Keynote address 2: Fundamental enhancements of particle swarm optimization in asynchronous, discrete, and multi-objective optimization

Particle Swarm Optimization (PSO) is a population based stochastic optimization algorithm, inspired by the social behavior of bird flocking and fish schooling. PSO has been introduced by Kennedy and Eberhart and contains a group of particles that move in a search space searching for an optimum solution according to a particular objective function. The movement of a particle is subjected to its own best found solution, pBest, and the best found solution in the neighborhood, gBest. This lecture presents the latest fundamental enhancements of PSO in asynchronous update, discrete, and multi-objective problems.