Journal
COMMUNICATIONS IN NONLINEAR SCIENCE AND NUMERICAL SIMULATION
Volume 18, Issue 11, Pages 3134-3145Publisher
ELSEVIER
DOI: 10.1016/j.cnsns.2013.03.011
Keywords
Particle swarm optimization; Age-group topology; Multimodal function optimization; Data clustering
Categories
Funding
- National Natural Science Foundation of China [60874072, 61070135]
- Zhejiang Provincial Natural Science Foundation [Q13F030023]
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This paper proposes particle swarm optimization with age-group topology (PSOAG), a novel age-based particle swarm optimization (PSO). In this work, we present a new concept of age to measure the search ability of each particle in local area. To keep population diversity during searching, we separate particles to different age-groups by their age and particles in each age-group can only select the ones in younger groups or their own groups as their neighbourhoods. To allow search escape from local optima, the aging particles are regularly replaced by new and randomly generated ones. In addition, we design an age-group based parameter setting method, where particles in different age-groups have different parameters, to accelerate convergence. This algorithm is applied to nonlinear function optimization and data clustering problems for performance evaluation. In comparison against several PSO variants and other EAs, we find that the proposed algorithm provides significantly better performances on both the function optimization problems and the data clustering tasks. (C) 2013 Elsevier B. V. All rights reserved.
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