Yang Meilan, Liu Yanmin, Yang Jie
School of Mathematics and Statistics, Guizhou University, Guiyang 550025, China.
Zunyi Normal University, Zunyi 563002, China.
Comput Intell Neurosci. 2022 Mar 9;2022:1522096. doi: 10.1155/2022/1522096. eCollection 2022.
In recent years, researchers have solved the multi-objective optimization problem by making various improvements to the multi-objective particle swarm optimization algorithm. However, we propose a hybrid multi-objective particle swarm optimization (CCHMOPSO) with a central control strategy. In this algorithm, a disturbance strategy based on boundary fluctuations is first used for the updated new particles and nondominant particles. To prevent the population from falling into a local extremum, some particles are disturbed. Then, when the external archive capacity reaches the extreme value, we use a central control strategy to update the external archive, so that the archive solution gets a good distribution. When the dominance of the current particle and the individual best particle cannot be determined, to enhance the diversity of the population, the combination method of the current particle and the individual best particle can be used to update the individual best particle. The experimental results show that CCHMOPSO is better than four multi-objective particle swarm optimization algorithms and four multi-objective evolutionary algorithms. It is a feasible method for solving multi-objective optimization problems.
近年来，研究人员通过对多目标粒子群优化算法进行各种改进来解决多目标优化问题。然而，我们提出了一种具有中央控制策略的混合多目标粒子群优化算法（CCHMOPSO）。在该算法中，首先对更新后的新粒子和非支配粒子采用基于边界波动的扰动策略。为防止种群陷入局部极值，对一些粒子进行扰动。然后，当外部存档容量达到极值时，我们使用中央控制策略来更新外部存档，以使存档解得到良好的分布。当无法确定当前粒子和个体最优粒子的支配关系时，为增强种群的多样性，可采用当前粒子与个体最优粒子的组合方法来更新个体最优粒子。实验结果表明，CCHMOPSO优于四种多目标粒子群优化算法和四种多目标进化算法。它是解决多目标优化问题的一种可行方法。
