An Improved Competitive Swarm Optimizer Based on Generalized Pareto Dominance for Large-scale Multi-objective and Many-objective Problems

Large-scale multi-objective and many-objective problems are widely existing in the real-world. These problems are extremely challenging to deal with as a result of exponentially expanded search space as well as complicated conflicting objectives. Most existing algorithms focus either on large-scale decision variables or multiple objectives solely while few algorithms consider both of them. In this paper, we propose an improved competitive swarm optimization (ICSO) dedicated to deal with large-scale search space. Moreover, we incorporate ICSO into the MultiGPO framework, an efficient framework for many-objective problems, and name it as MultiGPO_ICSO. To validate the performance of MultiGPO_ICSO, we test all algorithms on LSMOP with dimensions varying from 100 to 500. Compared with other algorithms, MultiGPO_ICSO shows competitive performance on most problems with limited computational resources. Therefore, MultiGPO_ICSO is suitable to deal with large-scale multi-objective and many-objective problems.