精英竞争和综合控制的多目标粒子群算法

目的 多目标粒子群算法虽然极易实现且收敛速度快，但在平衡其收敛性和多样性方面仍需进一步改善。方法 针对上述问题，提出一种精英竞争和综合控制的多目标粒子群算法(ECMOPSO)。一方面，算法采用全局损害选择精英粒子集，然后将两两竞争引入多目标粒子群算法中，通过精英竞争选取优胜者粒子，将其与全局领导者融合形成更全面的社会综合信息，以增强种群中粒子之间信息的交互性，更好引导种群中的粒子飞行，提升算法全局探索能力；另一方面，结合全局损害和基于位移密度估计对外部存档进行维护，从而提高外部存档中非劣解的质量，平衡算法的收敛性和多样性。结果 将ECMOPSO算法与4个多目标粒子群算法和4个多目标进化算法在ZDT和UF系列基准测试问题上进行仿真实验，并采用Wilcoxon秩和检验和Friedman秩检验比较ECMOPSO算法与所选对比算法的整体性能。实验结果表明：相比其他几个对比算法，ECMOPSO算法的收敛能力、解的分布性以及稳定性都得到了一定的提升。结论 ECMOPSO算法可以很好地平衡收敛性和多样性，提升其整体性能，能有效求解大多数多目标优化问题。

Multi-objective Particle Swarm Optimization with Elite Competition and Comprehensive Control

Objective Although multi-objective particle swarm optimization is easy to implement and has a fast convergence speed it still needs to be further improved in the aspect of balancing convergence and diversity. Methods To solve the above problem a multi-objective particle swarm optimization with elite competition and comprehensive control ECMOPSO was proposed. On one hand the algorithm selected the elite particle set with global detriment and then the pairwise competitions were introduced into the multi-objective particle swarm optimization. The winner particles were selected through elite competition and the winner particles were fused with the global leaders to form more comprehensive social information so as to enhance the information interaction between particles in the population better guide the flight of particles in the population and improve the global exploration ability of the algorithm. On the other hand global detriment and shifted-based density estimation were combined to maintain the external archive so as to improve the quality of non-dominated solutions in the external archive and balance the convergence and diversity of the algorithm. Results The ECMOPSO algorithm four multi-objective particle swarm optimization algorithms and four multi-objective evolutionary algorithms were simulated for ZDT and UF series benchmark problems and the Wilcoxon rank sum test and the Friedman rank test were used to compare the overall performance of ECMOPSO algorithm with the selected comparison algorithms. The experimental results showed that compared with other comparison algorithms the convergence ability solution distribution and stability of the ECMOPSO algorithm have been improved to some extent. Conclusion ECMOPSO algorithm can balance convergence and diversity well improve its overall performance and can effectively solve most multi-objective optimization problems.