Particle Swarm Optimization with Crossover Operator and its Engineering Applications
Millie Pant, Radha Thangaraj, Vikram Singh · 2009
Abstract — This paper presents a variant of diversity guided Particle Swarm Optimization (PSO) algorithm named QIPSO for solving global optimization problems. In QIPSO the conventional framework of PSO is modified by including a crossover operator to maintain the level of diversity in the swarm population. Numerical results show that the induction of a crossover operator not only discourages premature convergence to the local optima but also explores promising regions of the search space effectively. Empirical results show the superior performance of QIPSO with conventional PSO and ARPSO. Efficiency of QIPSO is further validated by applying it to a set of five real life problems (RLPs) with constraints. Penalty method is used for dealing with constraints. Once again the simulation results show the compatibility of QIPSO for solving real life problems.