The diversity of the population is a key factor for particle swarm optimisation (PSO) when dealing with most optimisation problems. The best previously visited positions of each particle are the exemplar in PSO to guide particle swarm to search, and the diversity of the population can be controlled by these best previously visited positions. Base on this idea of to control the diversity of population to improve the performance of PSO, this paper proposes a novel PSO with mutation breeding (MBPSO), which performs a mutation breeding operation periodically, to control the diversity of the population to improve the global optimisation ability. The mutation breeding operation can be divided into two steps: breeding and mutation. The breeding step is to replace all of best previously visited positions of each particle with the global best previously visited position, and the mutation step is to perform a mutation operation for those new generated best previously visited positions. In addition, we adopt a new updating mechanism of the global best previously position to avoid falling into local optimum. The experimental results on a suit of benchmark functions verifies that the proposed PSO is a competitive algorithm when compare with other PSO variants.

中文翻译：

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具有突变育种的新型粒子群优化

在处理大多数优化问题时，种群的多样性是粒子群优化 (PSO) 的关键因素。每个粒子的最佳先前访问位置是PSO中指导粒子群搜索的范例，并且可以通过这些最佳先前访问位置来控制种群的多样性。基于这种控制种群多样性以提高 PSO 性能的思想，本文提出了一种新的具有突变育种的 PSO（MBPSO），它周期性地执行突变育种操作，以控制种群的多样性以提高全局优化能力。突变育种操作可分为育种和突变两步。繁殖步骤是将每个粒子的所有最佳先前访问位置替换为全局最佳先前访问位置，变异步骤是对那些新生成的先前最佳访问位置执行变异操作。此外，我们采用了一种新的全局最佳先前位置更新机制，以避免陷入局部最优。在一套基准函数上的实验结果证实，与其他 PSO 变体相比，所提出的 PSO 是一种有竞争力的算法。