Performance Gaining for solving Many-objective Optimization Problems using Variation Operators

The recent years have witnessed phenomenal growth of interest in solving many-objective optimization problems. Many-objective Optimization Problems (MaOPs) are the Multi-objective Optimization Problems (MOPs) with typically more than three objectives. For instance, the designing of an electric motor involves satisfying at least seven objectives simultaneously. These include the size or weight of the machine, material cost, maximum or average torque, torque ripple and efficiency or losses (core and copper). In general, some of these objectives are at odds with each other. Among the various reasons cited for this inefficiency is the non-suitability of conventional variation operators of intelligent meta-heuristics in generating solutions that attain the convergence goal satisfactorily. Many latest studies have asserted the need for developing new variation operators as well as using their systematic combination to augment their search performance for solving MaOPs. Accordingly the main objective of this review paper is to give short description of the various significant attempts in this particular problem solving approach along with future research directions.