Particle Swarm Optimization (PSO) is a population-based, swarm-intelligence algorithm for global optimization. Originally developed by Kennedy, Eberhart and Shi for simulating social behavior, as a very abstract representation of the movement of bird flocks or fish school, the algorithm was recognized as a powerful optimizer and presented as such during 1995’s International Conference on Neural Networks (ICNN’95). The query “Particle Swarm Optimization” returns 470000 results on Google Scholar, and the original paper has almost 90000 citations, two results that highlight the popularity of the method among practitioners and evolutionary algorithms enthusiasts. The goal of this special session is to pay tribute to Kennedy, Eberhart and Shi’s algorithm, by bringing together the best researchers working on PSO.

Topics of Interest

• Theory and practice of particle swarm optimization
• Cutting edge modern applications of PSO
• Novel ideas and out-of-the-box versions of PSO
• Reports of PSO’s superiority in specific domains and applications
• Historical and retrospective information about PSO and its authors
• Novel mechanisms and operators
• Constraint handling in PSO
• Convergence and stall analysis
• PSO with evolutionary operators
• Discrete and combinatorial versions of PSO
• Multi- and many-objectives PSO

Organisers

• Leonardo Vanneschi
  Universidade NOVA de Lisboa, Portugal
  lvanneschi(at)novaims.unl.pt
• Marco S. Nobile
  Ca’ Foscari University of Venice, Italy
  marco.nobile(at)unive.it
• Vasco Coelho
  University of Milano-Bicocca, Italy
  vasco.coelho(at)unimib.it