Particle Swarm Optimization with Comprehensive Learning & Self-adaptive Mutation
- Hao Tan, Jianjun Li, Jing Huang
- Corresponding Author
- Hao Tan
Available Online March 2015.
- https://doi.org/10.2991/iset-15.2015.20How to use a DOI?
- Particle swarm optimization, Adaptive mutation, Weight, Learning factors, Convergence
- As a representative method of swarm intelligence, Particle Swarm Optimization (PSO) is an algorithm for searching the global optimum in the complex space through cooperation and competition among the individuals in a population of particle. But the basic PSO has some demerits, such as relapsing into local optimum solution, slowing convergence velocity in the late evolutionary. To solve those problems, an particle swarm optimization with comprehensive learning & self-adaptive mutation (MLAMPSO) was proposed. The improved algorithm made adaptive mutation on population of particles in the iteration process, at the same time, the weight and learning factors were updated adaptively. It could enhance the ability of PSO to jump out of local optimal solution. The experiment results of some classic benchmark functions show that the improved PSO obviously improves the global search ability and can effectively avoid the problem of premature convergence.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Hao Tan AU - Jianjun Li AU - Jing Huang PY - 2015/03 DA - 2015/03 TI - Particle Swarm Optimization with Comprehensive Learning & Self-adaptive Mutation BT - First International Conference on Information Science and Electronic Technology (ISET 2015) PB - Atlantis Press UR - https://doi.org/10.2991/iset-15.2015.20 DO - https://doi.org/10.2991/iset-15.2015.20 ID - Tan2015/03 ER -