Proceedings of the First International Conference on Information Science and Electronic Technology

Particle Swarm Optimization with Comprehensive Learning & Self-adaptive Mutation

Authors
Hao Tan, Jianjun Li, Jing Huang
Corresponding Author
Hao Tan
Available Online March 2015.
DOI
https://doi.org/10.2991/iset-15.2015.20How to use a DOI?
Keywords
Particle swarm optimization, Adaptive mutation, Weight, Learning factors, Convergence
Abstract
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.
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Proceedings
First International Conference on Information Science and Electronic Technology (ISET 2015)
Part of series
Advances in Computer Science Research
Publication Date
March 2015
ISBN
978-94-62520-50-9
DOI
https://doi.org/10.2991/iset-15.2015.20How to use a DOI?
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  -