Particle swarm optimization (PSO) is a population-based stochastic optimization technique inspired by swarm intelligence. The underlying motivation for the development of PSO algorithm was social behavior of animals such as bird flocking, fish schooling, and swarm theory. The fundamental to the development of PSO is a hypothesis that social sharing of information among peers offers an evolutionary advantage. One of reasons that PSO is attractive is that there are very few parameters to be adjusted.

The following PSO software are provided in our Code Library:

  • DPSO (Dissipative particle swarm optimization): It is a PSO variant that was developed according to the self-organization of dissipative structure. The negative entropy is introduced to construct an opening dissipative system that is far-from-equilibrium so as to driving the irreversible evolution process towards a better fitness. [Click here for downloading the C source code (doc)]
  • DEPSO (or called DEPS): It is an algorithm that hybridizes the advantages of both PSO and Differential Evolution (DE) for solving (constrained) numerical optimization problem (NOP). [Click here for the Project Portal (doc)]
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