A Novel Particle Swarm Optimizer Using Optimal Foraging Theory

摘要: Based on the research of optimal foraging theory (OFT), we present a novel particle swarm optimizer (PSO) to improve performance standard PSO (SPSO). The resulting algorithm is known as PSOOFT that makes use two mechanisms OFT: reproduction strategy enhance ability converge rapidly good solutions and patch-choice based scheme keep right balance exploration exploitation. In simulation studies, several benchmark functions are performed, proposed compared experimental results show prevents premature convergence high degree, but still has more rapid rate than SPSO.