Abstract
The global best (gbest) or local best (lbest) of every particle in state-of-the-art multi-objective particle swarm optimization (MOPSO) implementations is selected from the non-dominated solutions in the external archive. This approach emphasizes the elitism at the expense of diversity when the size of the current set of non-dominated solutions in the external archive is small. This article proposes that the gbests or lbests should be chosen from the top fronts in a non-domination sorted external archive of reasonably large size. In addition, a novel two local bests (lbest) based MOPSO (2LB-MOPSO) version is proposed to focus the search around small regions in the parameter space in the vicinity of the best existing fronts unlike the current MOPSO variants in which the pbest and gbest (or lbest) of a particle can be located far apart in the parameter space thereby potentially resulting in a chaotic search behaviour. Comparative evaluation using 19 multi-objectives test problems and 11 state-of-the-art multi-objective evolutionary algorithms ranks overall the 2LB-MOPSO as the best while two state-of-the-art MOPSO algorithms are ranked the worst with respect to other multi-objective evolutionary algorithms.
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call