warning requires lightning location systems to process sensors' measurements in near real time. A new algorithm based on particle swarm optimization (PSO) is developed to provide reliable and immediate solutions of lightning location and occurrence time. Comparing with iterative-type algorithms, the PSO-based algorithm does not require initial value and is easy to program. Different parameter choice schemes for

Zhixiang Hu; Yinping Wen; Wenguang Zhao; Hongping Zhu

2010-01-01

280

Adaptive branch and bound algorithm for selecting optimal features  

Microsoft Academic Search

We propose a new adaptive branch and bound algorithm for selecting the optimal subset of features in pattern recognition applications. The algorithm improves the search speed by avoiding unnecessary criterion function calculations at nodes in the solution tree. Our algorithm includes the following new properties: (i) ordering the tree nodes by the significance of features during construction of the tree,

Songyot Nakariyakul; David P. Casasent

2007-01-01

281

HEURISTIC OPTIMIZATION AND ALGORITHM TUNING APPLIED TO SORPTIVE BARRIER DESIGN  

EPA Science Inventory

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 ...

282

An optimal online algorithm for metrical task systems  

Microsoft Academic Search

In practice, almost all dynamic systems require decisions to be made online, without full knowledge of their future impact on the system. We introduce a general model for the processing of sequences of tasks and develop a general online decision algorithm. We show that, for an important class of special cases, this algorithm is optimal among all online algorithms.Specifically, a

Allan Borodin; Nathan Linial; Michael E. Saks

1987-01-01

283

An Effective Hybrid Cuckoo Search Algorithm with Improved Shuffled Frog Leaping Algorithm for 0-1 Knapsack Problems  

PubMed Central

An effective hybrid cuckoo search algorithm (CS) with improved shuffled frog-leaping algorithm (ISFLA) is put forward for solving 0-1 knapsack problem. First of all, with the framework of SFLA, an improved frog-leap operator is designed with the effect of the global optimal information on the frog leaping and information exchange between frog individuals combined with genetic mutation with a small probability. Subsequently, in order to improve the convergence speed and enhance the exploitation ability, a novel CS model is proposed with considering the specific advantages of Lévy flights and frog-leap operator. Furthermore, the greedy transform method is used to repair the infeasible solution and optimize the feasible solution. Finally, numerical simulations are carried out on six different types of 0-1 knapsack instances, and the comparative results have shown the effectiveness of the proposed algorithm and its ability to achieve good quality solutions, which outperforms the binary cuckoo search, the binary differential evolution, and the genetic algorithm.

Wang, Gai-Ge; Feng, Qingjiang; Zhao, Xiang-Jun

2014-01-01

284

Genetic Algorithms Applied to Multi-Objective Aerodynamic Shape Optimization  

NASA Technical Reports Server (NTRS)

A genetic algorithm approach suitable for solving multi-objective optimization problems is described and evaluated using a series of aerodynamic shape optimization problems. Several new features including two variations of a binning selection algorithm and a gene-space transformation procedure are included. The genetic algorithm is suitable for finding pareto optimal solutions in search spaces that are defined by any number of genes and that contain any number of local extrema. A new masking array capability is included allowing any gene or gene subset to be eliminated as decision variables from the design space. This allows determination of the effect of a single gene or gene subset on the pareto optimal solution. Results indicate that the genetic algorithm optimization approach is flexible in application and reliable. The binning selection algorithms generally provide pareto front quality enhancements and moderate convergence efficiency improvements for most of the problems solved.

Holst, Terry L.

2004-01-01

285

A Hybrid Particle Swarm Algorithm for Job Shop Scheduling Problems and its Convergence Analysis  

Microsoft Academic Search

A hybrid particle swarm algorithm with global asymptotic convergence was proposed, which was used to make up for the deficiencies of resolving job shop scheduling problem. In the hybrid particle swarm algorithm, the particle swarm with optimum keeping strategy was applied to search in the global solution space, and the taboo search algorithm was utilized as the local algorithm, which

Xiaoyu Song; Lihua Sun; Chunguang Chang

2009-01-01

286

Parallel Hybrid Vehicle Optimal Storage System  

NASA Technical Reports Server (NTRS)

A paper reports the results of a Hybrid Diesel Vehicle Project focused on a parallel hybrid configuration suitable for diesel-powered, medium-sized, commercial vehicles commonly used for parcel delivery and shuttle buses, as the missions of these types of vehicles require frequent stops. During these stops, electric hybridization can effectively recover the vehicle's kinetic energy during the deceleration, store it onboard, and then use that energy to assist in the subsequent acceleration.

Bloomfield, Aaron P.

2009-01-01

287

A hybrid algorithm for solving the EEG inverse problem from spatio-temporal EEG data.  

PubMed

Epilepsy is a neurological disorder caused by intense electrical activity in the brain. The electrical activity, which can be modelled through the superposition of several electrical dipoles, can be determined in a non-invasive way by analysing the electro-encephalogram. This source localization requires the solution of an inverse problem. Locally convergent optimization algorithms may be trapped in local solutions and when using global optimization techniques, the computational effort can become expensive. Fast recovery of the electrical sources becomes difficult that way. Therefore, there is a need to solve the inverse problem in an accurate and fast way. This paper performs the localization of multiple dipoles using a global-local hybrid algorithm. Global convergence is guaranteed by using space mapping techniques and independent component analysis in a computationally efficient way. The accuracy is locally obtained by using the Recursively Applied and Projected-MUltiple Signal Classification (RAP-MUSIC) algorithm. When using this hybrid algorithm, a four times faster solution is obtained. PMID:18427852

Crevecoeur, Guillaume; Hallez, Hans; Van Hese, Peter; D'Asseler, Yves; Dupré, Luc; Van de Walle, Rik

2008-08-01

288

Structural Query Optimization in Native XML Databases: A Hybrid Approach  

NASA Astrophysics Data System (ADS)

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.

Haw, Su-Cheng; Lee, Chien-Sing

289

A hybrid multi-objective evolutionary algorithm using an inverse neural network for aircraft control system design  

Microsoft Academic Search

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

2005-01-01

290

Aerodynamic Shape Design of Nozzles Using a Hybrid Optimization Method  

E-print Network

A hybrid design optimization method combining the stochastic method based on simultaneous perturbation stochastic approximation (SPSA) and the deterministic method of Broydon-Fletcher-Goldfarb-Shanno (BFGS) is developed ...

Xing, X.Q.

291

An improved marriage in honey bees optimization algorithm for single objective unconstrained optimization.  

PubMed

Marriage in honey bees optimization (MBO) is a metaheuristic optimization algorithm developed by inspiration of the mating and fertilization process of honey bees and is a kind of swarm intelligence optimizations. In this study we propose improved marriage in honey bees optimization (IMBO) by adding Levy flight algorithm for queen mating flight and neighboring for worker drone improving. The IMBO algorithm's performance and its success are tested on the well-known six unconstrained test functions and compared with other metaheuristic optimization algorithms. PMID:23935416

Celik, Yuksel; Ulker, Erkan

2013-01-01

292

An Improved Marriage in Honey Bees Optimization Algorithm for Single Objective Unconstrained Optimization  

PubMed Central

Marriage in honey bees optimization (MBO) is a metaheuristic optimization algorithm developed by inspiration of the mating and fertilization process of honey bees and is a kind of swarm intelligence optimizations. In this study we propose improved marriage in honey bees optimization (IMBO) by adding Levy flight algorithm for queen mating flight and neighboring for worker drone improving. The IMBO algorithm's performance and its success are tested on the well-known six unconstrained test functions and compared with other metaheuristic optimization algorithms. PMID:23935416

Celik, Yuksel; Ulker, Erkan

2013-01-01

293

Genetic-Algorithm Tool For Search And Optimization  

NASA Technical Reports Server (NTRS)

SPLICER computer program used to solve search and optimization problems. Genetic algorithms adaptive search procedures (i.e., problem-solving methods) based loosely on processes of natural selection and Darwinian "survival of fittest." Algorithms apply genetically inspired operators to populations of potential solutions in iterative fashion, creating new populations while searching for optimal or nearly optimal solution to problem at hand. Written in Think C.

Wang, Lui; Bayer, Steven

1995-01-01

294

Global Tree Optimization: A Non-greedy Decision Tree Algorithm  

Microsoft Academic Search

A non-greedy approach for constructing globally optimalmultivariate decision trees with fixed structure is proposed.Previous greedy tree construction algorithms arelocally optimal in that they optimize some splitting criterionat each decision node, typically one node at a time.In contrast, global tree optimization explicitly considersall decisions in the tree concurrently. An iterative linearprogramming algorithm is used to minimize the classificationerror of the entire

Kristin P. Bennett

1994-01-01

295

A Hybrid Genetic Algorithm to Solve Zero-One Knapsack Problem  

Microsoft Academic Search

\\u000a In order to overcome the disadvantages of the traditional genetic algorithm and improve the speed and precision of the algorithm,\\u000a the author improved the selection strategy, integrated the greedy algorithm with the genetic algorithm and formed the hybrid\\u000a genetic algorithm. The paper discussed the basic idea and method to solve the zero-one knapsack problem using this hybrid\\u000a genetic algorithm. The

Qing Chen; Yuxiang Shao

296

Truss optimization on shape and sizing with frequency constraints based on orthogonal multi-gravitational search algorithm  

NASA Astrophysics Data System (ADS)

Structural optimization on shape and sizing with frequency constraints is well-known as a highly nonlinear dynamic optimization problem with several local optimum solutions. Hence, efficient optimization algorithms should be utilized to solve this problem. In this study, orthogonal multi-gravitational search algorithm (OMGSA) as a meta-heuristic algorithm is introduced to solve truss optimization on shape and sizing with frequency constraints. The OMGSA is a hybrid approach based on a combination of multi-gravitational search algorithm (multi-GSA) and an orthogonal crossover (OC). In multi-GSA, the population is split into several sub-populations. Then, each sub-population is independently evaluated by an improved gravitational search algorithm (IGSA). Furthermore, the OC is used in the proposed OMGSA in order to find and exploit the global solution in the search space. The capability of OMGSA is demonstrated through six benchmark examples. Numerical results show that the proposed OMGSA outperform the other optimization techniques.

Khatibinia, Mohsen; Sadegh Naseralavi, Seyed

2014-12-01

297

A Genetic Algorithm for Multiobjective Design Optimization in Aerodynamics and  

E-print Network

A Genetic Algorithm for Multiobjective Design Optimization in Aerodynamics and Electromagnetics R. The objective functions in the optimization problem measure the aerodynamic feasibil­ ity based on the drag been optimized with respect to only one discipline such as aerodynamics or electromagnetics. Although

Coello, Carlos A. Coello

298

Genetic Algorithms Applied to Multi-Objective Aerodynamic Shape Optimization  

NASA Technical Reports Server (NTRS)

A genetic algorithm approach suitable for solving multi-objective problems is described and evaluated using a series of aerodynamic shape optimization problems. Several new features including two variations of a binning selection algorithm and a gene-space transformation procedure are included. The genetic algorithm is suitable for finding Pareto optimal solutions in search spaces that are defined by any number of genes and that contain any number of local extrema. A new masking array capability is included allowing any gene or gene subset to be eliminated as decision variables from the design space. This allows determination of the effect of a single gene or gene subset on the Pareto optimal solution. Results indicate that the genetic algorithm optimization approach is flexible in application and reliable. The binning selection algorithms generally provide Pareto front quality enhancements and moderate convergence efficiency improvements for most of the problems solved.

Holst, Terry L.

2005-01-01

299

A comparison of global search algorithms for continuous black box optimization.  

PubMed

Four methods for global numerical black box optimization with origins in the mathematical programming community are described and experimentally compared with the state of the art evolutionary method, BIPOP-CMA-ES. The methods chosen for the comparison exhibit various features that are potentially interesting for the evolutionary computation community: systematic sampling of the search space (DIRECT, MCS) possibly combined with a local search method (MCS), or a multi-start approach (NEWUOA, GLOBAL) possibly equipped with a careful selection of points to run a local optimizer from (GLOBAL). The recently proposed "comparing continuous optimizers" (COCO) methodology was adopted as the basis for the comparison. Based on the results, we draw suggestions about which algorithm should be used depending on the available budget of function evaluations, and we propose several possibilities for hybridizing evolutionary algorithms (EAs) with features of the other compared algorithms. PMID:22708992

Pošík, Petr; Huyer, Waltraud; Pál, László

2012-01-01

300

Weight minimization of structures for fixed flutter speed via an optimality criterion. [algorithm for lifting surfaces  

NASA Technical Reports Server (NTRS)

A rigorous optimality criterion is derived and a hybrid weight-reduction algorithm developed for the weight minimization of lifting surfaces with a constraint on flutter speed. The weight-reduction algorithm incorporates a simple recursion formula derived from the optimality criterion. Monotonic weight reduction is accomplished by dynamically adjusting a parameter in the recursion formula so as to achieve a predetermined weight decrease. The algorithm thus combines the simplicity of optimality-criterion methods with the convergence characteristics of mathematical-programming methods. The imposition of the flutter constraint is simplified by forcing to zero the imaginary part of the flutter eigenvalue, with the airspeed fixed. Four examples are discussed. The results suggest that significant improvements in efficiency are possible, in comparison with techniques based purely on mathematical programming.

Segenreich, S. A.; Mcintosh, S. C., Jr.

1975-01-01

301

Eagle Strategy Using L\\'evy Walk and Firefly Algorithms For Stochastic Optimization  

E-print Network

Most global optimization problems are nonlinear and thus difficult to solve, and they become even more challenging when uncertainties are present in objective functions and constraints. This paper provides a new two-stage hybrid search method, called Eagle Strategy, for stochastic optimization. This strategy intends to combine the random search using L\\'evy walk with the firefly algorithm in an iterative manner. Numerical studies and results suggest that the proposed Eagle Strategy is very efficient for stochastic optimization. Finally practical implications and potential topics for further research will be discussed.

Yang, Xin-She

2010-01-01

302

An improved hybrid global optimization method for protein tertiary structure prediction  

PubMed Central

First principles approaches to the protein structure prediction problem must search through an enormous conformational space to identify low-energy, near-native structures. In this paper, we describe the formulation of the tertiary structure prediction problem as a nonlinear constrained minimization problem, where the goal is to minimize the energy of a protein conformation subject to constraints on torsion angles and interatomic distances. The core of the proposed algorithm is a hybrid global optimization method that combines the benefits of the ?BB deterministic global optimization approach with conformational space annealing. These global optimization techniques employ a local minimization strategy that combines torsion angle dynamics and rotamer optimization to identify and improve the selection of initial conformations and then applies a sequential quadratic programming approach to further minimize the energy of the protein conformations subject to constraints. The proposed algorithm demonstrates the ability to identify both lower energy protein structures, as well as larger ensembles of low-energy conformations. PMID:20357906

McAllister, Scott R.

2009-01-01

303

An automated algorithm for stability analysis of hybrid dynamical systems  

NASA Astrophysics Data System (ADS)

There are many hybrid dynamical systems encountered in nature and in engineering, that have a large number of subsystems and a large number of switching conditions for transitions between subsystems. Bifurcation analysis of such systems poses a problem, because the detection of periodic orbits and the computation of their Floquet multipliers become difficult in such systems. In this paper we propose an algorithm to solve this problem. It is based on the computation of the fundamental solution matrix over a complete period-where the orbit may contain transitions through a large number of subsystems. The fundamental solution matrix is composed of the exponential matrices for evolution through the subsystems (considered linear time invariant in this paper) and the saltation matrices for the transitions through switching conditions. This matrix is then used to compose a Newton-Raphson search algorithm to converge on the periodic orbit. The algorithm-which has no restriction of the complexity of the system-locates the periodic orbit (stable or unstable), and at the same time computes its Floquet multipliers. The program is written in a sufficiently general way, so that it can be applied to any hybrid dynamical system.

Mandal, K.; Chakraborty, C.; Abusorrah, A.; Al-Hindawi, M. M.; Al-Turki, Y.; Banerjee, S.

2013-07-01

304

Exploring chemical space with discrete, gradient, and hybrid optimization methods  

Microsoft Academic Search

Discrete, gradient, and hybrid optimization 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

D. Balamurugan; Weitao Yang; David N. Beratan

2008-01-01

305

Genetic algorithms for multicriteria shape optimization of induction furnace  

NASA Astrophysics Data System (ADS)

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 optimization algorithms.

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

2012-09-01

306

Optimized algorithm for synthetic aperture imaging  

Microsoft Academic Search

We present a novel synthetic aperture imaging algorithm based on concepts used in synthetic aperture radar (SAR) and sonar (SAS). The algorithm, based on a convolution model of the imaging system developed in the frequency domain, accounts for the beam-pattern of the finite sized transducer used in the synthetic aperture. A 2D Fourier transform is used for the calculation of

T. Stepinski; F. Lingvall

2004-01-01

307

Cellular Probabilistic Evolutionary Algorithms for Real-Coded Function Optimization  

NASA Astrophysics Data System (ADS)

We propose a novel Cellular Probabilistic Evolutionary Algorithm (CPEA) based on a probabilistic representation of solutions for real coded problems. In place of binary integers, the basic unit of information here is a probability density function. This probabilistic coding allows superposition of states for a more efficient algorithm. Furthermore, the cellular structure of the proposed algorithm aims to provide an appropriate tradeoff between exploitation and exploration. Experimental results show that the performance of CPEA in several numerical benchmark problems is improved when compared with other evolutionary algorithms like Particle Swarm Optimization (PSO) and Genetic Algorithms (GA).

Akbarzadeh T., M. R.; Tayarani N., M.

308

Parallel projected variable metric algorithms for unconstrained optimization  

NASA Technical Reports Server (NTRS)

The parallel variable metric optimization algorithms of Straeter (1973) and van Laarhoven (1985) are reviewed, and the possible drawbacks of the algorithms are noted. By including Davidon (1975) projections in the variable metric updating, researchers can generalize Straeter's algorithm to a family of parallel projected variable metric algorithms which do not suffer the above drawbacks and which retain quadratic termination. Finally researchers consider the numerical performance of one member of the family on several standard example problems and illustrate how the choice of the displacement vectors affects the performance of the algorithm.

Freeman, T. L.

1989-01-01

309

Constrained Optimal Hybrid Control of a Flow Shop System  

Microsoft Academic Search

We consider an optimal control problem for the hybrid model of a deterministic flow shop system, in which the jobs are processed in the order they arrive at the system. The problem is decomposed into a higher-level discrete-event system control problem of determining the optimal service times, and a set of lower-level classical control problems of determining the optimal control

Kagan Gokbayrak; Omer Selvi

2007-01-01

310

Warpage Optimization in Injection Molding Using MPSO?HNN and MPSO Algorithm  

Microsoft Academic Search

In this paper, a Hybrid Neural Network trained by Modified Particle Swarm Optimization (MPSO-HNN) is first proposed for predicting warpage. Then the minimum warpage problem is solved by MPSO-HNN coupled with MPSO algorithm based on process parameters in injection molding. A model based on Taguchi experiment is designed to find process parameters' optimum levels by Signal-to-Noise (SN) ratio. All results

Xuejuan Li; Jie Ouyang; Binxin Yang; Tao Jiang

2010-01-01

311

Adaptive hybrid optimal quantum control for imprecisely characterized systems  

E-print Network

Optimal quantum control theory carries a huge promise for quantum technology. Its experimental application, however, is often hindered by imprecise knowledge of the its input variables, the quantum system's parameters. We show how to overcome this by Adaptive Hybrid Optimal Control (Ad-HOC). This protocol combines open- and closed-loop optimal by first performing a gradient search towards a near-optimal control pulse and then an experimental fidelity measure with a gradient-free method. For typical settings in solid-state quantum information processing, Ad-Hoc enhances gate fidelities by an order of magnitude hence making optimal control theory applicable and useful.

D. J. Egger; F. K. Wilhelm

2014-02-28

312

PCB drill path optimization by combinatorial cuckoo search algorithm.  

PubMed

Optimization of drill path can lead to significant reduction in machining time which directly improves productivity of manufacturing systems. In a batch production of a large number of items to be drilled such as printed circuit boards (PCB), the travel time of the drilling device is a significant portion of the overall manufacturing process. To increase PCB manufacturing productivity and to reduce production costs, a good option is to minimize the drill path route using an optimization algorithm. This paper reports a combinatorial cuckoo search algorithm for solving drill path optimization problem. The performance of the proposed algorithm is tested and verified with three case studies from the literature. The computational experience conducted in this research indicates that the proposed algorithm is capable of efficiently finding the optimal path for PCB holes drilling process. PMID:24707198

Lim, Wei Chen Esmonde; Kanagaraj, G; Ponnambalam, S G

2014-01-01

313

A Faster Algorithm for Quasi-convex Integer Polynomial Optimization  

E-print Network

Jun 23, 2010 ... is the binary encoding length of a bound on that region with r ? ldO(n), ... Kannan improved Lenstra's algorithm for linear integer optimization by ...... Symposium on Symbolic and Algebraic Computation, pages 259–266. ACM.

2010-06-23

314

Provably Good Approximation Algorithms for Optimal Kinodynamic Planning: Robots with  

E-print Network

Provably Good Approximation Algorithms for Optimal Kinodynamic Planning: Robots with Decoupled-7501 Patrick Xavier Sandia National Laboratories, Albuquerque NM 87185-0951 Keywords: robot motion planning, kinodynamics, polyhedral obstacles Abstract: We consider the following problem: given a robot system, nd

Richardson, David

315

Dutch Named Entity Recognition: Optimizing Features, Algorithms, and Output  

E-print Network

Dutch Named Entity Recognition: Optimizing Features, Algorithms, and Output Toine Bogers a . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1.2 Named Entity Recognition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.2.1 Applications of Named Entity Recognition . . . . . . . . . . . . . . . . . . . . 4 1

Bogers, Toine

316

A Sequential Quadratic Optimization Algorithm with Rapid Infeasibility Detection  

E-print Network

, Frank E. Curtis, and Hao Wang Lehigh Industrial and Systems Engineering COR@L Technical Report 2012T-12, FRANK E. CURTIS, AND HAO WANG Abstract. We present a sequential quadratic optimization (SQO) algorithm

Snyder, Larry

317

Application of a gradient-based algorithm to structural optimization  

E-print Network

Optimization methods have shown to be efficient at improving structural design, but their use is limited in the engineering practice by the difficulty of adapting state-of-the-art algorithms to particular engineering ...

Ghisbain, Pierre

2009-01-01

318

Hybrid NN/SVM Computational System for Optimizing Designs  

NASA Technical Reports Server (NTRS)

A computational method and system based on a hybrid of an artificial neural network (NN) and a support vector machine (SVM) (see figure) has been conceived as a means of maximizing or minimizing an objective function, optionally subject to one or more constraints. Such maximization or minimization could be performed, for example, to optimize solve a data-regression or data-classification problem or to optimize a design associated with a response function. A response function can be considered as a subset of a response surface, which is a surface in a vector space of design and performance parameters. A typical example of a design problem that the method and system can be used to solve is that of an airfoil, for which a response function could be the spatial distribution of pressure over the airfoil. In this example, the response surface would describe the pressure distribution as a function of the operating conditions and the geometric parameters of the airfoil. The use of NNs to analyze physical objects in order to optimize their responses under specified physical conditions is well known. NN analysis is suitable for multidimensional interpolation of data that lack structure and enables the representation and optimization of a succession of numerical solutions of increasing complexity or increasing fidelity to the real world. NN analysis is especially useful in helping to satisfy multiple design objectives. Feedforward NNs can be used to make estimates based on nonlinear mathematical models. One difficulty associated with use of a feedforward NN arises from the need for nonlinear optimization to determine connection weights among input, intermediate, and output variables. It can be very expensive to train an NN in cases in which it is necessary to model large amounts of information. Less widely known (in comparison with NNs) are support vector machines (SVMs), which were originally applied in statistical learning theory. In terms that are necessarily oversimplified to fit the scope of this article, an SVM can be characterized as an algorithm that (1) effects a nonlinear mapping of input vectors into a higher-dimensional feature space and (2) involves a dual formulation of governing equations and constraints. One advantageous feature of the SVM approach is that an objective function (which one seeks to minimize to obtain coefficients that define an SVM mathematical model) is convex, so that unlike in the cases of many NN models, any local minimum of an SVM model is also a global minimum.

Rai, Man Mohan

2009-01-01

319

A Hybrid Clustering Algorithm for Fire Detection in Video and Analysis with Color Based Thresholding Method  

Microsoft Academic Search

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

Ishita Chakraborty; Tanoy Kr. Paul

2010-01-01

320

Optimal sizing study of hybrid wind/PV/diesel power generation unit  

SciTech Connect

In this paper, a methodology of sizing optimization of a stand-alone hybrid wind/PV/diesel energy system is presented. This approach makes use of a deterministic algorithm to suggest, among a list of commercially available system devices, the optimal number and type of units ensuring that the total cost of the system is minimized while guaranteeing the availability of the energy. The collection of 6 months of data of wind speed, solar radiation and ambient temperature recorded for every hour of the day were used. The mathematical modeling of the main elements of the hybrid wind/PV/diesel system is exposed showing the more relevant sizing variables. A deterministic algorithm is used to minimize the total cost of the system while guaranteeing the satisfaction of the load demand. A comparison between the total cost of the hybrid wind/PV/diesel energy system with batteries and the hybrid wind/PV/diesel energy system without batteries is presented. The reached results demonstrate the practical utility of the used sizing methodology and show the influence of the battery storage on the total cost of the hybrid system. (author)

Belfkira, Rachid; Zhang, Lu; Barakat, Georges [Groupe de Recherche en Electrotechnique et Automatique du Havre, University of Le Havre, 25 rue Philippe Lebon, BP 1123, 76063 Le Havre (France)

2011-01-15

321

Optimization of image processing algorithms on mobile platforms  

Microsoft Academic Search

This work presents a technique to optimize popular image processing algorithms on mobile platforms such as cell phones, net-books and personal digital assistants (PDAs). The increasing demand for video applications like context-aware computing on mobile embedded systems requires the use of computationally intensive image processing algorithms. The system engineer has a mandate to optimize them so as to meet real-time

Pramod Poudel; Mukul Shirvaikar

2011-01-01

322

Standard Harmony Search Algorithm for Structural Design Optimization  

Microsoft Academic Search

Most engineering optimization algorithms 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

Kang Seok Lee

323

Parameters optimization on DHSVM model based on a genetic algorithm  

Microsoft Academic Search

Due to the multiplicity of factors including weather, the underlying surface and human activities, the complexity of parameter\\u000a optimization for a distributed hydrological model of a watershed land surface goes far beyond the capability of traditional\\u000a optimization methods. The genetic algorithm is a new attempt to find a solution to this problem. A genetic algorithm design\\u000a on the Distributed-Hydrology-Soil-Vegetation model

Changqing Yao; Zhifeng Yang

2009-01-01

324

A scaled nonlinear conjugate gradient algorithm for unconstrained optimization  

Microsoft Academic Search

The best spectral conjugate gradient algorithm by (Birgin, E. and Martínez, J.M., 2001, A spectral conjugate gradient method for unconstrained optimization. Applied Mathematics and Optimization, 43, 117–128). which is mainly a scaled variant of (Perry, J.M., 1977, A class of Conjugate gradient algorithms with a two step varaiable metric memory, Discussion Paper 269, Center for Mathematical Studies in Economics and

Neculai Andrei

2008-01-01

325

Truncated-newtono algorithms for large-scale unconstrained optimization  

Microsoft Academic Search

We present an algorithm for large-scale unconstrained optimization based onNewton's method. In large-scale optimization, solving\\u000a the Newton equations at each iteration can be expensive and may not be justified when far from a solution. Instead, an inaccurate\\u000a solution to the Newton equations is computed using a conjugate gradient method. The resulting algorithm is shown to have strong\\u000a convergence properties and

Ron S. Dembo; Trond Steihaug

1983-01-01

326

A chaotic firefly algorithm applied to reliability-redundancy optimization  

Microsoft Academic Search

The reliability-redundancy allocation problem can be approached as a mixed-integer programming problem. It has been solved by using optimization techniques such as dynamic programming, integer programming, and mixed-integer non- linear programming. On the other hand, a broad class of meta- heuristics has been developed for reliability-redundancy optimization. Recently, a new meta-heuristics called firefly algorithm (FA) algorithm has emerged. The FA

Leandro dos Santos Coelho; Diego Luis de Andrade Bernert; Viviana Cocco Mariani

2011-01-01

327

Hybrid optimization for 13C metabolic flux analysis using systems parametrized by compactification  

PubMed Central

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 hybrid optimization algorithm. 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 hybrid algorithm 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 hybrid optimization 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. PMID:18366780

Yang, Tae Hoon; Frick, Oliver; Heinzle, Elmar

2008-01-01

328

Using Genetic Algorithms to Optimize Operating System Parameters  

E-print Network

Using Genetic Algorithms to Optimize Operating System Parameters Dror G. Feitelson Michael Naaman files containing information about the local workload, and genetic algorithms are used to select of parame­ ters that can be modified by the system administrator in order to tune system performance

Feitelson, Dror

329

Optimal design of plant lighting system by genetic algorithms  

Microsoft Academic Search

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

Konstantinos P. Ferentinos; L. D. Albright

2005-01-01

330

Optimized View Frustum Culling Algorithms Ulf Assarsson and Tomas Moller  

E-print Network

. First we develop a fast basic VFC algorithm. Then we suggest and eval- uate four further optimizations, which are independent of each other and works for all kinds of VFC algorithms that test the bounding of the scene graph. A view frustum culler (VFC) culls away the nodes that lie outside the view frustum, i

Assarsson, Ulf

331

Optimal network problem: a branch-and-bound algorithm  

Microsoft Academic Search

The problem of selecting a subset of links so as to minimize the sum of shortest path distances between all pairs of nodes, subject to a budget constraint on total length of links, may be solved by a modification of a branch-and-bound algorithm developed for optimal variable selection problems in statistics. The modified algorithm is described in detail, and encouraging

D E Boyce; A Farhi; R Weischedel

1973-01-01

332

NORTHWESTERN UNIVERSITY Algorithms for LargeScale Nonlinear Optimization  

E-print Network

of Computer Engineering By Richard Alan Waltz EVANSTON, ILLINOIS June 2002 #12; c fl Copyright by Richard Alan Waltz 2002 All Rights Reserved ii #12; ABSTRACT Algorithms for Large­Scale Nonlinear Optimization Richard Alan Waltz Ph.D. advisor: Jorge Nocedal We investigate two algorithmic approaches

Waltz, Richard A.

333

A novel Fly Optimization Algorithm for swarming application  

Microsoft Academic Search

This paper presents an initial development stage of Fly Optimization Algorithm which will be used for the path planning system of a swarm of autonomous surface vehicles. This algorithm was initially designed to be implemented for a swarm of robots which would be able to locate the deepest portion of lakes. The ability of the robots to reach the designated

Zulkifli Zainal Abidin; Umi Kalthum Ngah; Mohd Rizal Arshad; Ong Boon Ping

2010-01-01

334

A Filter-Based Evolutionary Algorithm for Constrained Optimization  

Microsoft Academic Search

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

Lauren M. Clevenger; Lauren Ferguson; William E. Hart

2005-01-01

335

Time screening optimization algorithm for ROSAT PSPC/HRI observations  

E-print Network

Time screening optimization algorithm for ROSAT PSPC/HRI observations F. Bocchino 1 , M. Barbera 1 an algorithm for time screening PSPC/HRI observations in order to maximize the signal to noise (SNR) ratio signal to noise ratio (SNR) computed in a time screened observation (SNR) s and in the entire unscreened

336

A new discrete filled function algorithm for discrete global optimization  

Microsoft Academic Search

A definition of the discrete filled function is given in this paper. Based on the definition, a discrete filled function is proposed. Theoretical properties of the proposed discrete filled function are investigated, and an algorithm for discrete global optimization is developed from the new discrete filled function. The implementation of the algorithms on several test problems is reported with satisfactory

Yang Yongjian; Liang Yumei

2007-01-01

337

Genetic algorithm optimization applied to electromagnetics: a review  

Microsoft Academic Search

Genetic algorithms are on the rise in electromagnetics as design tools and problem solvers because of their versatility and ability to optimize in complex multimodal search spaces. This paper describes the basic genetic algorithm and recounts its history in the electromagnetics literature. Also, the application of advanced genetic operators to the field of electromagnetics is described, and design results are

Daniel S. Weile; Eric Michielssen

1997-01-01

338

A parallel variable metric optimization algorithm  

NASA Technical Reports Server (NTRS)

An algorithm, designed to exploit the parallel computing or vector streaming (pipeline) capabilities of computers is presented. When p is the degree of parallelism, then one cycle of the parallel variable metric algorithm is defined as follows: first, the function and its gradient are computed in parallel at p different values of the independent variable; then the metric is modified by p rank-one corrections; and finally, a single univariant minimization is carried out in the Newton-like direction. Several properties of this algorithm are established. The convergence of the iterates to the solution is proved for a quadratic functional on a real separable Hilbert space. For a finite-dimensional space the convergence is in one cycle when p equals the dimension of the space. Results of numerical experiments indicate that the new algorithm will exploit parallel or pipeline computing capabilities to effect faster convergence than serial techniques.

Straeter, T. A.

1973-01-01

339

An Ellipsoidal Branch and Bound Algorithm for Global Optimization  

Microsoft Academic Search

A branch and bound algorithm is developed for global optimization. Branching\\u000ain the algorithm is accomplished by subdividing the feasible set using\\u000aellipses. Lower bounds are obtained by replacing the concave part of the\\u000aobjective function by an affine underestimate. A ball approximation algorithm,\\u000aobtained by generalizing of a scheme of Lin and Han, is used to solve the\\u000aconvex

William W. Hager; Dzung T. Phan

2009-01-01

340

Genetic algorithm-based optimization for cognitive radio networks  

Microsoft Academic Search

Genetic algorithms are well suited for optimization problems involving large search spaces. In this paper, we present several approaches designed to enhance the convergence time and\\/or improve the performance results of genetic algorithm-based search engine for cognitive radio networks, including techniques such as population adaptation, variable quantization, variable adaptation, and multi-objective genetic algorithms (MOGA). Note that the time required for

Si Chen; Timothy R. Newman; Joseph B. Evans; Alexander M. Wyglinski

2010-01-01

341

Airfoil And Wing Design Through Hybrid Optimization Strategies  

Microsoft Academic Search

IntroductionSeveral techniques are today available for designthrough numerical optimization;1concerning inparticular the field of aerodynamic design, beyondmethods developed ad hoc and characterizedby inverse design capabilities, the techniquesmore properly related to direct optimization includemature gradient based methods, and morerecent approaches like automatic di#erentiation,control theory based methods and genetic algorithms (GAs). Generally speaking, it is not possibleto...

A. Vicini; D. Quagliarella

1998-01-01

342

Segregative Genetic Algorithms (SEGA): A hybrid superstructure upwards compatible to genetic algorithms for retarding premature convergence  

Microsoft Academic Search

Many problems of combinatorial optimization belong to the class of NP-complete problems and can be solved efficiently only by heuristics. Both, GeneticAlgorithms and Evolution Strategies have a number of drawbacks that reduce their applicability to that kind of problems. During the last decades plenty of work has been investigated in order to introduce new coding standards and operators especially for

Michael Affenzeller

2001-01-01

343

Segregative Genetic Algorithms (SEGA): A Hybrid Superstructure Upwards Compatible to Genetic Algorithms for Retarding Premature Convergence  

Microsoft Academic Search

Many problems of combinatorial optimization belong to the class of NP-complete problems and can be solved e-ciently only by heuristics. Both, Genetic Algorithms and Evolution Strategies have a number of drawbacks that reduce their applicability to that kind of problems. During the last decades plenty of work has been investigated in order to introduce new coding standards and operators especially

Michael Afienzeller

2001-01-01

344

A novel particle swarm optimizer hybridized with extremal optimization  

E-print Network

Shanghai Jiao Tong University, Shanghai 200240, P.R.China. Abstract .... In this study, we adopt the term “component” to represent “species” which is .... In order to compare the different algorithms, a fair time measure must be selected. The.

2008-05-26

345

New evolutionary algorithm for EBG materials optimization  

NASA Astrophysics Data System (ADS)

EBG structures are typically two or three dimensional periodic media characterized by the capability to inhibit the electromagnetic wave propagation for each angle and each polarization in a specific frequency band. These complex structures present different degrees of freedom, that can be used to optimize the performances of the application. On the other hand, the management of different degrees of freedom can result in the increasing of the complexity in the entire device-design procedure. The aim of this research is to analyse the optimization of EBG materials by means of a new technique: the Genetical Swarm Optimization (GSO). This approach consists of a co-operation of GA and PSO. The GSO results in a fast method for optimization of complex nonlinear objective functions and its wider potential makes it suitable for every electromagnetic applications. These optimized synthetic materials can represent an opportunity for the development and design of innovative electromagnetic devices.

Gandelli, Alessandro; Grimaccia, Francesco; Mussetta, Marco; Pirinoli, Paola; Zich, Riccardo E.

2004-02-01

346

Branch-and-Cut Algorithms for Combinatorial Optimization Problems1  

E-print Network

Branch-and-Cut Algorithms for Combinatorial Optimization Problems1 John E. Mitchell2 Mathematical of optimality. We describe how a branch-and-cut method can be tailored to a specific integer programming problem://www.math.rpi.edu/~mitchj April 19, 1999, revised September 7, 1999. Abstract Branch-and-cut methods are very successful

Mitchell, John E.

347

BranchandCut Algorithms for Combinatorial Optimization Problems 1  

E-print Network

Branch­and­Cut Algorithms for Combinatorial Optimization Problems 1 John E. Mitchell 2 Mathematical of optimality. We describe how a branch­and­cut method can be tailored to a specific integer programming problem://www.math.rpi.edu/�mitchj April 19, 1999, revised September 7, 1999. Abstract Branch­and­cut methods are very successful

Mitchell, John E.

348

Model Specification Searches Using Ant Colony Optimization Algorithms  

ERIC Educational Resources Information Center

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 optimization algorithms for conducting automated searches.

Marcoulides, George A.; Drezner, Zvi

2003-01-01

349

Algorithm for the Optimal Riding Scheme Problem in Public traffic  

Microsoft Academic Search

A two-stage algorithm is proposed for the optimal riding scheme problem in public traffic querying system. The first stage is to find out the least transfer schemes, in which bus line network model is presented to convert the least transfer scheme problem into the shortest path problem. The second stage is to search out the optimal riding scheme from the

Dong Jiyang; Chen Luzhuo

2005-01-01

350

Optimizing the reservoir operating rule curves by genetic algorithms  

Microsoft Academic Search

Genetic algorithms, founded upon the principle of evolution, are applicable to many optimization problems, especially popular for solving parameter optimization problems. Reservoir operating rule curves are the most common way for guiding and managing the reservoir operation. These rule curves traditionally are derived through intensive simulation techniques. The main aim of this study is to investigate the efficiency and effectiveness

Fi-John Chang; Li Chen; Li-Chiu Chang

2005-01-01

351

Design optimization of electrical machines using genetic algorithms  

Microsoft Academic Search

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

G. F. Uler; O. A. Mohammed; Chang-Seop Koh

1995-01-01

352

Targeted 2D/3D registration using ray normalization and a hybrid optimizer  

SciTech Connect

X-ray images are often used to guide minimally invasive procedures in interventional radiology. The use of a preoperatively obtained 3D volume can enhance the visualization needed for guiding catheters and other surgical devices. However, for intraoperative usefulness, the 3D dataset needs to be registered to the 2D x-ray images of the patient. We investigated the effect of targeting subvolumes of interest in the 3D datasets and registering the projections with C-arm x-ray images. We developed an intensity-based 2D/3D rigid-body registration using a Monte Carlo-based hybrid algorithm as the optimizer, using a single view for registration. Pattern intensity (PI) and mutual information (MI) were two metrics tested. We used normalization of the rays to address the problems due to truncation in 3D necessary for targeting. We tested the algorithm on a C-arm x-ray image of a pig's head and a 3D dataset reconstructed from multiple views of the C-arm. PI and MI were comparable in performance. For two subvolumes starting with a set of initial poses from +/-15 mm in x, from +/-3 mm (random), in y and z and +/-4 deg in the three angles, the robustness was 94% for PI and 91% for MI, with accuracy of 2.4 mm (PI) and 2.6 mm (MI), using the hybrid algorithm. The hybrid optimizer, when compared with a standard Powell's direction set method, increased the robustness from 59% (Powell) to 94% (hybrid). Another set of 50 random initial conditions from [+/-20] mm in x,y,z and [+/-10] deg in the three angles, yielded robustness of 84% (hybrid) versus 38% (Powell) using PI as metric, with accuracies 2.1 mm (hybrid) versus 2.0 mm (Powell)

Dey, Joyoni; Napel, Sandy [Department of Radiology, University of Massachusetts Medical School, Worcester, Massachusetts 01655 (United States); Department of Radiology, Stanford University, Palo Alto, California (United States)

2006-12-15

353

Optimal placement of sensors for structural system identification and health monitoring using a hybrid swarm intelligence technique  

NASA Astrophysics Data System (ADS)

Setting up a health monitoring system for large-scale civil engineering structures requires a large number of sensors and the placement of these sensors is of great significance for such spatially separated large structures. In this paper, we present an optimal sensor placement (OSP) algorithm by treating OSP as a combinatorial optimization problem which is solved using a swarm intelligence technique called particle swarm optimization (PSO). We propose a new hybrid PSO algorithm by combining a self-configurable PSO with the Nelder-Mead algorithm to solve this rather difficult combinatorial problem of OSP. The proposed algorithm aims precisely to achieve the best identification of modal frequencies and mode shapes. Numerical experiments have been carried out by considering civil engineering structures to evaluate the performance of the proposed swarm-intelligence-based OSP algorithm. Numerical studies indicate that the proposed hybrid PSO algorithm generates sensor configurations superior to the conventional iterative information-based approaches which have been popularly used for large structures. Further, the proposed hybrid PSO algorithm exhibits superior convergence characteristics when compared to other PSO counterparts.

Rama Mohan Rao, A.; Anandakumar, Ganesh

2007-12-01

354

A Discrete Lagrangian Algorithm for Optimal Routing Problems  

SciTech Connect

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)

2008-11-06

355

Approximation algorithms for trilinear optimization with nonconvex ...  

E-print Network

Apr 2, 2011 ... mization problems, which has a close relationship with the trilinear optimization problems. ..... rational numbers 0 encoding lengths are ..... From linear algebra, we immediately have another conclusion.

2011-04-02

356

Approximate algorithms for Space Station Maneuver Optimization  

E-print Network

. Torque smoothing is used to avoid discontinuous jumps in the control which would excite vibrational modes in the structure. The switch times, maximum thrust magnitudes, and optimal final maneuver times are determined using the MATLAB built-in function...

Mur-Dongil, Andres

2012-06-07

357

Approximation algorithms for combinatorial optimization under uncertainty  

E-print Network

Combinatorial optimization problems arise in many fields of industry and technology, where they are frequently used in production planning, transportation, and communication network design. Whereas in the context of classical ...

Minkoff, Maria, 1976-

2003-01-01

358

Parallel Algorithms for Big Data Optimization  

E-print Network

Index Terms—Parallel optimization, Distributed methods, Ja- cobi method ... Usually the nonsmooth term is used to ..... dard Armijo-like line-search procedure or a (suitably small) constant ..... enter the identification phase xk i is not zero, the

2014-02-21

359

OPTIMIZATION OF LONG RURAL FEEDERS USING A GENETIC ALGORITHM  

SciTech Connect

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)

2010-06-15

360

Horizontal Well Placement Optimization in Gas Reservoirs Using Genetic Algorithms  

E-print Network

(1995) optimized the drilling schedule and well location in an oil reservoir through a traveling salesman structure with the use of Simulated Annealing. Bittencourt and Horne (1997) approached the well placement optimization problem using a genetic... robust, stochastic, and streamlined optimization method. Genetic Algorithms ?efficiently exploit historical information to speculate on new search points with expected improved performance.? (Goldberg 1989) The GA population is represented by a...

Gibbs, Trevor Howard

2011-08-08

361

Artificial bee colony algorithm for solving optimal power flow problem.  

PubMed

This paper proposes an artificial bee colony (ABC) algorithm for solving optimal power flow (OPF) problem. The objective of the OPF problem is to minimize total cost of thermal units while satisfying the unit and system constraints such as generator capacity limits, power balance, line flow limits, bus voltages limits, and transformer tap settings limits. The ABC algorithm is an optimization method inspired from the foraging behavior of honey bees. The proposed algorithm has been tested on the IEEE 30-bus, 57-bus, and 118-bus systems. The numerical results have indicated that the proposed algorithm can find high quality solution for the problem in a fast manner via the result comparisons with other methods in the literature. Therefore, the proposed ABC algorithm can be a favorable method for solving the OPF problem. PMID:24470790

Le Dinh, Luong; Vo Ngoc, Dieu; Vasant, Pandian

2013-01-01

362

Hybrid Reduced Order Modeling Algorithms for Reactor Physics Calculations  

NASA Astrophysics Data System (ADS)

Reduced order modeling (ROM) has been recognized as an indispensable approach when the engineering analysis requires many executions of high fidelity simulation codes. Examples of such engineering analyses in nuclear reactor core calculations, representing the focus of this dissertation, include the functionalization of the homogenized few-group cross-sections in terms of the various core conditions, e.g. burn-up, fuel enrichment, temperature, etc. This is done via assembly calculations which are executed many times to generate the required functionalization for use in the downstream core calculations. Other examples are sensitivity analysis used to determine important core attribute variations due to input parameter variations, and uncertainty quantification employed to estimate core attribute uncertainties originating from input parameter uncertainties. ROM constructs a surrogate model with quantifiable accuracy which can replace the original code for subsequent engineering analysis calculations. This is achieved by reducing the effective dimensionality of the input parameter, the state variable, or the output response spaces, by projection onto the so-called active subspaces. Confining the variations to the active subspace allows one to construct an ROM model of reduced complexity which can be solved more efficiently. This dissertation introduces a new algorithm to render reduction with the reduction errors bounded based on a user-defined error tolerance which represents the main challenge of existing ROM techniques. Bounding the error is the key to ensuring that the constructed ROM models are robust for all possible applications. Providing such error bounds represents one of the algorithmic contributions of this dissertation to the ROM state-of-the-art. Recognizing that ROM techniques have been developed to render reduction at different levels, e.g. the input parameter space, the state space, and the response space, this dissertation offers a set of novel hybrid ROM algorithms which can be readily integrated into existing methods and offer higher computational efficiency and defendable accuracy of the reduced models. For example, the snapshots ROM algorithm is hybridized with the range finding algorithm to render reduction in the state space, e.g. the flux in reactor calculations. In another implementation, the perturbation theory used to calculate first order derivatives of responses with respect to parameters is hybridized with a forward sensitivity analysis approach to render reduction in the parameter space. Reduction at the state and parameter spaces can be combined to render further reduction at the interface between different physics codes in a multi-physics model with the accuracy quantified in a similar manner to the single physics case. Although the proposed algorithms are generic in nature, we focus here on radiation transport models used in support of the design and analysis of nuclear reactor cores. In particular, we focus on replacing the traditional assembly calculations by ROM models to facilitate the generation of homogenized cross-sections for downstream core calculations. The implication is that assembly calculations could be done instantaneously therefore precluding the need for the expensive evaluation of the few-group cross-sections for all possible core conditions. Given the generic natures of the algorithms, we make an effort to introduce the material in a general form to allow non-nuclear engineers to benefit from this work.

Bang, Youngsuk

363

Evaluation of hybrids algorithms for mass detection in digitalized mammograms  

NASA Astrophysics Data System (ADS)

The breast cancer remains being a significant public health problem, the early detection of the lesions can increase the success possibilities of the medical treatments. The mammography is an image modality effective to early diagnosis of abnormalities, where the medical image is obtained of the mammary gland with X-rays of low radiation, this allows detect a tumor or circumscribed mass between two to three years before that it was clinically palpable, and is the only method that until now achieved reducing the mortality by breast cancer. In this paper three hybrids algorithms for circumscribed mass detection on digitalized mammograms are evaluated. In the first stage correspond to a review of the enhancement and segmentation techniques used in the processing of the mammographic images. After a shape filtering was applied to the resulting regions. By mean of a Bayesian filter the survivors regions were processed, where the characteristics vector for the classifier was constructed with few measurements. Later, the implemented algorithms were evaluated by ROC curves, where 40 images were taken for the test, 20 normal images and 20 images with circumscribed lesions. Finally, the advantages and disadvantages in the correct detection of a lesion of every algorithm are discussed.

Cordero, José; Garzón Reyes, Johnson

2011-01-01

364

Binary wavefront optimization using a genetic algorithm  

NASA Astrophysics Data System (ADS)

We demonstrate the use of a genetic algorithm with binary amplitude modulation of light through turbid media. We apply this method to binary amplitude modulation with a digital micromirror device. We achieve the theoretical maximum enhancement of 64 with 384 segments, and an enhancement of 320 with 6144 segments.

Zhang, Xiaolong; Kner, Peter

2014-12-01

365

Benchmarking Derivative-Free Optimization Algorithms  

E-print Network

problems, ordinary or partial differential equations) that describe the ... The computational noise associated with these complex simulations means that ... algorithms are compared by their trajectories (plot of the best function value against the ..... We now explore an extension of Theorem 2.1 to nonlinear functions that is ...

2008-05-13

366

Genetic algorithm optimization of feedback control systems  

Microsoft Academic Search

In this paper we are concerned with Smart Materials that contain many actuators and sensors along with digital signal processing electronics that allow for the implementation of a control algorithm. Smart Materials have been proposed for the active control of sound from a vibrating structure. Here we investigate the design of structural control systems for these Smart Structures for noise

Douglas K. Lindner; Gregory A. Zvonar; George C. Kirby; Grant M. Emery

1996-01-01

367

Searching for Pareto-optimal Randomised Algorithms  

E-print Network

the potential benefits of allowing randomised algorithms to use non-uniform probability distributions. We show. Clark1 1 Department of Computer Science, University of York, UK {millard, jac}@cs.york.ac.uk 2 School traditionally make stochastic deci- sions based on the result of sampling from a uniform probability dis

White, David R.

368

A training algorithm for optimal margin classifiers  

Microsoft Academic Search

A training algorithm that maximizes the margin between the training patterns and the decision boundary is presented. The technique is applicable to a wide variety of the classification functions, including Perceptrons, polynomials, and Radial Basis Functions. The effective number of parameters is adjusted automatically to match the complexity of the problem. The solution is expressed as a linear combination of

Bernhard E. Boser; Isabelle M. Guyon; Vladimir N. Vapnik

1992-01-01

369

Lazy skip-lists: An algorithm for fast hybridization-expansion quantum Monte Carlo  

NASA Astrophysics Data System (ADS)

The solution of a generalized impurity model lies at the heart of electronic structure calculations with dynamical mean field theory. In the strongly correlated regime, the method of choice for solving the impurity model is the hybridization-expansion continuous-time quantum Monte Carlo (CT-HYB). Enhancements to the CT-HYB algorithm are critical for bringing new physical regimes within reach of current computational power. Taking advantage of the fact that the bottleneck in the algorithm is a product of hundreds of matrices, we present optimizations based on the introduction and combination of two concepts of more general applicability: (a) skip lists and (b) fast rejection of proposed configurations based on matrix bounds. Considering two very different test cases with d electrons, we find speedups of ˜25 up to ˜500 compared to the direct evaluation of the matrix product. Even larger speedups are likely with f electron systems and with clusters of correlated atoms.

Sémon, P.; Yee, Chuck-Hou; Haule, Kristjan; Tremblay, A.-M. S.

2014-08-01

370

Forecasting of ozone level in time series using MLP model with a novel hybrid training algorithm  

NASA Astrophysics Data System (ADS)

As far as the impact of tropospheric ozone (O 3) on human heath and plant life are concerned, forecasting its daily maximum level is of great importance in Hong Kong as well as other metropolises in the world. This paper proposed a multi-layer perceptron (MLP) model with a novel hybrid training method to perform the forecasting task. The training method synergistically couples a stochastic particle swarm optimization (PSO) algorithm and a deterministic Levenberg-Marquardt (LM) algorithm, which aims at exploiting the advantage of both. The performance of such a hybrid model is further compared with ones obtained by the MLP model trained individually by these two training methods mentioned above. Based on original data collected from two typical monitoring sites with different O 3 formation and transportation mechanism, the simulation results show that the hybrid model is more robust and efficient than the other two models by not only producing good results during non-episodes but also providing better consistency with the original data during episodes.

Wang, Dong; Lu, Wei-Zhen

371

Solving constrained optimization problems with hybrid particle swarm optimization  

NASA Astrophysics Data System (ADS)

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.

Zahara, Erwie; Hu, Chia-Hsin

2008-11-01

372

An Improved Hybrid Encoding Cuckoo Search Algorithm for 0-1 Knapsack Problems  

PubMed Central

Cuckoo search (CS) is a new robust swarm intelligence method that is based on the brood parasitism of some cuckoo species. In this paper, an improved hybrid encoding cuckoo search algorithm (ICS) with greedy strategy is put forward for solving 0-1 knapsack problems. First of all, for solving binary optimization problem with ICS, based on the idea of individual hybrid encoding, the cuckoo search over a continuous space is transformed into the synchronous evolution search over discrete space. Subsequently, the concept of confidence interval (CI) is introduced; hence, the new position updating is designed and genetic mutation with a small probability is introduced. The former enables the population to move towards the global best solution rapidly in every generation, and the latter can effectively prevent the ICS from trapping into the local optimum. Furthermore, the greedy transform method is used to repair the infeasible solution and optimize the feasible solution. Experiments with a large number of KP instances show the effectiveness of the proposed algorithm and its ability to achieve good quality solutions. PMID:24527026

Feng, Yanhong; Jia, Ke; He, Yichao

2014-01-01

373

An improved hybrid encoding cuckoo search algorithm for 0-1 knapsack problems.  

PubMed

Cuckoo search (CS) is a new robust swarm intelligence method that is based on the brood parasitism of some cuckoo species. In this paper, an improved hybrid encoding cuckoo search algorithm (ICS) with greedy strategy is put forward for solving 0-1 knapsack problems. First of all, for solving binary optimization problem with ICS, based on the idea of individual hybrid encoding, the cuckoo search over a continuous space is transformed into the synchronous evolution search over discrete space. Subsequently, the concept of confidence interval (CI) is introduced; hence, the new position updating is designed and genetic mutation with a small probability is introduced. The former enables the population to move towards the global best solution rapidly in every generation, and the latter can effectively prevent the ICS from trapping into the local optimum. Furthermore, the greedy transform method is used to repair the infeasible solution and optimize the feasible solution. Experiments with a large number of KP instances show the effectiveness of the proposed algorithm and its ability to achieve good quality solutions. PMID:24527026

Feng, Yanhong; Jia, Ke; He, Yichao

2014-01-01

374

A Hybrid PSO-BFGS Strategy for Global Optimization of Multimodal Functions.  

PubMed

Particle swarm optimizer (PSO) is a powerful optimization algorithm that has been applied to a variety of problems. It can, however, suffer from premature convergence and slow convergence rate. Motivated by these two problems, a hybrid global optimization strategy combining PSOs with a modified Broyden-Fletcher-Goldfarb-Shanno (BFGS) method is presented in this paper. The modified BFGS method is integrated into the context of the PSOs to improve the particles' local search ability. In addition, in conjunction with the territory technique, a reposition technique to maintain the diversity of particles is proposed to improve the global search ability of PSOs. One advantage of the hybrid strategy is that it can effectively find multiple local solutions or global solutions to the multimodal functions in a box-constrained space. Based on these local solutions, a reconstruction technique can be adopted to further estimate better solutions. The proposed method is compared with several recently developed optimization algorithms on a set of 20 standard benchmark problems. Experimental results demonstrate that the proposed approach can obtain high-quality solutions on multimodal function optimization problems. PMID:21278022

Li, Shutao; Tan, Mingkui; Tsang, Ivor W; Kwok, James Tin-Yau

2011-01-28

375

A Hybrid Quantum Search Engine: A Fast Quantum Algorithm for Multiple Matches  

Microsoft Academic Search

In this paper we will present a quantum algorithm which works very efficiently in case of multiple matches within the search space and in the case of few matches, the algorithm performs classically. This allows us to propose a hybrid quantum search engine that integrates Grover's algorithm and the proposed algorithm here to have general performance better that any pure

Ahmed Younes; Jon Rowe; Julian Miller

2003-01-01

376

An enhanced DWBA algorithm in hybrid WDM/TDM EPON networks with heterogeneous propagation delays  

NASA Astrophysics Data System (ADS)

An enhanced dynamic wavelength and bandwidth allocation (DWBA) algorithm in hybrid WDM/TDM PON is proposed and experimentally demonstrated. In addition to the fairness of bandwidth allocation, this algorithm also considers the varying propagation delays between ONUs and OLT. The simulation based on MATLAB indicates that the improved algorithm has a better performance compared with some other algorithms.

Li, Chengjun; Guo, Wei; Jin, Yaohui; Sun, Weiqiang; Hu, Weisheng

2011-12-01

377

A Hybrid Search Algorithm for Swarm Robots Searching in an Unknown Environment  

PubMed Central

This paper proposes a novel method to improve the efficiency of a swarm of robots searching in an unknown environment. The approach focuses on the process of feeding and individual coordination characteristics inspired by the foraging behavior in nature. A predatory strategy was used for searching; hence, this hybrid approach integrated a random search technique with a dynamic particle swarm optimization (DPSO) search algorithm. If a search robot could not find any target information, it used a random search algorithm for a global search. If the robot found any target information in a region, the DPSO search algorithm was used for a local search. This particle swarm optimization search algorithm is dynamic as all the parameters in the algorithm are refreshed synchronously through a communication mechanism until the robots find the target position, after which, the robots fall back to a random searching mode. Thus, in this searching strategy, the robots alternated between two searching algorithms until the whole area was covered. During the searching process, the robots used a local communication mechanism to share map information and DPSO parameters to reduce the communication burden and overcome hardware limitations. If the search area is very large, search efficiency may be greatly reduced if only one robot searches an entire region given the limited resources available and time constraints. In this research we divided the entire search area into several subregions, selected a target utility function to determine which subregion should be initially searched and thereby reduced the residence time of the target to improve search efficiency. PMID:25386855

Li, Shoutao; Li, Lina; Lee, Gordon; Zhang, Hao

2014-01-01

378

A solution quality assessment method for swarm intelligence optimization algorithms.  

PubMed

Nowadays, swarm intelligence optimization has become an important optimization tool and wildly used in many fields of application. In contrast to many successful applications, the theoretical foundation is rather weak. Therefore, there are still many problems to be solved. One problem is how to quantify the performance of algorithm in finite time, that is, how to evaluate the solution quality got by algorithm for practical problems. It greatly limits the application in practical problems. A solution quality assessment method for intelligent optimization is proposed in this paper. It is an experimental analysis method based on the analysis of search space and characteristic of algorithm itself. Instead of "value performance," the "ordinal performance" is used as evaluation criteria in this method. The feasible solutions were clustered according to distance to divide solution samples into several parts. Then, solution space and "good enough" set can be decomposed based on the clustering results. Last, using relative knowledge of statistics, the evaluation result can be got. To validate the proposed method, some intelligent algorithms such as ant colony optimization (ACO), particle swarm optimization (PSO), and artificial fish swarm algorithm (AFS) were taken to solve traveling salesman problem. Computational results indicate the feasibility of proposed method. PMID:25013845

Zhang, Zhaojun; Wang, Gai-Ge; Zou, Kuansheng; Zhang, Jianhua

2014-01-01

379

Bayesian Optimization Algorithm for Multi-Objective Solutions: Application to Electric Equipment Configuration Problems in a Power Plant  

E-print Network

Search (Tabu-BOA) to electric equipments configuration problems in a power plant. Tabu-BOA is a hybridBayesian Optimization Algorithm for Multi-Objective Solutions: Application to Electric Equipment Configuration Problems in a Power Plant Yuji Katsumata Graduate School of Business Science, University

Coello, Carlos A. Coello

380

Swarm Intelligence for Optimizing Hybridized Smoothing Filter in Image Edge Enhancement  

NASA Astrophysics Data System (ADS)

In this modern era, image transmission and processing plays a major role. It would be impossible to retrieve information from satellite and medical images without the help of image processing techniques. Edge enhancement is an image processing step that enhances the edge contrast of an image or video in an attempt to improve its acutance. Edges are the representations of the discontinuities of image intensity functions. For processing these discontinuities in an image, a good edge enhancement technique is essential. The proposed work uses a new idea for edge enhancement using hybridized smoothening filters and we introduce a promising technique of obtaining best hybrid filter using swarm algorithms (Artificial Bee Colony (ABC), Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO)) to search for an optimal sequence of filters from among a set of rather simple, representative image processing filters. This paper deals with the analysis of the swarm intelligence techniques through the combination of hybrid filters generated by these algorithms for image edge enhancement.

Rao, B. Tirumala; Dehuri, S.; Dileep, M.; Vindhya, A.

381

Engineering Optimization Using a Simple Evolutionary Algorithm  

Microsoft Academic Search

This paper presents a simple Evolution Strat- egy and three simple selection criteria to solve engineer- ing optimization problems. This approach avoids the use of a penalty function to deal with constraints. Its main advan- tage is that it does not require the definition of extra pa- rameters, other than those used by the evolution strategy. A self-adaptation mechanism allows

Efrén Mezura-montes; Carlos A. Coello Coello; Ricardo Landa-Becerra

2003-01-01

382

A study of speech emotion recognition based on hybrid algorithm  

NASA Astrophysics Data System (ADS)

To effectively improve the recognition accuracy of the speech emotion recognition system, a hybrid algorithm 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.

Zhu, Ju-xia; Zhang, Chao; Lv, Zhao; Rao, Yao-quan; Wu, Xiao-pei

2011-10-01

383

Performance Trend of Different Algorithms for Structural Design Optimization  

NASA Technical Reports Server (NTRS)

Nonlinear programming algorithms play an important role in structural design optimization. Fortunately, several algorithms with computer codes are available. At NASA Lewis Research Center, a project was initiated to assess performance of different optimizers through the development of a computer code CometBoards. This paper summarizes the conclusions of that research. CometBoards was employed to solve sets of small, medium and large structural problems, using different optimizers on a Cray-YMP8E/8128 computer. The reliability and efficiency of the optimizers were determined from the performance of these problems. For small problems, the performance of most of the optimizers could be considered adequate. For large problems however, three optimizers (two sequential quadratic programming routines, DNCONG of IMSL and SQP of IDESIGN, along with the sequential unconstrained minimizations technique SUMT) outperformed others. At optimum, most optimizers captured an identical number of active displacement and frequency constraints but the number of active stress constraints differed among the optimizers. This discrepancy can be attributed to singularity conditions in the optimization and the alleviation of this discrepancy can improve the efficiency of optimizers.

Patnaik, Surya N.; Coroneos, Rula M.; Guptill, James D.; Hopkins, Dale A.

1996-01-01

384

Comparative Evaluation of Different Optimization Algorithms for Structural Design Applications  

NASA Technical Reports Server (NTRS)

Non-linear programming algorithms play an important role in structural design optimization. Fortunately, several algorithms with computer codes are available. At NASA Lewis Research Centre, a project was initiated to assess the performance of eight different optimizers through the development of a computer code CometBoards. This paper summarizes the conclusions of that research. CometBoards was employed to solve sets of small, medium and large structural problems, using the eight different optimizers on a Cray-YMP8E/8128 computer. The reliability and efficiency of the optimizers were determined from the performance of these problems. For small problems, the performance of most of the optimizers could be considered adequate. For large problems, however, three optimizers (two sequential quadratic programming routines, DNCONG of IMSL and SQP of IDESIGN, along with Sequential Unconstrained Minimizations Technique SUMT) outperformed others. At optimum, most optimizers captured an identical number of active displacement and frequency constraints but the number of active stress constraints differed among the optimizers. This discrepancy can be attributed to singularity conditions in the optimization and the alleviation of this discrepancy can improve the efficiency of optimizers.

Patnaik, Surya N.; Coroneos, Rula M.; Guptill, James D.; Hopkins, Dale A.

1996-01-01

385

Optimization of reliability allocation strategies through use of genetic algorithms  

SciTech Connect

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.

Campbell, J.E.; Painton, L.A.

1996-08-01

386

OPTIMAL DESIGN AND DYNAMIC SIMULATION OF A HYBRID SOLAR VEHICLE  

Microsoft Academic Search

The paper deals with a detailed study on the optimal sizing of a solar hybrid car, based on a longitudinal vehicle dynamic model and considering energy flows, weight and costs. The model describes the effects of solar panels area and position, vehicle dimensions and propulsion system components on vehicle performance, weight, fuel savings and costs. It is shown that significant

Ivan Arsie; Gianfranco Rizzo; Marco Sorrentino

387

Seven-spot ladybird optimization: a novel and efficient metaheuristic algorithm for numerical optimization.  

PubMed

This paper presents a novel biologically inspired metaheuristic algorithm called seven-spot ladybird optimization (SLO). The SLO is inspired by recent discoveries on the foraging behavior of a seven-spot ladybird. In this paper, the performance of the SLO is compared with that of the genetic algorithm, particle swarm optimization, and artificial bee colony algorithms by using five numerical benchmark functions with multimodality. The results show that SLO has the ability to find the best solution with a comparatively small population size and is suitable for solving optimization problems with lower dimensions. PMID:24385879

Wang, Peng; Zhu, Zhouquan; Huang, Shuai

2013-01-01

388

Seven-Spot Ladybird Optimization: A Novel and Efficient Metaheuristic Algorithm for Numerical Optimization  

PubMed Central

This paper presents a novel biologically inspired metaheuristic algorithm called seven-spot ladybird optimization (SLO). The SLO is inspired by recent discoveries on the foraging behavior of a seven-spot ladybird. In this paper, the performance of the SLO is compared with that of the genetic algorithm, particle swarm optimization, and artificial bee colony algorithms by using five numerical benchmark functions with multimodality. The results show that SLO has the ability to find the best solution with a comparatively small population size and is suitable for solving optimization problems with lower dimensions. PMID:24385879

Zhu, Zhouquan

2013-01-01

389

A hybrid multi-objective particle swarm algorithm for a mixed-model assembly line sequencing problem  

NASA Astrophysics Data System (ADS)

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 hybrid algorithm significantly outperforms existing genetic algorithms in large-sized problems.

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

2007-12-01

390

Optimized Algorithms for Prediction within Robotic Tele-Operative Interfaces  

NASA Technical Reports Server (NTRS)

Robonaut, the humanoid robot developed at the Dexterous Robotics Laboratory at NASA Johnson Space Center serves as a testbed for human-robot collaboration research and development efforts. One of the primary efforts investigates how adjustable autonomy can provide for a safe and more effective completion of manipulation-based tasks. A predictive algorithm developed in previous work was deployed as part of a software interface that can be used for long-distance tele-operation. In this paper we provide the details of this algorithm, how to improve upon the methods via optimization, and also present viable alternatives to the original algorithmic approach. We show that all of the algorithms presented can be optimized to meet the specifications of the metrics shown as being useful for measuring the performance of the predictive methods. Judicious feature selection also plays a significant role in the conclusions drawn.

Martin, Rodney A.; Wheeler, Kevin R.; SunSpiral, Vytas; Allan, Mark B.

2006-01-01

391

A local stability supported parallel distributed constraint optimization algorithm.  

PubMed

This paper presents a new distributed constraint optimization algorithm called LSPA, which can be used to solve large scale distributed constraint optimization problem (DCOP). Different from the access of local information in the existing algorithms, a new criterion called local stability is defined and used to evaluate which is the next agent whose value needs to be changed. The propose of local stability opens a new research direction of refining initial solution by finding key agents which can seriously effect global solution once they modify assignments. In addition, the construction of initial solution could be received more quickly without repeated assignment and conflict. In order to execute parallel search, LSPA finds final solution by constantly computing local stability of compatible agents. Experimental evaluation shows that LSPA outperforms some of the state-of-the-art incomplete distributed constraint optimization algorithms, guaranteeing better solutions received within ideal time. PMID:25105166

Peibo, Duan; Changsheng, Zhang; Bin, Zhang

2014-01-01

392

A Local Stability Supported Parallel Distributed Constraint Optimization Algorithm  

PubMed Central

This paper presents a new distributed constraint optimization algorithm called LSPA, which can be used to solve large scale distributed constraint optimization problem (DCOP). Different from the access of local information in the existing algorithms, a new criterion called local stability is defined and used to evaluate which is the next agent whose value needs to be changed. The propose of local stability opens a new research direction of refining initial solution by finding key agents which can seriously effect global solution once they modify assignments. In addition, the construction of initial solution could be received more quickly without repeated assignment and conflict. In order to execute parallel search, LSPA finds final solution by constantly computing local stability of compatible agents. Experimental evaluation shows that LSPA outperforms some of the state-of-the-art incomplete distributed constraint optimization algorithms, guaranteeing better solutions received within ideal time. PMID:25105166

Peibo, Duan; Changsheng, Zhang; Bin, Zhang

2014-01-01

393

Intelligent Parallel Particle Swarm Optimization Algorithms  

Microsoft Academic Search

Some social systems of natural species, such as flocks of birds and schools of fish, possess interesting collective behavior.\\u000a In these systems, globally sophisticated behavior emerges from local, indirect communication amongst simple agents with only\\u000a limited capabilities. In an attempt to simulate this flocking behavior by computers, Kennedy and Eberthart (1995) realized\\u000a that an optimization problem can be formulated as

Shu-chuan Chu; Jeng-shyang Pan

2006-01-01

394

Hybrid intelligent control scheme for air heating system using fuzzy logic and genetic algorithm  

SciTech Connect

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.

2000-01-01

395

An hybrid real genetic algorithm to detect structural damage using modal properties  

NASA Astrophysics Data System (ADS)

An hybrid real-coded Genetic Algorithm with damage penalization is implemented to locate and quantify structural damage. Genetic Algorithms provide a powerful tool to solved optimization problems. With an appropriate selection of their operators and parameters they can potentially explore the entire solution space and reach the global optimum. Here, the set-up of the Genetic Algorithm operators and parameters is addressed, providing guidelines to their selection in similar damage detection problems. The performance of five fundamental functions based on modal data is studied. In addition, this paper proposes the use of a damage penalization that satisfactorily avoids false damage detection due to experimental noise or numerical errors. A tridimensional space frame structure with single and multiple damages scenarios provides an experimental framework which verifies the approach. The method is tested with different levels of incompleteness in the measured degrees of freedom. The results show that this approach reaches a much more precise solution than conventional optimization methods. A scenario of three simultaneous damage locations was correctly located and quantified by measuring only a 6.3% of the total degrees of freedom.

Meruane, V.; Heylen, W.

2011-07-01

396

Evaluation of hybrid optimization methods for the optimal design of heat integrated distillation sequences  

Microsoft Academic Search

Optimal process design often requires the solution of mixed integer non-linear programming problems. Optimization procedures must be robust and efficient if they are to be incorporated in automated design systems. For heat integrated separation process design, a natural hybrid evolutionary\\/local search method with these properties is possible. The method is based on the use of local search methods for the

E. S. Fraga

2003-01-01

397

An Evolutionary Algorithm with Spatially Distributed Surrogates for Multiobjective Optimization  

Microsoft Academic Search

In this paper, an evolutionary algorithm with spatially distributed surrogates (EASDS) for multiobjective optimization is\\u000a presented. The algorithm performs actual analysis for the initial population and periodically every few generations. An external\\u000a archive of the unique solutions evaluated using the actual analysis is maintained to train the surrogate models. The data\\u000a points in the archive are split into multiple partitions

Amitay Isaacs; Tapabrata Ray; Warren Smith

2007-01-01

398

An optimal on-line algorithm for metrical task system  

Microsoft Academic Search

In practice, almost all dynamic systems require decisions to be made on-line, without full knowledge of their future impact on the system. A general model for the processing of sequences of tasks is introduced, and a general on-line decision algorithm is developed. It is shown that, for an important class of special cases, this algorithm is optimal among all on-line

Allan Borodin; Nathan Linial; Michael E. Saks

1992-01-01

399

Three Parallel Algorithms for Solving Nonlinear Systems and Optimization Problems  

Microsoft Academic Search

\\u000a In this work we describe three sequential algorithms and their parallel counterparts for solving nonlinear systems, when the\\u000a Jacobian matrix is symmetric and positive definite. This case appears frequently in unconstrained optimization problems. Two\\u000a of the three algorithms are based on Newton’s method. The first solves the inner iteration with Cholesky decomposition while\\u000a the second is based on the inexact

Jesús Peinado; Antonio M. Vidal

2004-01-01

400

Prediction of China's coal production-environmental pollution based on a hybrid genetic algorithm-system dynamics model  

Microsoft Academic Search

This paper proposes a hybrid model based on genetic algorithm (GA) and system dynamics (SD) for coal production–environmental pollution load in China. GA has been utilized in the optimization of the parameters of the SD model to reduce implementation subjectivity. The chain of “Economic development–coal demand–coal production–environmental pollution load†of China in 2030 was predicted, and scenarios were analyzed. Results

Shiwei Yu; Yi-ming Wei

2012-01-01

401

A Global Optimization Algorithm for Nonconvex Generalized Disjunctive Programming and Applications to Process Systems  

E-print Network

. Keywords: Nonconvex GDP, nonconvex MINLP, convex hull relaxation, branch and bound, global optimization1 A Global Optimization Algorithm for Nonconvex Generalized Disjunctive Programming Carnegie Mellon University Pittsburgh, PA 15213 Abstract A global optimization algorithm for nonconvex

Grossmann, Ignacio E.

402

Shape Optimization of Rubber Bushing Using Differential Evolution Algorithm  

PubMed Central

The objective of this study is to design rubber bushing at desired level of stiffness characteristics in order to achieve the ride quality of the vehicle. A differential evolution algorithm based approach is developed to optimize the rubber bushing through integrating a finite element code running in batch mode to compute the objective function values for each generation. Two case studies were given to illustrate the application of proposed approach. Optimum shape parameters of 2D bushing model were determined by shape optimization using differential evolution algorithm. PMID:25276848

2014-01-01

403

[Optimizing algorithm design of piecewise linear classifier for spectra].  

PubMed

Being able to identify pollutant gases quickly and accurately is a basic request of spectroscopic technique for envirment monitoring for spectral classifier. Piecewise linear classifier is simple needs less computational time and approachs nonlinear boundary beautifully. Combining piecewise linear classifier and linear support vector machine which is based on the principle of maximizing margin, an optimizing algorithm for single side piecewise linear classifier was devised. Experimental results indicate that the piecewise linear classifier trained by the optimizing algorithm proposed in this paper can approach nonolinear boundary with fewer super_planes and has higher veracity for classification and recognition. PMID:19271528

Lan, Tian-Ge; Fang, Yong-Hua; Xiong, Wei; Kong, Chao; Li, Da-Cheng; Dong, Da-Ming

2008-11-01

404

Particle Swarm Optimization and Varying Chemotactic Step-Size Bacterial Foraging Optimization Algorithms Based Dynamic Economic Dispatch with Non-smooth Fuel Cost Functions  

NASA Astrophysics Data System (ADS)

The Dynamic economic dispatch (DED) problem is an optimization problem with an objective to determine the optimal combination of power outputs for all generating units over a certain period of time in order to minimize the total fuel cost while satisfying dynamic operational constraints and load demand in each interval. Recently social foraging behavior of Escherichia coli bacteria has been explored to develop a novel algorithm for distributed optimization and control. The Bacterial Foraging Optimization Algorithm (BFOA) is currently gaining popularity in the community of researchers, for its effectiveness in solving certain difficult real-world optimization problems. This article comes up with a hybrid approach involving Particle Swarm Optimization (PSO) and BFO algorithms with varying chemo tactic step size for solving the DED problem of generating units considering valve-point effects. The proposed hybrid algorithm has been extensively compared with those methods reported in the literature. The new method is shown to be statistically significantly better on two test systems consisting of five and ten generating units.

Praveena, P.; Vaisakh, K.; Rama Mohana Rao, S.

405

Benchmarking derivative-free optimization algorithms.  

SciTech Connect

We propose data profiles as a tool for analyzing the performance of derivative-free optimization solvers when there are constraints on the computational budget. We use performance and data profiles, together with a convergence test that measures the decrease in function value, to analyze the performance of three solvers on sets of smooth, noisy, and piecewise-smooth problems. Our results provide estimates for the performance difference between these solvers, and show that on these problems, the model-based solver tested performs better than the two direct search solvers tested.

More', J. J.; Wild, S. M.; Mathematics and Computer Science; Cornell Univ.

2009-01-01

406

An efficient cuckoo search algorithm for numerical function optimization  

NASA Astrophysics Data System (ADS)

Cuckoo search algorithm which reproduces the breeding strategy of the best known brood parasitic bird, the cuckoos has demonstrated its superiority in obtaining the global solution for numerical optimization problems. However, the involvement of fixed step approach in its exploration and exploitation behavior might slow down the search process considerably. In this regards, an improved cuckoo search algorithm with adaptive step size adjustment is introduced and its feasibility on a variety of benchmarks is validated. The obtained results show that the proposed scheme outperforms the standard cuckoo search algorithm in terms of convergence characteristic while preserving the fascinating features of the original method.

Ong, Pauline; Zainuddin, Zarita

2013-04-01

407

The Guided Improvement Algorithm for Exact, General-Purpose, Many-Objective Combinatorial Optimization  

E-print Network

This paper presents a new general-purpose algorithm for exact solving of combinatorial many-objective optimization problems. We call this new algorithm the guided improvement algorithm. The algorithm is implemented on top ...

Jackson, Daniel

2009-07-03

408

A new efficient optimal path planner for mobile robot based on Invasive Weed Optimization algorithm  

NASA Astrophysics Data System (ADS)

Planning of the shortest/optimal route is essential for efficient operation of autonomous mobile robot or vehicle. In this paper Invasive Weed Optimization (IWO), a new meta-heuristic algorithm, has been implemented for solving the path planning problem of mobile robot in partially or totally unknown environments. This meta-heuristic optimization is based on the colonizing property of weeds. First we have framed an objective function that satisfied the conditions of obstacle avoidance and target seeking behavior of robot in partially or completely unknown environments. Depending upon the value of objective function of each weed in colony, the robot avoids obstacles and proceeds towards destination. The optimal trajectory is generated with this navigational algorithm when robot reaches its destination. The effectiveness, feasibility, and robustness of the proposed algorithm has been demonstrated through series of simulation and experimental results. Finally, it has been found that the developed path planning algorithm can be effectively applied to any kinds of complex situation.

Mohanty, Prases K.; Parhi, Dayal R.

2014-08-01

409

Control optimization, stabilization and computer algorithms for aircraft applications  

NASA Technical Reports Server (NTRS)

Research related to reliable aircraft design is summarized. Topics discussed include systems reliability optimization, failure detection algorithms, analysis of nonlinear filters, design of compensators incorporating time delays, digital compensator design, estimation for systems with echoes, low-order compensator design, descent-phase controller for 4-D navigation, infinite dimensional mathematical programming problems and optimal control problems with constraints, robust compensator design, numerical methods for the Lyapunov equations, and perturbation methods in linear filtering and control.

1975-01-01

410

Global path planning approach based on ant colony optimization algorithm  

Microsoft Academic Search

Ant colony optimization (ACO) algorithm was modified to optimize the global path. In order to simulate the real ant colonies,\\u000a according to the foraging behavior of ant colonies and the characteristic of food, conceptions of neighboring area and smell\\u000a area were presented. The former can ensure the diversity of paths and the latter ensures that each ant can reach the

Zhi-qiang Wen; Zi-xing Cai

2006-01-01

411

A mesh adaptive direct search algorithm for multiobjective optimization  

Microsoft Academic Search

This work studies multiobjective optimization (MOP) of nonsmooth functions subject to general constraints. We first present definitions and optimality conditions as well as some single-objective formulations of MOP, parameterized with respect to some reference point in the space of objective functions. Next, we propose a new algorithm called MultiMads (multiobjective mesh adaptive direct search) for MOP. MultiMads generates an approximation

Charles Audet; Gilles Savard; Walid Zghal

2010-01-01

412

A Hybrid Quantum Search Engine: A Fast Quantum Algorithm for Multiple Matches  

E-print Network

In this paper we will present a quantum algorithm which works very efficiently in case of multiple matches within the search space and in the case of few matches, the algorithm performs classically. This allows us to propose a hybrid quantum search engine that integrates Grover's algorithm and the proposed algorithm here to have general performance better that any pure classical or quantum search algorithm.

Ahmed Younes; Jon Rowe; Julian Miller

2003-11-25

413

Self-Optimization Energy Management Considering Stochastic Influences for a Hybrid  

E-print Network

management, hybrid energy storage system, self-optimization I. INTRODUCTION TODAY'S electric and hybridSelf-Optimization Energy Management Considering Stochastic Influences for a Hybrid Energy Storage- ergy storage. By hybridization of the storage, adding double layer capacitors, the battery can

Paderborn, Universität

414

An algorithm for optimal water resources planning  

E-print Network

. m' Given these values, the problem is to find the maximum expected value of r and the corresponding optimal value of v, i. e. , v*. For v = x where x = s m m m' 2 2 r = ? a(xl pl+ x2 p2+ m +x 1 p +x ) p) 2 2k j=m +b(xp +xp + ? cx +d m k + x... is not present p 3 3p s = s + R, if %3 is present p 3 3p 3p I+I s +s =s pb spb bl b 2 bl b 2 ! bl vb1 b 2 I R P s3 I s 8 S pm 3 2 "4 x3 x2 V4 v3 V 2 F' ure 4. 5. Simplified functional diagram equivalent to that igure xl shown in Figure 4. 4. 3g...

Raju, Indukuri Venkata Satyanarayana

2012-06-07

415

Using genetic algorithm to solve a new multi-period stochastic optimization model  

NASA Astrophysics Data System (ADS)

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.

Zhang, Xin-Li; Zhang, Ke-Cun

2009-09-01

416

Identifying Optimal Inorganic Nanomateirals for Hybrid Solar Cells  

SciTech Connect

As a newly developed photovoltaic technology, organic-inorganic hybrid solar cells have attracted great interest because of the combined advantages from both components. An ideal inorganic acceptor should have a band gap of about 1.5 eV and energy levels of frontier orbitals matching those of the organic polymer in hybrid solar cells. Hybrid density functional calculations are performed to search for optimal inorganic nanomaterials for hybrid solar sells based on poly(3-hexylthiophene) (P3HT). Our results demonstrate that InSb quantum dots or quantum wires can have a band gap of about 1.5 eV and highest occupied molecular orbital level about 0.4 eV lower than P3HT, indicating that they are good candidates for use in hybrid solar cells. In addition, we predict that chalcopyrite MgSnSb{sub 2} quantum wire could be a low-cost material for realizing high-efficiency hybrid solar cells.

Xiang, H.; Wei, S. H.; Gong, X. G.

2009-01-01

417

An efficient hybrid approach for multiobjective optimization of water distribution systems  

NASA Astrophysics Data System (ADS)

efficient hybrid approach for the design of water distribution systems (WDSs) with multiple objectives is described in this paper. The objectives are the minimization of the network cost and maximization of the network resilience. A self-adaptive multiobjective differential evolution (SAMODE) algorithm has been developed, in which control parameters are automatically adapted by means of evolution instead of the presetting of fine-tuned parameter values. In the proposed method, a graph algorithm is first used to decompose a looped WDS into a shortest-distance tree (T) or forest, and chords (?). The original two-objective optimization problem is then approximated by a series of single-objective optimization problems of the T to be solved by nonlinear programming (NLP), thereby providing an approximate Pareto optimal front for the original whole network. Finally, the solutions at the approximate front are used to seed the SAMODE algorithm to find an improved front for the original entire network. The proposed approach is compared with two other conventional full-search optimization methods (the SAMODE algorithm and the NSGA-II) that seed the initial population with purely random solutions based on three case studies: a benchmark network and two real-world networks with multiple demand loading cases. Results show that (i) the proposed NLP-SAMODE method consistently generates better-quality Pareto fronts than the full-search methods with significantly improved efficiency; and (ii) the proposed SAMODE algorithm (no parameter tuning) exhibits better performance than the NSGA-II with calibrated parameter values in efficiently offering optimal fronts.

Zheng, Feifei; Simpson, Angus R.; Zecchin, Aaron C.

2014-05-01

418

Single-objective optimization of thermo-electric coolers using genetic algorithm  

NASA Astrophysics Data System (ADS)

Thermo-electric Coolers (TECs) nowadays is applied in a wide range of thermal energy systems. This is due to its superior features where no refrigerant and dynamic parts are needed. TECs generate no electrical or acoustical noise and are environment friendly. Over the past decades, many researches were employed to improve the efficiency of TECs by enhancing the material parameters and design parameters. The material parameters are restricted by currently available materials and module fabricating technologies. Therefore, the main objective of TECs design is to determine a set of design parameters such as leg area, leg length and the number of legs. Two elements that play an important role when considering the suitability of TECs in applications are rated of refrigeration (ROR) and coefficient of performance (COP). In this paper, the review of some previous researches will be conducted to see the diversity of optimization in the design of TECs in enhancing the performance and efficiency. After that, single objective optimization problems (SOP) will be tested first by using Genetic Algorithm (GA) to optimize geometry properties so that TECs will operate at near optimal conditions. In the future works, multi-objective optimization problems (MOP) using hybrid GA with another optimization technique will be considered to give a better results and compare with previous research such as Non-Dominated Sorting Genetic Algorithm (NSGA-II) to see the advantages and disadvantages.

Khanh, Doan V. K.; Vasant, P.; Elamvazuthi, Irraivan; Dieu, Vo N.

2014-10-01

419

Intelligent Optimization Scheduling Algorithm for Professional Sports Games  

Microsoft Academic Search

The world financial crisis has caused a great impact to human beings’ daily life. The significant evidence is that the oil price has hit, more than, 90 U.S. according to the report of ministry of economic affairs. The price reflects the difficulty not only to transportation but finance status. In this paper, an optimization algorithm concerning the scheduling issues was

Jason C. Hung; Miller K. Chien; Neil Y. Yen

2011-01-01

420

Optimizing Interleaver for Turbo Codes by Genetic Algorithms  

Microsoft Academic Search

Since the appearance in 1993, first approaching the Shannon limit, the Turbo Codes give a new direction for the channel encoding field, especially since they were adopted for multiple norms of telecommunications, such as deeper communication. To obtain an excellent performance it is necessary to design robust turbo code interleaver. We are investigating genetic algorithms as a promising optimization method

P. Kromer; V. Snasel; J. Platos; P. N. Ouddane

2007-01-01

421

GENETIC ALGORITHMS AND OPTIMIZING CHEMICAL OXYGEN-IODINE LASERS  

Microsoft Academic Search

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

David L. Carroll

1996-01-01

422

GLOBAL OPTIMIZATION AND APPROXIMATION ALGORITHMS IN COMPUTER VISION  

E-print Network

GLOBAL OPTIMIZATION AND APPROXIMATION ALGORITHMS IN COMPUTER VISION CARL OLSSON Faculty Vision Abstract Computer Vision is today a wide research area including topics like robot vision, image there has been a rapid development in understanding and modeling different computer vision applications

Lunds Universitet

423

A Clustering Genetic Algorithm for Actuator Optimization in Flow Control  

Microsoft Academic Search

Active flow control can provide a leap in the perform- nace of engineering configurations. Although a number of sensor and actuator configurations have been proposed the task of identifying optimal parameters for control devices is based on engineering intuition usually gathered from un- controlled flow experiments. Here we propose a clustering genetic algorithm that adaptively identifies critical points in the

Michele Milano; Petros Koumoutsakos

2000-01-01

424

Genetic Algorithms Are NOT Function Optimizers Kenneth A. De Jong  

E-print Network

IS AGENETIC ALGORITHM? Figure 1 provides a flow diagram of a fairly generic version of a GA. If we ask what functions. The level of interest and success in this area has led to a number of improvements to GA optimization. However, the motivating context of Holland's initial GA work was the design and implementation

George Mason University

425

Automatic optimal design algorithm for the foundation of tower cranes  

Microsoft Academic Search

As buildings become taller and larger, the lifting plan safety review has become more important in construction project management. However, the cost and safety aspects of the lifting plan are contradictory to each other. Therefore, an optimization algorithm needs to be devised as a solution to this problem. In many cases at construction sites, the selection and stability review of

Sun-Kuk Kim; Jang-Young Kim; Dong-Hoon Lee; Sang-Yeon Ryu

2011-01-01

426

Field-Programmable Gate Array Architectures and Algorithms Optimized  

E-print Network

Circuits Andy Gean Ye November 2004 #12;Field-Programmable Gate Array Architectures and Algorithms Optimized for Implementing Datapath Circuits by Andy Gean Ye A thesis submitted in conformity of Electrical and Computer Engineering University of Toronto Toronto, Ontario, Canada © Copyright by Andy Gean

Ye, Andy G.

427

A tutorial on optimization techniques applied to DSM algorithms  

NASA Astrophysics Data System (ADS)

xDSL systems are widely used nowadays. Services such as VDSL2 can achieve high bitrates over copper wires. The usage of dynamic spectrum management techniques (DSM) can result in even better bitrates, through mitigation of crosstalk, the worst interference in such systems. This tutorial surveys the recent progress in DSM, covering the main algorithms and optimization concepts used by then.

Neves, Darlene Maciel; Klautau, Aldebaro Barreto da, Jr.; Conte, Marcio Murilo; Medeiros, Eduardo Lins de; Reis, Jacklyn Dias; Dortschy, Boris

2007-09-01

428

Exploring and Optimizing Dynamic Neural Fields Parameters Using Genetic Algorithms  

E-print Network

of the characteristics needed to adopt complex goal-oriented behaviors. Bubbles can indeed shape the attractor landscape: they either lead to the saturation of the field, the lack of any coherent activity, or the selfExploring and Optimizing Dynamic Neural Fields Parameters Using Genetic Algorithms Jean

Boyer, Edmond

429

The Cache Performance and Optimizations of Blocked Algorithms  

Microsoft Academic Search

Blocking is a well-known optimization technique for improving the effectiveness of memory hierarchies. Instead of operating on entire rows or columns of an array, blocked algorithms operate on submatrices or blocks, so that data loaded into the faster levels of the memory hierarchy are reused. This paper presents cache performance data for blocked programs and evaluates several op- timizations to

Monica S. Lam; Edward E. Rothberg; Michael E. Wolf

1991-01-01

430

Application of genetic algorithms in resource constrained network optimization  

Microsoft Academic Search

There are limited solution techniques available for resource constrained project scheduling problems with stochastic task durations. Due to computational complexity, scheduling heuristics have been found useful for large deterministic problems. In this paper, the authors demonstrate the use of a genetic algorithm to optimize over a linear combination of scheduling heuristics. A simulation model is used to evaluate the performance

J. Pet-Edwards; M. Mollaghasemi

1995-01-01

431

Evolution-based decision tree optimization using cultural algorithms  

Microsoft Academic Search

Recently decision trees have been used in data mining application to extract new concepts. While current decision tree algorithms exhibit many improvements over earlier versions, there are still problems with the generation of optimal trees in situations that use attributes that vary widely in their possible outcomes. Quinlan's gain-ratio measure has been needed to reduce the bias towards variables with

Hasan A Al-Shehri

1997-01-01

432

Harmonic optimization of multilevel converters using genetic algorithms  

Microsoft Academic Search

In this paper, a genetic algorithm (GA) optimization technique is applied to multilevel inverter to determine optimum switching angles for cascaded multilevel inverters for eliminating some higher order harmonics while maintaining the required fundamental voltage. This technique can be applied to multilevel inverters with any number of levels; as an example in this paper, a 7-level inverter is considered, and

B. Ozpineci; L. M. Tolbert; J. N. Chiasson

2004-01-01

433

EXPERIMENTAL ANALYSIS OF LOCAL SEARCH ALGORITHMS FOR OPTIMAL BASE STATION  

E-print Network

Dominating Set (MDS) problem [1]. Local search techniques 1 , such as Genetic Algo­ 1 There is some confusion unit distance apart. From this set of 100,000 possible points, 51 locations where base transmittingEXPERIMENTAL ANALYSIS OF LOCAL SEARCH ALGORITHMS FOR OPTIMAL BASE STATION LOCATION Bhaskar

Krishnamachari, Bhaskar

434

Matlab coding standard for Stochastic optimization algorithms, FFR105  

E-print Network

Matlab coding standard for Stochastic optimization algorithms, FFR105 v 1.1, 2009-02-02, v 1 clear and highly readable code you reduce the risk of introducing unwanted errors. It is the aim, you should use Matlab, and you should follow the code standard described be- low. Programs

Wolff, Krister

435

Wind Turbine Tower Optimization Method Using a Genetic Algorithm  

Microsoft Academic Search

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

Shigeo Yoshida

2006-01-01

436

E cient Approximation and Optimization Algorithms for Computational Metrology  

E-print Network

E cient Approximation and Optimization Algorithms for Computational Metrology Christian A. Duncan in computational metrology, focusing on the fun- damental issues of \\ atness" and \\roundness." Speci c- ally, we-dimensional point set, which corresponds to the metrology notion of \\ atness," giv- ing an approximation method

Goodrich, Michael T.

437

A new filled function algorithm for constrained global optimization problems  

Microsoft Academic Search

A new filled function with one parameter is proposed for solving constrained global optimization problems without the coercive condition, in which the filled function contains neither exponential term nor fractional term and is easy to be calculated. A corresponding filled function algorithm is established based on analysis of the properties of the filled function. At last, we perform numerical experiments

Suxiang He; Weilai Chen; Hui Wang

2011-01-01

438

An optimal algorithm for scheduling checkpoints with variable costs  

E-print Network

An optimal algorithm for scheduling checkpoints with variable costs Mohamed-Slim Bouguerra, Denis are then used to restart computations from the last checkpoint. This last approach called checkpointing is one of the most popular fault tolerance technique in parallel systems. 1.2 Brief review of related works Young

Boyer, Edmond

439

Fast Optimal Load Balancing Algorithms for 1D Partitioning  

SciTech Connect

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.

Pinar, Ali; Aykanat, Cevdet

2002-12-09

440

Propeller performance analysis and multidisciplinary optimization using a genetic algorithm  

NASA Astrophysics Data System (ADS)

A propeller performance analysis program has been developed and integrated into a Genetic Algorithm for design optimization. The design tool will produce optimal propeller geometries for a given goal, which includes performance and/or acoustic signature. A vortex lattice model is used for the propeller performance analysis and a subsonic compact source model is used for the acoustic signature determination. Compressibility effects are taken into account with the implementation of Prandtl-Glauert domain stretching. Viscous effects are considered with a simple Reynolds number based model to account for the effects of viscosity in the spanwise direction. An empirical flow separation model developed from experimental lift and drag coefficient data of a NACA 0012 airfoil is included. The propeller geometry is generated using a recently introduced Class/Shape function methodology to allow for efficient use of a wide design space. Optimizing the angle of attack, the chord, the sweep and the local airfoil sections, produced blades with favorable tradeoffs between single and multiple point optimizations of propeller performance and acoustic noise signatures. Optimizations using a binary encoded IMPROVE(c) Genetic Algorithm (GA) and a real encoded GA were obtained after optimization runs with some premature convergence. The newly developed real encoded GA was used to obtain the majority of the results which produced generally better convergence characteristics when compared to the binary encoded GA. The optimization trade-offs show that single point optimized propellers have favorable performance, but circulation distributions were less smooth when compared to dual point or multiobjective optimizations. Some of the single point optimizations generated propellers with proplets which show a loading shift to the blade tip region. When noise is included into the objective functions some propellers indicate a circulation shift to the inboard sections of the propeller as well as a reduction in propeller diameter. In addition the propeller number was increased in some optimizations to reduce the acoustic blade signature.

Burger, Christoph

441

Genetic algorithms for optimal design of underground reinforced concrete tube structure  

Microsoft Academic Search

Applying genetic algorithms to optimal design of underground reinforced concrete tube structure, the author develops the optimal model of structural design based on genetic algorithms for underground reinforced concrete tube. An example of the reinforced concrete tube structure is calculated by the proposed computer programs based on genetic algorithms optimal model, and the result indicates that using genetic algorithms for

Sheng-Li Zhao; Min-Qiang Li; Ji-Song Kou; Yan Liu

2004-01-01

442

Research reactor loading pattern optimization using estimation of distribution algorithms  

SciTech Connect

A new evolutionary search based approach for solving the nuclear reactor loading pattern optimization problems is presented based on the Estimation of Distribution Algorithms. The optimization technique developed is then applied to the maximization of the effective multiplication factor (K{sub eff}) of the Imperial College CONSORT research reactor (the last remaining civilian research reactor in the United Kingdom). A new elitism-guided searching strategy has been developed and applied to improve the local convergence together with some problem-dependent information based on the 'stand-alone K{sub eff} with fuel coupling calculations. A comparison study between the EDAs and a Genetic Algorithm with Heuristic Tie Breaking Crossover operator has shown that the new algorithm is efficient and robust. (authors)

Jiang, S. [Dept. of Earth Science and Engineering, Applied Modeling and Computation Group AMCG, Imperial College, London, SW7 2AZ (United Kingdom); Ziver, K. [Dept. of Earth Science and Engineering, Applied Modeling and Computation Group AMCG, Imperial College, London, SW7 2AZ (United Kingdom); AMCG Group, RM Consultants, Abingdon (United Kingdom); Carter, J. N.; Pain, C. C.; Eaton, M. D.; Goddard, A. J. H. [Dept. of Earth Science and Engineering, Applied Modeling and Computation Group AMCG, Imperial College, London, SW7 2AZ (United Kingdom); Franklin, S. J.; Phillips, H. J. [Imperial College, Reactor Centre, Silwood Park, Buckhurst Road, Ascot, Berkshire, SL5 7TE (United Kingdom)

2006-07-01

443

Recursive hybrid algorithm for non-linear system identification using radial basis function networks  

Microsoft Academic Search

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

S. CHEN; S. A. BILLINGS; P. M. GRANT

1992-01-01

444

A novel hybrid discrete differential evolution algorithm for blocking flow shop scheduling problems  

Microsoft Academic Search

This paper proposes a novel hybrid discrete differential evolution (HDDE) algorithm for solving blocking flow shop scheduling problems to minimize the maximum completion time (i.e. makespan). Firstly, in the algorithm, the individuals are represented as discrete job permutations, and new mutation and crossover operators are developed for this representation, so that the algorithm can directly work in the discrete domain.

Ling Wang; Quan-Ke Pan; Ponnuthurai N. Suganthan; Wen-Hong Wang; Ya-Min Wang

2010-01-01

445

Optimal design of membrane-hybrid systems for waste reduction  

SciTech Connect

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)

1993-01-01

446

A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm  

Microsoft Academic Search

Swarm intelligence is a research branch that models the population of interacting agents or swarms that are able to self-organize.\\u000a An ant colony, a flock of birds or an immune system is a typical example of a swarm system. Bees’ swarming around their hive\\u000a is another example of swarm intelligence. Artificial Bee Colony (ABC) Algorithm is an optimization algorithm based

Dervis Karaboga; Bahriye Basturk

2007-01-01

447

Aeroelastic tailoring using piezoelectric actuation and hybrid optimization  

NASA Astrophysics Data System (ADS)

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 hybrid optimization 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

1999-02-01

448

Aeroelastic tailoring using piezoelectric actuation and hybrid optimization  

NASA Astrophysics Data System (ADS)

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 hybrid optimization procedure is applied to the design of an airplane wing, 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

1998-07-01

449

Global structual optimizations of surface systems with a genetic algorithm  

SciTech Connect

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.

Chuang, Feng-Chuan

2005-05-01

450

Multiobjective Optimization of Rocket Engine Pumps Using Evolutionary Algorithm  

NASA Technical Reports Server (NTRS)

A design optimization method for turbopumps of cryogenic rocket engines has been developed. Multiobjective Evolutionary Algorithm (MOEA) is used for multiobjective pump design optimizations. Performances of design candidates are evaluated by using the meanline pump flow modeling method based on the Euler turbine equation coupled with empirical correlations for rotor efficiency. To demonstrate the feasibility of the present approach, a single stage centrifugal pump design and multistage pump design optimizations are presented. In both cases, the present method obtains very reasonable Pareto-optimal solutions that include some designs outperforming the original design in total head while reducing input power by one percent. Detailed observation of the design results also reveals some important design criteria for turbopumps in cryogenic rocket engines. These results demonstrate the feasibility of the EA-based design optimization method in this field.

Oyama, Akira; Liou, Meng-Sing

2001-01-01

451

Design of underwater robot lines based on a hybrid automatic optimization strategy  

NASA Astrophysics Data System (ADS)

In this paper, a hybrid automatic optimization strategy is proposed for the design of underwater robot lines. Isight is introduced as an integration platform. The construction of this platform is based on the user programming and several commercial software including UG6.0, GAMBIT2.4.6 and FLUENT12.0. An intelligent parameter optimization method, the particle swarm optimization, is incorporated into the platform. To verify the strategy proposed, a simulation is conducted on the underwater robot model 5470, which originates from the DTRC SUBOFF project. With the automatic optimization platform, the minimal resistance is taken as the optimization goal; the wet surface area as the constraint condition; the length of the fore-body, maximum body radius and after-body's minimum radius as the design variables. With the CFD calculation, the RANS equations and the standard turbulence model are used for direct numerical simulation. By analyses of the simulation results, it is concluded that the platform is of high efficiency and feasibility. Through the platform, a variety of schemes for the design of the lines are generated and the optimal solution is achieved. The combination of the intelligent optimization algorithm and the numerical simulation ensures a global optimal solution and improves the efficiency of the searching solutions.

Lyu, Wenjing; Luo, Weilin

2014-09-01

452

Using genetic algorithms to search for an optimal investment strategy  

NASA Astrophysics Data System (ADS)

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.

Mandere, Edward; Xi, Haowen

2007-10-01

453

Optimization of solar air collector using genetic algorithm and artificial bee colony algorithm  

NASA Astrophysics Data System (ADS)

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.

?encan ?ahin, Arzu

2012-11-01

454

Hybrid Genetic Algorithm - Local Search Method for Ground-Water Management  

NASA Astrophysics Data System (ADS)

Ground-water management problems commonly are formulated as a mixed-integer, non-linear programming problem (MINLP). Relying only on conventional gradient-search methods to solve the management problem is computationally fast; however, the methods may become trapped in a local optimum. Global-optimization schemes can identify the global optimum, but the convergence is very slow when the optimal solution approaches the global optimum. In this study, we developed a hybrid optimization scheme, which includes a genetic algorithm and a gradient-search method, to solve the MINLP. The genetic algorithm identifies a near- optimal solution, and the gradient search uses the near optimum to identify the global optimum. Our methodology is applied to a conjunctive-use project in the Warren ground-water basin, California. Hi- Desert Water District (HDWD), the primary water-manager in the basin, plans to construct a wastewater treatment plant to reduce future septic-tank effluent from reaching the ground-water system. The treated wastewater instead will recharge the ground-water basin via percolation ponds as part of a larger conjunctive-use strategy, subject to State regulations (e.g. minimum distances and travel times). HDWD wishes to identify the least-cost conjunctive-use strategies that control ground-water levels, meet regulations, and identify new production-well locations. As formulated, the MINLP objective is to minimize water-delivery costs subject to constraints including pump capacities, available recharge water, water-supply demand, water-level constraints, and potential new-well locations. The methodology was demonstrated by an enumerative search of the entire feasible solution and comparing the optimum solution with results from the branch-and-bound algorithm. The results also indicate that the hybrid method identifies the global optimum within an affordable computation time. Sensitivity analyses, which include testing different recharge-rate scenarios, pond layouts, and water-supply constraints, indicate that the number of new wells is insensitive to water-supply constraints; however, pumping rates and patterns of the existing wells are sensitive. The locations of new wells are mildly sensitive to the pond layout.

Chiu, Y.; Nishikawa, T.; Martin, P.

2008-12-01

455

Optimization of image processing algorithms on mobile platforms  

NASA Astrophysics Data System (ADS)

This work presents a technique to optimize popular image processing algorithms on mobile platforms such as cell phones, net-books and personal digital assistants (PDAs). The increasing demand for video applications like context-aware computing on mobile embedded systems requires the use of computationally intensive image processing algorithms. The system engineer has a mandate to optimize them so as to meet real-time deadlines. A methodology to take advantage of the asymmetric dual-core processor, which includes an ARM and a DSP core supported by shared memory, is presented with implementation details. The target platform chosen is the popular OMAP 3530 processor for embedded media systems. It has an asymmetric dual-core architecture with an ARM Cortex-A8 and a TMS320C64x Digital Signal Processor (DSP). The development platform was the BeagleBoard with 256 MB of NAND RAM and 256 MB SDRAM memory. The basic image correlation algorithm is chosen for benchmarking as it finds widespread application for various template matching tasks such as face-recognition. The basic algorithm prototypes conform to OpenCV, a popular computer vision library. OpenCV algorithms can be easily ported to the ARM core which runs a popular operating system such as Linux or Windows CE. However, the DSP is architecturally more efficient at handling DFT algorithms. The algorithms are tested on a variety of images and performance results are presented measuring the speedup obtained due to dual-core implementation. A major advantage of this approach is that it allows the ARM processor to perform important real-time tasks, while the DSP addresses performance-hungry algorithms.

Poudel, Pramod; Shirvaikar, Mukul

2011-03-01

456

Harmonic Optimization of Multilevel Converters Using Genetic Algorithms Abstract--In this paper, a genetic algorithm (GA) optimization  

E-print Network

, a genetic algorithm (GA) optimization technique is applied to multilevel inverter to determine optimum switching angles for cascaded multilevel inverters for eliminating some higher order harmonics while maintaining the required fundamental voltage. This technique can be applied to multilevel inverters with any

Tolbert, Leon M.

457

An optimal quantum algorithm for the oracle identification problem  

E-print Network

In the oracle identification problem, we are given oracle access to an unknown N-bit string x promised to belong to a known set C of size M and our task is to identify x. We present a quantum algorithm for the problem that is optimal in its dependence on N and M. Our algorithm considerably simplifies and improves the previous best algorithm due to Ambainis et al. Our algorithm also has applications in quantum learning theory, where it improves the complexity of exact learning with membership queries, resolving a conjecture of Hunziker et al. The algorithm is based on ideas from classical learning theory and a new composition theorem for solutions of the filtered $\\gamma_2$-norm semidefinite program, which characterizes quantum query complexity. Our composition theorem is quite general and allows us to compose quantum algorithms with input-dependent query complexities without incurring a logarithmic overhead for error reduction. As an application of the composition theorem, we remove all log factors from the best known quantum algorithm for Boolean matrix multiplication.

Robin Kothari

2013-11-29

458

Multidisciplinary Multiobjective Optimal Design for Turbomachinery Using Evolutionary Algorithm  

NASA Technical Reports Server (NTRS)

This report summarizes Dr. Lian s efforts toward developing a robust and efficient tool for multidisciplinary and multi-objective optimal design for turbomachinery using evolutionary algorithms. This work consisted of two stages. The first stage (from July 2003 to June 2004) Dr. Lian focused on building essential capabilities required for the project. More specifically, Dr. Lian worked on two subjects: an enhanced genetic algorithm (GA) and an integrated optimization system with a GA and a surrogate model. The second stage (from July 2004 to February 2005) Dr. Lian formulated aerodynamic optimization and structural optimization into a multi-objective optimization problem and performed multidisciplinary and multi-objective optimizations on a transonic compressor blade based on the proposed model. Dr. Lian s numerical results showed that the proposed approach can effectively reduce the blade weight and increase the stage pressure ratio in an efficient manner. In addition, the new design was structurally safer than the original design. Five conference papers and three journal papers were published on this topic by Dr. Lian.

2005-01-01

459

Dynamic learning rate optimization of the backpropagation algorithm.  

PubMed

It has been observed by many authors that the backpropagation (BP) error surfaces usually consist of a large amount of flat regions as well as extremely steep regions. As such, the BP algorithm with a fixed learning rate will have low efficiency. This paper considers dynamic learning rate optimization of the BP algorithm using derivative information. An efficient method of deriving the first and second derivatives of the objective function with respect to the learning rate is explored, which does not involve explicit calculation of second-order derivatives in weight space, but rather uses the information gathered from the forward and backward propagation, Several learning rate optimization approaches are subsequently established based on linear expansion of the actual outputs and line searches with acceptable descent value and Newton-like methods, respectively. Simultaneous determination of the optimal learning rate and momentum is also introduced by showing the equivalence between the momentum version BP and the conjugate gradient method. Since these approaches are constructed by simple manipulations of the obtained derivatives, the computational and storage burden scale with the network size exactly like the standard BP algorithm, and the convergence of the BP algorithm is accelerated with in a remarkable reduction (typically by factor 10 to 50, depending upon network architectures and applications) in the running time for the overall learning process. Numerous computer simulation results are provided to support the present approaches. PMID:18263352

Yu, X H; Chen, G A; Cheng, S X

1995-01-01

460

Optimization of a CNG series hybrid concept vehicle  

SciTech Connect

Compressed Natural Gas (CNG) has favorable characteristics as a vehicular fuel, in terms of fuel economy as well as emissions. Using CNG as a fuel in a series hybrid vehicle has the potential of resulting in very high fuel economy (between 26 and 30 km/liter, 60 to 70 mpg) and very low emissions (substantially lower than Federal Tier II or CARB ULEV). This paper uses a vehicle evaluation code and an optimizer to find a set of vehicle parameters that result in optimum vehicle fuel economy. The vehicle evaluation code used in this analysis estimates vehicle power performance, including engine efficiency and power, generator efficiency, energy storage device efficiency and state-of-charge, and motor and transmission efficiencies. Eight vehicle parameters are selected as free variables for the optimization. The optimum vehicle must also meet two perfect requirements: accelerate to 97 km/h in less than 10 s, and climb an infinitely long hill with a 6% slope at 97 km/h with a 272 kg (600 lb.) payload. The optimizer used in this work was originally developed in the magnetic fusion energy program, and has been used to optimize complex systems, such as magnetic and inertial fusion devices, neutron sources, and mil guns. The optimizer consists of two parts: an optimization package for minimizing non-linear functions of many variables subject to several non-linear equality and/or inequality constraints and a programmable shell that allows interactive configuration and execution of the optimizer. The results of the analysis indicate that the CNG series hybrid vehicle has a high efficiency and low emissions. These results emphasize the advantages of CNG as a near-term alternative fuel for vehicles.

Aceves, S.M.; Smith, J.R.; Perkins, L.J.; Haney, S.W.; Flowers, D.L.

1995-09-22

461

Leveraging off genetic algorithms for optimizing AGRIN lenses  

NASA Astrophysics Data System (ADS)

While researching various gradient index glass families for superb color correction using ZEMAX1 optical design program, the authors found that certain solutions could only be found using the Hammer routine2. Hammer is a genetic algorithm that breeds a particular lens configuration with variations of itself3. It is not intended to be a global search routine. Hammer is typically used after the best performance is obtained using the standard damped least squares (DLS) algorithm with the default merit function (MF) based on minimizing root mean square (RMS) spot size. Upon this discovery, the authors proceeded to explore the benefit of using the genetic Hammer algorithm on three different lens systems. To make the solution space more complicated, two axial gradient index (AGRIN) elements were used in each lens type; a bi- AGRIN cemented doublet; a bi-AGRIN air spaced triplet with CaF2 as the center element, and a double Gauss with four AGRIN elements and two CaF2 elements. AGRIN elements were used in each lens to provide a more complex solution space and to make optimization more difficult. After optimization, the performance of each lens was compared wiht the conventionally optimized counterpart using the default MF with a DLS algorithm. After this comparison was made, another trade study was done between the Hammer and DLS algorithms, but in this case, the optimization used a custom MF instead of the default MF. The authors believe this study shows the importance of MF construction over that of using the default RMS spot size metric. A significant improvement was obtained for all lenses with the default MF using the Hammer over the DLS technique, but that improvement was less obvious when a custom MF was used.

Manhart, Paul K.; Sparrold, Scott W.

2000-10-01

462

Facial skin segmentation using bacterial foraging optimization algorithm.  

PubMed

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%). PMID:23724370

Bakhshali, Mohamad Amin; Shamsi, Mousa

2012-10-01

463

Hierarchical artificial bee colony algorithm for RFID network planning optimization.  

PubMed

This paper presents a novel optimization algorithm, namely, hierarchical artificial bee colony optimization, called HABC, to tackle the radio frequency identification network planning (RNP) problem. In the proposed multilevel model, the higher-level species can be aggregated by the subpopulations from lower level. In the bottom level, each subpopulation employing the canonical ABC method searches the part-dimensional optimum in parallel, which can be constructed into a complete solution for the upper level. At the same time, the comprehensive learning method with crossover and mutation operators is applied to enhance the global search ability between species. Experiments are conducted on a set of 10 benchmark optimization problems. The results demonstrate that the proposed HABC obtains remarkable performance on most chosen benchmark functions when compared to several successful swarm intelligence and evolutionary algorithms. Then HABC is used for solving the real-world RNP problem on two instances with different scales. Simulation results show that the proposed algorithm is superior for solving RNP, in terms of optimization accuracy and computation robustness. PMID:24592200

Ma, Lianbo; Chen, Hanning; Hu, Kunyuan; Zhu, Yunlong

2014-01-01

464

Optimization of an Antenna Array Using Genetic Algorithms  

NASA Astrophysics Data System (ADS)

An array of antennas is usually used in long distance communication. The observation of celestial objects necessitates a large array of antennas, such as the Giant Metrewave Radio Telescope (GMRT). Optimizing this kind of array is very important when observing a high performance system. The genetic algorithm (GA) is an optimization solution for these kinds of problems that reconfigures the position of antennas to increase the u-v coverage plane or decrease the sidelobe levels (SLLs). This paper presents how to optimize a correlator antenna array using the GA. A brief explanation about the GA and operators used in this paper (mutation and crossover) is provided. Then, the results of optimization are discussed. The results show that the GA provides efficient and optimum solutions among a pool of candidate solutions in order to achieve the desired array performance for the purposes of radio astronomy. The proposed algorithm is able to distribute the u-v plane more efficiently than GMRT with a more than 95% distribution ratio at snapshot, and to fill the u-v plane from a 20% to more than 68% filling ratio as the number of generations increases in the hour tracking observations. Finally, the algorithm is able to reduce the SLL to -21.75 dB.

Kiehbadroudinezhad, Shahideh; Kamariah Noordin, Nor; Sali, A.; Zainal Abidin, Zamri

2014-06-01

465

Hybrid Nested Partitions algorithm for scheduling in job shop problem  

Microsoft Academic Search

This paper introduces the main idea of Nested Partitions algorithm, and applied it to solve the job shop scheduling problem. In the algorithm the job shop scheduling problem is considered as a partition tree. The algorithm partitions the feasible region and concentrates the sampling effort in those subsets of feasible regions that are considered the most promising. Genetic algorithm search

Wei Wu; Junhu Wei; Xiaohong Guan

2009-01-01

466

Fuel management optimization using genetic algorithms and code independence  

SciTech Connect

Fuel management optimization is a hard problem for traditional optimization techniques. Loading pattern optimization is a large combinatorial problem without analytical derivative information. Therefore, methods designed for continuous functions, such as linear programming, do not always work well. Genetic algorithms (GAs)