Multiobjective particle swarm for environmental/economic dispatch problem

Abstract

A multiobjective particle swarm optimization (MOPSO) technique for 
environmental/economic dispatch (EED) problem is proposed in this 
paper. The new MOPSO technique evolves a multiobjective version 
of PSO by proposing redefinition of global best and local best individuals 
in multiobjective optimization domain. A clustering algorithm to manage 
the size of the Pareto-optimal set and fuzzy-based mechanism to extract 
the best compromise solution are imposed. The proposed MOPSO technique 
has been implemented to solve the EED problem with competing cost and 
emission objectives. Several optimization runs of the proposed approach 
have been carried out on a standard test system. The results demonstrate 
the capabilities of the proposed MOPSO technique to generate a set of 
well-distributed Pareto-optimal solutions in one single run. The comparison 
with the different reported techniques demonstrates the superiority of the 
proposed MOPSO in terms of the diversity of the Pareto optimal solutions obtained.