Multi-objective Co-operative Co-evolutionary

This paper presents the integration between two types of genetic algorithm: a multi-objective genetic algorithm (MOGA) and a co-operative co-evolutionary genetic algorithm (CCGA). The resulting algorithm is referred to as a multi-objective co-operative co-evolutionary genetic algorithm or MOCCGA. The integration between the twoalgorithms is carried out in order to improve the performance of the MOGA by adding the co-operativeco-evolutionary effect to the searchmechanisms employed by the MOGA. The MOCCGA is benchmarked against the MOGA in six different test cases. The test problems cover six differentcharacteristics that can be found within multi-objective optimisation problems: convex Pareto front, non-convex Pareto front, discrete Pareto front, multi-modality, deceptivePareto front and non-uniformity in the solution distribution. The simulation results indicate that overall the MOCCGA is superior to the MOGA in terms of the variety in solutions generated and the closeness of solutions to the true Pareto-optimal solutions