Particle Swarm Optimizer with Aging Operator for Multimodal Function Optimization

DOI

10.1080/18756891.2013.807108How to use a DOI?

Keywords

soft computing, function optimization, particle swarm optimizer (PSO), swarm intelligence

Abstract

This paper proposes a new scheme for preventing a Particle Swarm Optimizer from premature convergence on multimodal optimization problems. Instead of only using fitness evaluation, we use a new index called particle age to guide population towards more promising region of the search space. The particle age is a measure of how long each particle moves towards a better solution. The main novelty of the proposed method is to let each particle learn from not only neighbours with better fitness values but also the neighbours whose fitness values are updated more frequently. To achieve this, we design a comprehensive age-based learning strategy, in which age is used for excluding old particles, selecting learning exemplars and deciding mutation strength and inertial weight for each particle. Experiments were conducted on 15 multimodal test functions to assess the performance of this new strategy in comparison with 7 state-of-the-art PSOs from the literature. The experimental results show the good performance of the proposed algorithm in solving multimodal functions when compared with several existing PSO variants.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Bo Jiang
AU  - Ning Wang
AU  - Xiaodong Li
PY  - 2013
DA  - 2013/09/01
TI  - Particle Swarm Optimizer with Aging Operator for Multimodal Function Optimization
JO  - International Journal of Computational Intelligence Systems
SP  - 862
EP  - 880
VL  - 6
IS  - 5
SN  - 1875-6883
UR  - https://doi.org/10.1080/18756891.2013.807108
DO  - 10.1080/18756891.2013.807108
ID  - Jiang2013
ER  -