Multiobjective Particle Swarm Algorithm with Fuzzy Clustering for Electrical Power Dispatch

Abstract

Economic dispatch is a highly constrained optimization problem encompassing 
interaction among decision variables. Environmental concerns that arise due to 
the operation of fossil fuel fired electric generators, transforms the classical 
problem into multiobjective environmental/economic dispatch (EED). In this 
paper, a fuzzy clustering-based particle swarm (FCPSO) algorithm has been 
proposed to solve the highly constrained EED problem involving conflicting 
objectives. FCPSO uses an external repository to preserve nondominated 
particles found along the search process. The proposed fuzzy clustering 
technique, manages the size of the repository within limits without destroying 
the characteristics of the Pareto front. Niching mechanism has been incorporated 
to direct the particles towards lesser explored regions of the Pareto front. To 
avoid entrapment into local optima and enhance the exploratory capability of the 
particles, a self-adaptive mutation operator has been proposed. In addition, the 
algorithm incorporates a fuzzy-based feedback mechanism and iteratively uses 
the information to determine the compromise solution. The algorithm's performance 
has been examined over the standard IEEE 30 bus six-generator test system, 
whereby it generated a uniformly distributed Pareto front whose optimality 
has been authenticated by benchmarking against the epsilon-constraint method. 
Results also revealed that the proposed approach obtained high-quality solutions 
and was able to provide a satisfactory compromise solution in almost all the trials, 
thereby validating the efficacy and applicability of the proposed approach over the 
real-world multiobjective optimization problems.