Particle Swarm Optimization: A Comprehensive Survey

Particle swarm optimization (PSO) is one of the most well-regarded swarm-based algorithms in literature. Although original PSO has shown good performance, it still severely suffers from premature convergence. As a result, many researchers have been modifying resulting large number variants with either slightly or significantly better performance. Mainly, standard modified by four main strategies: modification controlling parameters, hybridizing other well-known meta-heuristic such as genetic algorithm (GA) and differential evolution (DE), cooperation multi-swarm techniques. This paper attempts to provide comprehensive review PSO, including basic concepts binary neighborhood topologies recent historical variants, remarkable engineering applications its drawbacks. Moreover, this reviews studies that utilize solve feature selection problems. Finally, eight potential research directions can help further enhance performance are provided.