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A novel multi-objective competitive swarm optimization algorithm for multi-modal multi objective problems

机译:一种新型多目标竞争群优化算法,用于多模态多目标问题

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摘要

With the combination of multi-objective optimization problems and practical application, it is important to find as many Pareto optimal solutions as possible for solving multi-objective optimization problems. In many real world cases, different decision makers may tend to make choices from various aspects, which leads to the generation of multi-modal optimization problems. Such featured problems may include multiple Pareto solutions in the decision space corresponding to the same objective value in the objective space, remarkably challenging the conventional solvers. In this paper, a novel multi-objective multi-modal optimization algorithm named MO_Ring_CSO_SCD is proposed based on a recent proposed meta-heuristic method competitive swarm optimizer algorithm. It also combines the ring topology, non-dominated sort strategy and special crowding distance approach. Numerical experiments were conducted on 11 multi-modal test functions and the proposed optimization algorithm has been compared with other counterparts. The results show that the proposed algorithm has competitive performance in solving the multi-objective multi-modal problem.
机译:随着多目标优化问题和实际应用的组合,重要的是找到尽可能多的帕累托最佳解决方案,以解决多目标优化问题。在许多现实世界案例中,不同的决策者可能倾向于从各个方面做出选择,这导致了多模态优化问题的产生。这种特征问题可以包括在决策空间中的多个帕累托解决方案对应于目标空间中相同的客观值,显着挑战传统求解器。本文基于近期建议的元启发式方法竞争群优化器算法,提出了一种名为Mo_ring_CSO_SCD的新型多目标多模态优化算法。它还结合了环形拓扑,非主导的排序策略和特殊拥挤距离方法。在11个多模态测试功能上进行了数值实验,并将所提出的优化算法与其他对应物进行比较。结果表明,该算法在解决多目标多模态问题方面具有竞争性能。

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