A comparative study of the various genetic approaches to solve multi-objective optimization problems

Many simple decision processes are based on a single objective such as minimizing cost, maximizing profit, minimizing runtime and so forth. However decisions must be made in an environment where more than one objective, constrains the problem, and the relative value of each of these objective is different. Such problems where multiple objectives are to be optimized are known as Multi-objective optimization (MOO) problems. The problem becomes challenging in case of mutually conflicting objectives i.e. the optimal solution widely varies with the shifting of focus from one objective to the other while all of them are quite relevant for the problem. MOO is used in various fields viz. Production Planning, Structural Design etc. There are various genetic approaches to solve multi-objective optimization problems like Non-dominated sorting GA (NSGA-II), Strength Pareto Evolutionary Approach (SPEA) etc. Each of these methods has their pros and cons and none of these is found to be perfect. In this paper we present a comparative study of the various available methods based on evolutionary genetic algorithms for Multi-Objective Optimization on different performance metrics.