基于膜系统的粒子群算法
Particle Swarm Optimization Algorithm Based on Membrane System
摘要: 由于粒子群算法具有易于理解实现等特点，受到科学与工程领域的广泛关注，但其搜索策略较为单一，使得算法很难获得Pareto前沿且容易陷入局部最优和无限迭代，在全局搜索和收敛性方面还有一定的不足。受P系统理论启发，本文提出了一种基于膜系统框架的多目标粒子群优化算法用于解决多目标优化问题。根据P系统的层次结构、对象和规则，在基本膜中采用粒子群算法实现并行搜寻策略，在表层膜中采用非支配排序和拥挤距离机制来提高算法收敛速度，并通过进化规则以保持解的多样性。仿真实验采用KUR、ZDT系列和DTLZ系列测试函数对MPSO算法进行测试，并与其他多目标优化算法进行对比，包括MOPSO、dMOPSO、SMPSO、MMOPSO、MOEA/D、SPEA2、PESA2、NSGAII。实验结果表明：新算法能够保证解的多样性，快速收敛并接近于真实的Pareto前沿。
Abstract: Due to the particle swarm algorithm has the characteristics of easy understanding and implementation, it is widely concerned in the field of science and engineering, but its search strategy is relatively simple, making it difficult for the algorithm to obtain the Pareto frontier and easy to fall into local optimal and infinite iteration. There are still some deficiencies in the aspect of global search and convergence. Inspired by the P system theory, this paper proposes a multi-objective particle swarm optimization algorithm based on the membrane system framework to solve the mul-ti-objective optimization problem. According to the hierarchical structure, objects and rules of P system, the particle swarm algorithm is used to implement the parallel search strategy in the base film. The nondominated sorting and congestion distance mechanism are used in the surface film to improve the convergence speed of the algorithm, and the evolution rule is used to maintain the diversity of solution. The simulation experiment uses the KUR, ZDT series and DTLZ series test functions to test the MPSO algorithm and compare it with other multi-objective optimization algorithms, including MOPSO, dMOPSO, SMPSO, MMOPSO, MOEA/D, SPEA2, PESA2, NSGAII. The experimental results show that the new algorithm can guarantee the diversity of the solution, is fast convergence and close to the real Pareto frontier.
文章引用：宋楠, 陈韬伟, 余益民, 赵昆. 基于膜系统的粒子群算法[J]. 计算机科学与应用, 2019, 9(7): 1406-1415. https://doi.org/10.12677/CSA.2019.97158

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