A New Cooperative Co-evolutionary Multi-objective Algorithm for Function Optimization

In this reach work, a well performing approach in the context of multiobjective evolutionary algorithm (MOEA) is investigated due to its complexity. This approach called NSCCGA is based upon a previously introduced approach called NSGA-II. NSCCGA performs better than NSGA-II but with a heavy load of computational complexity. Here, a novel approach called GBCCGA is introduced based on MOCCGA with some modifications. The main difference between GBCCGA and MOCCGA is in their niching technique, i.e., a novel grid-based technique is used in GBCCGA instead of the traditional sharing mechanism in MOCCGA. The results show that GBCCGA performs roughly the same as NSCCGA but with very low computational complexity with respect to the original MOCCGA.