Clustering-Based Leaders' Selection in Multi-Objective Particle Swarm Optimisation

Clustering-based Leaders’ Selection (CLS) is a novel approach for leaders selection in multi-objective particle swarm optimisation. Both objective and solution spaces are clustered. An indirect mapping between clusters in both spaces is defined to recognize regions with potentially better solutions. A leaders archive is built which contains representative particles of selected clusters in the objective and solution spaces. The results of applying CLS integrated with OMOPSO on seven standard multi-objective problems, show that clustering based leaders selection OMOPSO (OMOPSO/C) is highly competitive compared to the original algorithm.