Name
Abstract | ||
|---|---|---|
AbstractParticle swarm optimization PSO has recently been extended in several directions. Heterogeneous PSO HPSO is one of such recent extensions, which implements behavioural heterogeneity of particles. In this paper, we propose a further extended version, Hierarchcial Heterogeenous PSO HHPSO, in which heterogeneous behaviors of particles are enforced through interactions among hierarchically structured particles. Two algorithms have been developed and studied: multi-layer HHPSO ml-HHPSO and multi-group HHPSO mg-HHPSO. In each HHPSO algorithm, stagnancy and overcrowding detection mechanisms were implemented to avoid premature convergence. The algorithm performance was measured on a set of benchmark functions and compared with performances of standard PSO SPSO and HPSO. The results demonstrated that both ml-HHPSO and mg-HHPSO performed well on all testing problems and significantly outperformed SPSO and HPSO in terms of solution accuracy, convergence speed and diversity maintenance. Further computational experiments revealed the optimal frequencies of stagnation and overcrowding detection for each HHPSO algorithm. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1080/17445760.2015.1118477 | Periodicals |
Keywords | Field | DocType |
Heterogeneous behaviors, hierarchical heterogeneous particle swarm optimization, hierarchical structure, particle swarm optimization | Convergence (routing),Particle swarm optimization,Mathematical optimization,Premature convergence,Computer science,Algorithm,Multi-swarm optimization,Diversity maintenance | Journal |
Volume | Issue | ISSN |
31 | 5 | 1744-5760 |
Citations | PageRank | References |
0 | 0.34 | 22 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| xinpei ma | 1 | 0 | 0.34 |
| Hiroki Sayama | 2 | 319 | 49.14 |