Comprehensive learning particle swarm optimization (CLPSO) and enhanced CLPSO (ECLPSO) are two literature metaheuristics for global optimization. ECLPSO significantly improves the exploitation and convergence performance of CLPSO by perturbation-based exploitation and adaptive learning probabilities. However, ECLPSO still cannot locate the global optimum or find a near-optimum solution for a number of problems. In this paper, we study further bettering the exploration performance of ECLPSO. We propose to assign an independent inertia weight and an independent acceleration coefficient corresponding to each dimension of the search space, as well as an independent learning probability for each particle on each dimension. Like ECLPSO, a normative interval bounded by the minimum and maximum personal best positions is determined with respect to each dimension in each generation. The dimensional independent maximum velocities, inertia weights, acceleration coefficients, and learning probabilities are proposed to be adaptively updated based on the dimensional normative intervals in order to facilitate exploration, exploitation, and convergence, particularly exploration. Our proposed metaheuristic, called adaptive CLPSO (ACLPSO), is evaluated on various benchmark functions. Experimental results demonstrate that the dimensional independent and adaptive maximum velocities, inertia weights, acceleration coefficients, and learning probabilities help to significantly mend ECLPSO’s exploration performance, and ACLPSO is able to derive the global optimum or a near-optimum solution on all the benchmark functions for all the runs with parameters appropriately set.

中文翻译：

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具有尺寸独立和自适应参数的增强型综合学习粒子群算法

全面学习粒子群优化（CLPSO）和增强型CLPSO（ECLPSO）是用于全局优化的两种文献元启发式方法。ECLPSO通过基于扰动的利用和自适应学习概率，大大提高了CLPSO的利用和融合性能。但是，ECLPSO仍然无法找到全局最优值，也无法为许多问题找到接近最优的解决方案。在本文中，我们将进一步研究改善ECLPSO的勘探性能。我们建议为搜索空间的每个维度分配一个独立的惯性权重和一个独立的加速度系数，并为每个维度上的每个粒子分配一个独立的学习概率。像ECLPSO一样，相对于每一代中的每个维度，确定由最小和最大个人最佳位置所界定的标准间隔。提出了基于尺寸标准间隔的，与尺寸无关的最大速度，惯性权重，加速度系数和学习概率的适应性更新，以便于探索，开发和收敛，尤其是探索。我们提出的称为启发式CLPSO（ACLPSO）的元启发式方法在各种基准功能上进行了评估。实验结果表明，尺寸独立和自适应的最大速度，惯性权重，加速度系数和学习概率有助于显着改善ECLPSO的勘探性能，