Metaheuristic algorithms, especially those based on swarm intelligence (SI), form an important part of contemporary global optimization algorithms (Kennedy and Ebarhart, 1995; Yang, 2008; Auger and Teytaud, 2010; Auger and Doerr, 2010; Blum and Roli, 2003; Neumann and Witt 2010; Parpinelli and Lopes, 2011). Good examples are particle swarm optimization (PSO) (Kennedy and Eberhart, 1995) and fir...

Particle Swarm Optimization (PSO) is a technique used to explore the search space of a given problem to find the settings or parameters required to maximize a particular objective. This technique, first described by James Kennedy and Russell C. Eberhart in 1995 [1], originates from two separate concepts: the idea of swarm intelligence based off the observation of swarming habits by certain kind...

Particle Swarm Optimization (PSO) is a population based stochastic optimization technique developed by Eberhart (1995) and Kennedy, inspired by the social behavior of bird flocking or fish schooling. PSO shares many similarities with evolutionary computation techniques such as GA. The system is initialized with a population of random solutions and searches for optima by updating generations. Ho...