PSGO: Particle swarm gravitation optimization algorithm

Ko Wei Huang, Jui Le Chen, Chu Sing Yang, Chun Wei Tsai

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)

Abstract

Particle swarm optimization (PSO) is the most well known of the swarm-based intelligence algorithms. However, the PSO converges prematurely, which rapidly decreases the population diversity, especially when approaching local optima. To improve the diversity of the PSO, we here propose a memetic algorithm called particle swarm gravitation optimization (PSGO). After a specific number of iterations, some individuals selected from the PSO and GSA systems are exchanged by the roulette wheel approach. Finally, to increase the diversities of the PSO and GSA, we introduce a diversity enhancement operator, which is inspired by the crossover operator used in differential evolution algorithms. In evaluations of five benchmark functions, the PSGO significantly outperformed the PSO and Cuckoo search and yielded a superior performance to the GSA of most of instances and computation times.

Original languageEnglish
Pages (from-to)2655-2665
Number of pages11
JournalJournal of Intelligent and Fuzzy Systems
Volume28
Issue number6
DOIs
Publication statusPublished - 2015 Aug 10

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Engineering(all)
  • Artificial Intelligence

Fingerprint Dive into the research topics of 'PSGO: Particle swarm gravitation optimization algorithm'. Together they form a unique fingerprint.

Cite this