Multiobjective particle swarm optimization with nondominated local and global sets

Abstract

In multiobjective particle swarm optimization (MOPSO) methods, selecting the 
local best and the global best for each particle of the population has a great 
impact on the convergence and diversity of solutions, especially when optimizing 
problems with high number of objectives. This paper presents an approach using 
two sets of nondominated solutions. The ability of the proposed approach to 
detect the true Pareto optimal solutions and capture the shape of the Pareto 
front is evaluated through experiments on well-known non-trivial multiobjective 
test problems as well as the real-life electric power dispatch problem. The 
diversity of the nondominated solutions obtained is demonstrated through 
different measures. The proposed approach has been assessed through a 
comparative study with the reported results in the literature.