Distributed Particle Swarm Optimization Based on Primal-Dual Decomposition Architectures

• Wakasa Yuji

  Graduate School of Science and Engineering, Yamaguchi University

• Yamasaki Sho

  Graduate School of Science and Engineering, Yamaguchi University

Abstract

Distributed optimization methods have been studied recently motivated by emerging applications in smart grid, multi-robot, etc. In most of the studies, convexity and smoothness of the objective and constraint functions are assumed while such assumptions are not always made in practice. This paper presents a distributed particle swarm optimization algorithm based on primal-dual decomposition architectures so that any gradient information is not needed. To investigate the potential of the presented algorithm, some simple numerical examples are provided while comparing with the conventional distributed primal-dual perturbation algorithm.

Journal

• Proceedings of the ISCIE International Symposium on Stochastic Systems Theory and its Applications

  Proceedings of the ISCIE International Symposium on Stochastic Systems Theory and its Applications 2015 (0), 97-101, 2015

  The ISCIE Symposium on Stochastic Systems Theory and Its Applications

Keywords

• Materials Science (miscellaneous)

Details

• CRID: 1390282680740008320
• NII Article ID: 130005107460
• DOI: 10.5687/sss.2015.97
• ISSN: 21884749
• ISSN: 21884730
• Web Site: https://www.jstage.jst.go.jp/article/sss/2015/0/2015_97/_pdf
• Text Lang: en
• Data Source:
  • JaLC
  • Crossref
  • CiNii Articles
• Abstract License Flag: Disallowed