Particle swarm optimization algorithm based on dynamic memory strategy
Pages 55 - 60
Abstract
This paper mainly studies the influence of memory on individual performance in particle swarm system. Based on the observation of social phenomenon from the perspective of social psychology, the concept of individual memory contribution is defined and several measurement methods to determine the level of effect of individual memory on its behavior are discussed. A dynamic memory particle swarm optimization algorithm is implemented by dynamically assigning appropriate weight to each individual's memory according to the selected metrics values. Numerical experiment results on benchmark optimization function set show that the proposed scheme can effectively adjust the weight of individual memory according to different optimization problems adaptively. Numerical results also demonstrate that dynamic memory is an effective improvement strategy for preventing premature convergence in particle swarm optimization algorithm.
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- Particle swarm optimization algorithm based on dynamic memory strategy
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Published In
June 2009
1112 pages
ISBN:9781605583266
DOI:10.1145/1543834
- General Chairs:
- Lihong Xu,
- Erik D. Goodman,
- Program Chairs:
- Guoliang Chen,
- Darrell Whitley,
- Yongsheng Ding
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Published: 12 June 2009
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GEC '09: GEC '09 - ACM/SIGEVO Genetic and Evolutionary Computation Summit
June 12 - 14, 2009
Shanghai, China
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