Vector coevolving particle swarm optimization algorithm
Number of Authors: 5
2017 (English)In: Information Sciences, ISSN 0020-0255, E-ISSN 1872-6291, Vol. 394-395, 273-298 p.Article in journal (Refereed) Published
In this paper, we propose a novel vector coevolving particle swarm optimization algorithm (VCPSO). In VCPSO, the full dimension of each particle is first randomly partitioned into several sub-dimensions. Then, we randomly assign either one of our newly designed scalar operators or learning operators to update the values in each sub-dimension. The scalar operators are designed to enhance the population diversity and avoid premature convergence. In addition, the learning operators are designed to enhance the global and local search ability. The proposed algorithm is compared with several other classical swarm optimizers on thirty-three benchmark functions. Comprehensive experimental results show that VCPSO displays a better or comparable performance compared to the other algorithms in terms of solution accuracy and statistical results.
Place, publisher, year, edition, pages
2017. Vol. 394-395, 273-298 p.
Media and Communication Technology
Research subject Mobile and Pervasive Computing
IdentifiersURN: urn:nbn:se:ltu:diva-61834DOI: 10.1016/j.ins.2017.01.038ScopusID: 2-s2.0-85012262497OAI: oai:DiVA.org:ltu-61834DiVA: diva2:1071719
Validerad; 2017; Nivå 2; 2017-03-21 (rokbeg)2017-02-062017-02-062017-03-21Bibliographically approved