Details for NCL-TR-2006007
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NameNCL-TR-2006007
Description
Particle Swarm Guided Evolution Strategy for Real-Parameter Optimization
Hsieh, Chang-Tai
Abstract: Evolutionary algorithms are stochastic search methods that mimic the metaphor of natural biological evolution. Among of these algorithms, evolution strategy (ES) and particle swarm optimization (PSO) are two of the most popular research topics. Both of ES and PSO are deal with real-parameter optimization problems but have different search behaviors. By observing the search behavior of ES and PSO, we find both of them have strength and weakness. In ES, the mutation operator lacks an explicit mechanism to guide the search into promising direction due to random variance. Moreover, there is no coordination in the movement of individuals within the search space. However, the powerful selection procedure allows solutions with superior characteristics to pass these from generation to generation. In the PSO, the search mechanism used the swarm cooperation concept. Each particle will move toward the direction which is expected to be good. The objective of this article is tried to combine ES and PSO at the concept level. We introduce the concept of swarm cooperation of PSO into the mutation operator of ES for reducing the disturbance of mutation in the mutation direction. We proposed a new mutation operator called guided mutation. Combining the guided mutation into the traditional ES framework and develop a new optimization algorithm called particle swarm guided evolution strategy. Numerical experiments are conducted on a set of carefully designed benchmark functions and demonstrate good performance achieved by the proposed methodology.
FilenameNCL-TR-2006007.pdf
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Created On: 07/23/2006 01:46
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