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Advances in particle swarm algorithms in asynchronous, discrete and multi-objective optimization

This paper presents the latest advancement of Particle Swarm Optimization (PSO) in asynchronous update, discrete, and multi-objective problems. 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.