基于多种群动态协同的多目标粒子群算法

针对复杂的多目标问题,文中提出了一种基于多种群动态协同的多目标粒子群算法。该算法设置多个种群同时进行独立搜索,从而有效提高算法的搜索能力。此外,为进一步保证种群多样性,该算法利用动态聚类策略将种群划分为两个子群,并改变子种群的更新方式。通过动态学习样本和差分变异,进一步避免算法陷入局部最优。经过对一系列标准测试函数进行仿真,验证了该算法在多目标问题上的有效性。将该算法与5种现存算法进行比较,结果显示该算法的多样性和收敛性均具有明显的优势。

Multi-Objective Particle Swarm Optimization Based on Multi-population Dynamic Cooperation

Aiming at the complex multi-objective problems, a multi-objective particle swarm optimization algorithm based on multi-population dynamic cooperation was proposed. In this algorithm, multiple populations were set up to search independently at the same time, thereby effectively improving the search ability of the algorithm. In addition, in order to further ensure the diversity of the population, the algorithm divided the population into two sub-populations by dynamic clustering strategy, and changed the updating mode of subpopulations. Furthermore, dynamic learning samples and differential mutation were used to further prevent the algorithm from falling into local optimum. A series of standard test functions were simulated to verify the effectiveness of the algorithm on multi-objective problems. In addition, comparing the algorithm with five existing algorithms, the results showed that the algorithm has obvious advantages in diversity and convergence.