The random drift particle swarm optimization (RDPSO) algorithm is an effective random search technique inspired by the trajectory analysis of the canonical PSO and the free electron model in metal conductors placed in an external electric field. However, like other PSO variants, the RDPSO algorithm also inevitably encounters premature convergence when solving multimodal problems. To address this issue, this paper proposes a novel diversity collaboratively guided (DCG) strategy for the RDPSO algorithm that enhances the search ability of the algorithm. In this strategy, two kinds of diversity measures are defined and modified in a collaborative manner. Specifically, the whole search process of the RDPSO is divided into three phases based on the changes in the two diversity measures. In each phase, different values are selected for the key parameters of the update equation in the RDPSO to make the particle swarm perform different search modes. Consequently, the improved RDPSO algorithm with the DCG strategy (DCG-RDPSO) can maintain its diversity dynamically at a certain level, and thus can search constantly without stagnation until the search process terminates. The performance evaluation of the proposed algorithm is done on the CEC-2013 benchmark suite, in comparison with several versions of RDPSO, different variants of PSO and several non-PSO evolutionary algorithms. Experimental results show that the proposed DCG strategy can significantly improve the performance and robustness of the RDPSO algorithm for most of the multimodal problems. Further experiments on economic dispatch problems also verify the effectiveness of the DCG strategy.

随机漂移粒子群优化 (RDPSO) 算法是一种有效的随机搜索技术，其灵感来自于规范 PSO 的轨迹分析和放置在外部电场中的金属导体中的自由电子模型。但是，与其他 PSO 变体一样，RDPSO 算法在解决多模态问题时也不可避免地会遇到早熟收敛。针对这个问题，本文为RDPSO算法提出了一种新颖的多样性协同引导（DCG）策略，增强了算法的搜索能力。在该策略中，以协作方式定义和修改了两种多样性度量。具体来说，RDPSO的整个搜索过程根据两种分集度量的变化分为三个阶段。在每个阶段，RDPSO中更新方程的关键参数选择不同的值，使粒子群执行不同的搜索模式。因此，改进的带有 DCG 策略的 RDPSO 算法（DCG-RDPSO）可以在一定程度上动态地保持其多样性，从而可以不断搜索而不会停滞，直到搜索过程终止。所提出算法的性能评估是在 CEC-2013 基准套件上完成的，与几个版本的 RDPSO、PSO 的不同变体和几个非 PSO 进化算法进行了比较。实验结果表明，所提出的 DCG 策略可以显着提高 RDPSO 算法对大多数多模态问题的性能和鲁棒性。对经济调度问题的进一步实验也验证了 DCG 策略的有效性。