Abstract

In this paper, a modified Competitive Mechanism Multi-Objective Particle Swarm Optimization (MCMOPSO) algorithm is presented for multi-objective optimization. The algorithm consists of an improved leader selection scheme called multi-competition leader selection. Under this scheme, particles move to the winner among the elite particles for the social cognitive by comparing the nearest angle or the farthest angle of several randomly selected elite particles. Besides, as the inertia weight plays an important role in controlling the previous velocity of each particle, the competitive mechanism is applied to the inertia weight in order to investigate for the most suitable balance between the exploration and exploitation abilities of the algorithm during the search process. The experimental results show that the proposed algorithm outperforms four other popular multi-objective particle swarm optimization algorithms most of the time on thirty-seven benchmarks in terms of inverted generational distance. Furthermore, the proposed algorithm is applied to the signalized traffic problem to optimize the effective green time of each phase, and the proposed algorithm performs better than other MOPSO algorithms for the traffic problem in terms of hypervolume.

摘要

提出了一种改进的竞争机制多目标粒子群算法（MCMOPSO），用于多目标优化。该算法由一种改进的领导者选择方案（称为多竞争领导者选择）组成。在此方案下，通过比较几个随机选择的精英粒子的最近角度或最远角度，粒子在社会认知的精英粒子中向赢家移动。此外，由于惯性权重在控制每个粒子的先前速度中起着重要作用，因此将竞争机制应用于惯性权重，以研究搜索过程中算法的探索能力与开发能力之间最合适的平衡。 。实验结果表明，在倒置世代距离方面，该算法大部分时间都在其他37种基准上优于其他四种流行的多目标粒子群优化算法。此外，将所提出的算法应用于信号交通问题，以优化每个阶段的有效绿灯时间，并且在交通量方面，该算法的性能优于其他MOPSO算法。