In this paper, a heterogeneous comprehensive learning and dynamic multi-swarm particle swarm optimizer with two mutation operators (HCLDMS-PSO) is presented. In addition, a comprehensive learning (CL) strategy with the global optimal experience of the whole population is conducted to generate an exploitation subpopulation exemplar. However, a modified dynamic multi-swarm (DMS) strategy is specially designed to construct the exploration subpopulation exemplar. In the canonical DMS strategy, it is unfavorable for different sub-swarms to use the same linear decreasing inertia weight parameter. We first propose classifying the DMS sub-swarms at the search level and then constructing a novel nonlinear adaptive decreasing inertia weight for different sub-swarms, introducing a non-uniform mutation operator to enhance its exploration capability. Finally, the gbest of the whole population also adopts a Gaussian mutation operator to avoid falling into the local optimum. The particles of the two subpopulations will update their velocity independently without crippling one another to prevent a loss of diversity. The performance of HCLDMS-PSO is compared with those of 8 other PSO variants and 11 evolutionary algorithms on two classical benchmark optimization problems and a real-world engineering problem. Experimental results demonstrate that the HCLDMS-PSO improves the convergence speed, accuracy, and reliability on most optimization problems.

本文提出了一种具有两个变异算子（HCLDMS-PSO）的异构综合学习和动态多群粒子群优化器。此外，还进行了具有全体人口全球最佳经验的综合学习（CL）策略，以生成开发亚种群示例。但是，专门设计了一种改进的动态多群（DMS）策略来构造勘探亚种群示例。在规范的DMS策略中，对于不同的子群，使用相同的线性递减惯性权重参数是不利的。我们首先提出在搜索级对DMS子群进行分类，然后针对不同的子群构造一种新颖的非线性自适应递减惯性权重，引入一个非均匀突变算子以增强其探索能力。最后，GBEST整个人口也采用高斯变异操作，避免陷入局部最优。两个亚群的粒子将独立更新其速度，而不会互相削弱，以防止多样性损失。在两个经典基准优化问题和一个实际工程问题上，将HCLDMS-PSO的性能与其他8个PSO变体和11个进化算法的性能进行了比较。实验结果表明，HCLDMS-PSO可以提高大多数优化问题的收敛速度，准确性和可靠性。