Abstract

Intuitionistic Fuzzy Sets is one of the most influential extension and development of Zadeh’s fuzzy set theory. It has strong performance in dealing with uncertain information, while taking into account information on membership degree, non-membership degree and hesitation degree. In this paper, a new loose Pareto dominant relationship named intuitionistic fuzzy dominance is adopted to research multi/many-objective particle swarm optimization problems. Particle swarm optimization (PSO) with double search strategy is employed to update the population to enhance the exploitation and exploration capability of particle in the objective space, especially high-dimensional objective space. In addition, the uniformly distributed reference points are used to balance the convergence and diversity of the algorithm. The proposed algorithm has been compared with four recent multi-objective particle swarm optimization algorithms and four state-of-the-art many-objective evolutionary algorithms on 16 benchmark MOPs with 3, 5,8,10 and 15 objectives, respectively. The simulation results show that the proposed algorithm has better performance on most test problems.

中文翻译：

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基于参考点和直觉模糊优势的粒子群算法的多目标优化

摘要

直觉模糊集是Zadeh模糊集理论最有影响力的扩展和发展之一。它在处理不确定性信息方面具有很强的性能，同时考虑到有关成员资格程度，非成员资格程度和犹豫程度的信息。本文采用一种新的松散的Pareto主导关系，称为直觉模糊优势，研究多/多目标粒子群优化问题。利用具有双重搜索策略的粒子群优化算法（PSO）来更新种群，以增强目标空间（尤其是高维目标空间）中粒子的开发和探索能力。另外，均匀分布的参考点用于平衡算法的收敛性和多样性。将该算法与分别针对3个，5个，8个，10个和15个目标的16个基准MOP的四种最新的多目标粒子群优化算法和四种最新的多目标进化算法进行了比较。仿真结果表明，该算法在大多数测试问题上具有较好的性能。