| Identification of strategy parameters for particle swarm optimizer through Taguchi method |
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| 作者姓名： | KHOSLA Arun KUMAR Shakti AGGARWAL K.K. |
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| 作者单位： | Department of Electronics and Communication Engineering National Institute of Technology Jalandhar 144011 India，Centre for Advanced Technology Haryana Engineering College Jagadhari 135003 India，Vice Chancellor GGS Indraprastha University Delhi 110006 India |
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| 摘 要： | INTRODUCTION The particle swarm optimization (PSO) method is a member of the broad category of swarm intelli- gence techniques for finding optimized solutions. The PSO algorithm is based on the social behavior of animals such as flocking of birds and schooling of fish, etc. PSO has its origin in simulation for visual- izing the synchronized choreography of bird flock by incorporating concepts such as nearest-neighbor ve- locity matching and acceleration by distance (Par- sopoulos and V…
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| 关 键 词： | 策略参数 微粒群优化 PSO Taguchi法 ANOVA |
| 收稿时间： | 2005-11-23 |
| 修稿时间： | 2006-02-26 |
Identification of strategy parameters for particle swarm optimizer through Taguchi method |
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| KHOSLA Arun KUMAR Shakti AGGARWAL K.K..Identification of strategy parameters for particle swarm optimizer through Taguchi method[J].Journal of Zhejiang University Science,2006,7(12):1989-1994. |
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| Authors: | Arun Khosla Shakti Kumar KK Aggarwal |
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| Institution: | (1) Department of Electronics and Communication Engineering, National Institute of Technology, Jalandhar, 144011, India;(2) Centre for Advanced Technology, Haryana Engineering College, Jagadhari, 135003, India;(3) Vice Chancellor, GGS Indraprastha University, Delhi, 110006, India |
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| Abstract: | Particle swarm optimization (PSO), like other evolutionary algorithms is a population-based stochastic algorithm inspired
from the metaphor of social interaction in birds, insects, wasps, etc. It has been used for finding promising solutions in
complex search space through the interaction of particles in a swarm. It is a well recognized fact that the performance of
evolutionary algorithms to a great extent depends on the choice of appropriate strategy/operating parameters like population
size, crossover rate, mutation rate, crossover operator, etc. Generally, these parameters are selected through hit and trial
process, which is very unsystematic and requires rigorous experimentation. This paper proposes a systematic based on Taguchi
method reasoning scheme for rapidly identifying the strategy parameters for the PSO algorithm. The Taguchi method is a robust
design approach using fractional factorial design to study a large number of parameters with small number of experiments.
Computer simulations have been performed on two benchmark functions—Rosenbrock function and Griewank function—to validate
the approach.
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| Keywords: | Strategy parameters Particle swarm optimization (PSO) Taguchi method ANOVA |
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