Multiobjective optimization with competitive coevolutionary genetic algorithms

Many methods solving multiobjective optimization using genetic algorithm tend to work negatively, causing that the population converges to small number of solutions due to the random genetic drift. To avoid this phenomenon, a competitive coevolutionary genetic algorithm(CoCGA) for multiobjective optimization is proposed. In the algorithms, each objective corresponds to a population. At each generation, these populations compete among themselves. The result of the competition is employed to direct the adjustment over the relation at individual and population levels. The proposed approach stores the Pareto optimal point obtained along the evolutionary process into external set. The proposed approach is validated using Schaffer´s test function f2 and it is compared with the Niched Pareto GA(nPGA). Simulation experiments prove that the algorithm has a better performance in finding the Pareto solutions, and the CoCGA can have advantages over the other algorithms under consideration in convergence to the Pareto-optimal front.