A Competitive and Cooperative Swarm Optimizer for Constrained Multiobjective Optimization Problems
针对现有竞争群优化器在处理约束多目标问题时收敛慢、易陷入局部可行域的问题，提出一种结合竞争与合作两种粒子更新策略的新算法，在47个测试实例上优于其他先进方法。
Solving multiobjective optimization problems (MOPs) through metaheuristic methods gets considerable attention. Based on the classical variation operators, several enhanced operators, as well as multiobjective optimization evolutionary algorithms, have been developed. Among these operators, the competitive swarm optimizer (CSO) exhibits promising performance. However, it encounters difficulties when tackling constrained MOPs (CMOPs) with large objective spaces or complex infeasible regions. In this article, a competitive and cooperative swarm optimizer is proposed, which contains two particle update strategies: 1) the CSO provides faster convergence speed to accelerate the approximation of the Pareto front and 2) the cooperative swarm optimizer suggests a mutual-learning strategy to enhance the ability to jump out of local feasible regions or local optima. We also present a new algorithm for CMOPs. The results on four benchmark suites with 47 instances demonstrate the superiority of our approach compared with other state-of-the-art methods. Additionally, its effectiveness on large-scale CMOPs has also been verified.