Multi-Objective Particle Swarm Optimization (MOPSO) based on Pareto Dominance Approach

This paper presents a comprehensive review of a multiobjective particle swarm optimization (MOPSO) reported in the specialized literature. The success of the Particle Swarm Optimization (PSO) algorithm as a single-objective optimizer has motivated researchers to extend the use of bio-inspired technique to other areas. One of them is multi-objective optimization. Multi-objective optimization is a class of problems with solutions that can be evaluated along two or more incomparable or conflicting objectives. These types of problems differ from standard optimization problems in that the end result is not a single \best solution" but rather a set of alternatives, where for each member of the set, no other solution is completely better (the Pareto set). Multi-objective optimization problems occur in many different real-world domains such as automobile design and architecture. A multiobjective particle swarm optimization (MOPSO) method can be used to solve the problem of effective channel selection. General Terms MOPSO and PSO Algorithm