Adaptive Multi-layer Particle Swarm Optimization with Neighborhood Search

Particle swarm optimization（PSO） has shown a good performance on solving global optimization problems. Traditional PSO has two main drawbacks of premature convergence and low convergence speed, especially on complex problems. This paper presents a new approach called Adaptive multi-layer particle sw...

Full description

Saved in:
Bibliographic Details
Published in:Chinese Journal of Electronics 2016-11, Vol.25 (6), p.1079-1088
Main Authors: Tran, Dang Cong, Wu, Zhijian
Format: Article
Language:English
Subjects:
adaptive multilayer particle swarm optimization
adaptive multilayer search strategy
Adaptive multi‐layer search
AMPSONS algorithm
Global optimization
global optimization problems
Neighborhood search
neighborhood search strategy
particle swarm optimisation
Particle swarm optimization
search problems
Citations: Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Particle swarm optimization（PSO） has shown a good performance on solving global optimization problems. Traditional PSO has two main drawbacks of premature convergence and low convergence speed, especially on complex problems. This paper presents a new approach called Adaptive multi-layer particle swarm optimization with neighborhood search（AMPSONS）, where the traditional PSO is improved by employing an adaptive multi-layer search and neighborhood search strategy to achieve a trade-off between exploitation and exploration abilities. In order to evaluate the performance of the proposed AMPSONS algorithm, the performance of AMPSONS is compared with five other PSO family algorithms,namely, CLPSO, DNLPSO, DNSPSO, global MLPSO and local MLPSO on a set of benchmark functions. The comparison results show that AMPSONS has a promising performance on ma jority of the test functions.
ISSN:1022-4653
2075-5597
DOI:10.1049/cje.2016.06.011