Multi-objective cloud estimation of distribution particle swarm optimizer using maximum ranking

By combining CMBOA and PSO with quasi-oppositional learning, a cloud estimation of distribution particle swarm optimizer is firstly introduced. Then, it is extended to multi-objective optimization problems by using maximum ranking. In the algorithm´s offspring generation scheme, new individuals are generated in the cloud estimation of distribution way or in the PSO way. And instead of Pareto dominance, maximum ranking is used to identify the best individuals in order to guide the search process. An archive with maximum capacity is maintained. At iteration, the archive gets updated with the inclusion of the non-dominated solutions from the combined population and the archive. If the size of the archive exceeds the maximum capacity, it is cut according to the maximum ranking. The proposed algorithm is applied to some well-known benchmarks. Convergence metric, diversity metric are used to evaluate the performance of the algorithm. The experimental results show that the algorithm is effective on the benchmark functions.