Performance scalability of a cooperative coevolution multiobjective evolutionary algorithm

Recently, numerous Multiobjective Evolutionary Algorithms (MOEAs) have been presented to solve real life problems. However, a number of issues still remain with regards to MOEAs such as convergence to the true Pareto front as well as scalability to many objective problems rather than just bi-objective problems. The performance of these algorithms maybe augmented by incorporating the coevolutionary concept. Hence, in this paper, a new algorithm for multiobjective optimization called SPEA2-CC is illustrated. SPEA2-CC combines an MOEA, Strength Pareto Evolutionary Algorithm 2 (SPEA2) with Cooperative Coevolution (CC). Scalability tests have been conducted to evaluate and compare the SPEA2-CC against the original SPEA2 for seven DTLZ test problems with a set of objectives (3 to 5 objectives). The results show clearly that the performance scalability of SPEA2-CC was significantly better compared to the original SPEA2 as the number of objectives becomes higher