Two Decomposition-based Modern Metaheuristic Algorithms for Multi-objective Optimization - A Comparative Study

This paper presents the multi-objective variants of two popular metaheuristics of current interest, namely, the artificial bee colony algorithm, and the teaching-learning-based optimization algorithm. These two approaches are used to solve real-parameter, bound constrained multi-objective optimization problems. The proposed multi-objective variants are based on a decomposition approach, where a multi-objective optimization problem is transformed into a number of scalar optimization sub-problems which are simultaneously optimized. The proposed algorithms are tested on seven unconstrained test problems proposed for the special session and competition on multi-objective optimizers held at the 2009 IEEE Congress on Evolutionary Computation as well as on five classical bi-objective test instances. The proposed approaches are compared with two decomposition- based multi-objective evolutionary algorithms which are representative of the state-of-the-art in the area. Our results indicate that the proposed approaches obtain highly competitive results in most of the test instances.