This article introduces an enhanced particle swarm optimizer (PSO), termed Orthogonal PSO with Mutation (OPSO-m). Initially, it proposes an orthogonal array-based learning approach to cultivate an improved initial swarm for PSO, significantly boosting the adaptability of swarm-based optimization algorithms. The article further presents archive-based self-adaptive learning strategies, dividing the population into regular and elite subgroups. Each subgroup employs distinct learning mechanisms. The regular group utilizes efficient learning schemes derived from three unique archives, which categorize individuals based on their quality levels. Additionally, a mutation strategy is implemented to update the positions of elite individuals. Comparative studies are conducted to assess the effectiveness of these learning strategies in OPSO-m, evaluating its optimization capacity through exploration-exploitation dynamics and population diversity analysis. The proposed OPSO-m model is tested on real-parameter challenges from the CEC 2017 suite in 10, 30, 50, and 100-dimensional search spaces, with its results compared to contemporary state-of-the-art algorithms using a sensitivity metric. OPSO-m exhibits distinguished performance in the precision of solutions, rapidity of convergence, efficiency in search, and robust stability, thus highlighting its superior aptitude for resolving intricate optimization issues.

翻译：本文介绍了一种增强型粒子群优化器（PSO），称为带变异操作的正交粒子群优化算法（OPSO-m）。首先，提出了一种基于正交阵列的学习方法，旨在为PSO培育一个改进的初始种群，从而显著提升基于种群的优化算法的适应能力。文章进一步提出了基于存档的自适应学习策略，将种群划分为常规子群和精英子群。每个子群采用不同的学习机制。常规子群利用从三个独特存档中衍生的高效学习方案，这些存档根据个体的质量水平对其进行分类。此外，还实施了一种变异策略来更新精英个体的位置。通过比较研究评估了这些学习策略在OPSO-m中的有效性，并通过探索-开发动态分析和种群多样性分析来评估其优化能力。所提出的OPSO-m模型在CEC 2017测试集的实数参数优化问题上进行了测试，搜索空间维度包括10、30、50和100维，并使用灵敏度指标将其结果与当前最先进的算法进行了比较。OPSO-m在求解精度、收敛速度、搜索效率和鲁棒稳定性方面均表现出卓越的性能，从而凸显了其在解决复杂优化问题上的突出能力。