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Evolutionary multi-objective optimization to attain practically desirable solutions

Abstract:

This work investigates two methods to search practically desirable solutions expanding the objective space with additional fitness functions associated to particular decision variables. The aim is to find solutions around preferred values of the chosen variables while searching for optimal solutions in the original objective space. Solutions to be practically desirable are constrained to be within a certain distance from the present non-dominated solutions set computed in the original objective space. The proposed methods are compared with an algorithm that simply restricts the range of decision variables around the preferred values and an algorithm that expands the space without constraining the distance from optimality. Our results show that the proposed methods can effectively find practically desirable solutions. Copyright © 2013 ACM.