%A N.C. Sahoo
%A S. Ganguly
%A D. Das
%V 24
%T Simple heuristics-based selection of guides for multi-objective PSO with an application to electrical distribution system planning
%P 567-585
%K Bench-mark problems; Controlling parameters; Convergence characteristics; Electrical distribution system; Evolutionary multiobjective optimization; Guide selection; Multi objective; Multi-objective particle swarm optimization; Multi-objective planning; Nondominated solutions; Pareto-optimal sets; Pareto-optimality; Power distribution system planning; Real-world problem; Selection of local guides; Simulation result; Strength Pareto evolutionary algorithm, Algorithms; Electric load distribution; Electric power distribution; Encoding (symbols); Iterative methods; Pareto principle; Particle swarm optimization (PSO), Multiobjective optimization
%X In multi-objective particle swarm optimization (MOPSO), a proper selection of local guides significantly influences detection of non-dominated solutions in the objective/solution space and, hence, the convergence characteristics towards the Pareto-optimal set. This paper presents an algorithm based on simple heuristics for selection of local guides in MOPSO, named as HSG-MOPSO (Heuristics-based-Selection-of-Guides in MOPSO). In the HSG-MOPSO, the set of potential guides (in a PSO iteration) consists of the non-dominated solutions (which are normally stored in an elite archive) and some specifically chosen dominated solutions. Thus, there are two types of local guides in the HSG-MOPSO, i.e., non-dominated and dominated guides; they are named so as to signify whether the chosen guide is a non-dominated or a dominated solution. In any iteration, a guide, from the set of available guides, is suitably selected for each population member. Some specified proportion of the current population members follow their respective nearest non-dominated guides and the rest follow their respective nearest dominated guides. The proposed HSG-MOPSO is firstly evaluated on a number of multi-objective benchmark problems along with investigations on the controlling parameters of the guide selection algorithm. The performance of the proposed method is compared with those of two well-known guide selection methods for evolutionary multi-objective optimization, namely the Sigma method and the Strength Pareto Evolutionary Algorithm-2 (SPEA2) implemented in PSO framework. Finally, the HSG-MOPSO is evaluated on a more involved real world problem, i.e., multi-objective planning of electrical distribution system. Simulation results are reported and analyzed to illustrate the viability of the proposed guide selection method for MOPSO. © 2011 Elsevier Ltd. All rights reserved.
%J Engineering Applications of Artificial Intelligence
%L scholars2079
%O cited By 33
%N 4
%R 10.1016/j.engappai.2011.02.007
%D 2011