SS05: Advances in Particle Swarm Optimization(ISICA 2019 Special Session)

Today, Swarm Intelligence has become an increasingly important driving role for business strategies and real modern world applications. The essential idea of Swarm Intelligence is the emergent collective intelligence of groups of simple agents. There are several popular algorithms based on these concepts, including Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Artificial Bee Colony (ABC) algorithms and Simplified Swarm Optimization (SSO), etc. Despite a significant amount of research on Swarm Intelligence, there remain many open issues and intriguing challenges in the field. The aims of this special session are to demonstrate the current state-of-the-art concepts, theory and practice of Swarm Intelligence, to reflect on the latest advances in swarm intelligent design and applications for real world problems, and to explore the future directions in Swarm Intelligence.


Potential authors for this Special Session should present original research and innovative results including (but not limited to) the following topics:

  • Convergence analysis and parameter choice of PSO
  • Empirical and theoretical analyses of the dynamics of PSO particles and populations
  • Multiple population cooperative PSO
  • Advanced bare-bones and distribution-based PSOs
  • PSOs for stochastic, dynamic, multi-objective and combinatorial optimization problems
  • Novel combinations of PSO algorithms with other techniques
  • Novel applications in bioinformatics, image and signal processing, and computational intelligence


Kangshun Li
South China Agricultural University, China,