AN EVALUATION OF A CONSTRAINED MULTI-OBJECTIVE GENETIC ALGORITHM

Abstract

Real world optimization problems involve multiple conflicting objectives (such as minimizing cost while maximizing the quality of a product) and are subject to constraints (such as physical feasibility or budget limitations) which makes them interesting to solve. Over the last decades, evolutionary algorithms have been largely used in solving optimization problems in various fields of science. The aim of this study is to evaluate the performance of a constrained version of the Non-dominated Sorting Genetic Algorithm 2 (NSGA 2), a multi-objective evolutionary optimization algorithm, written in MATLAB. The developed NSGA 2 is compared, in terms of convergence and diversity of the obtained solutions, to a number of popular constrained multi-objective evolutionary algorithms from the literature. Widely used four benchmark problems (including CONSTR, OSY, SRN, and TNK problems) with varying difficulty and type of constraints are reviewed and used. The NSGA 2 obtained the lowest values of inverse generational distance (IGD) values for almost all the problems. These results show that the developed constrained NSGA 2 is an effective technique and is competitive to the other optimization methods in the literature.