MEANDS: A Many-objective Evolutionary Algorithm based on Non-dominated Decomposed Sets applied to multicast routing

Abstract

Evolutionary algorithms have emerged in the last twenty years as a powerful approach for dealing with multi-objective optimization problems (MOPs). Although classical multi-objective evolutionary algorithms (MOEAs), such as SPEA2 and NSGA-II, have been designed to manipulate any number of objectives, the results of their practical application to MOPs with more than three objectives revealed that they have limitations. Many-objective algorithms represent the novelty in MOEA research because they are specially designed to handle search spaces of high dimension. In this paper, a new evolutionary algorithm able to handle discrete optimization problems with many objectives is proposed, called Many-objective Evolutionary Algorithm based on Non-dominated Decomposed Sets (MEANDS). MEANDS decomposes the original MOP into several, simpler MOPs, for which sub-populations of non-dominated solutions are maintained and evolved together. MEANDS relaxes several restrictions of predecessor algorithms, such as the size of sub-populations and the need for weights in the lower dimension MOPs. Empirical results show that MEANDS was able to find better solutions than those from well-known MOEAs (NSGA-III, SPEA2, SPEA2+SDE, MOEA/D, MOEA/DD, and its predecessor MEAMT) in the multicast routing problem involving 4, 5, and 6 QoS-based objectives.