The primary aim of this original work is to provide a more in-depth insight into the relations between control parameters adjustments, learning techniques, inner swarm dynamics and possible hybridization strategies for popular swarm metaheuristic Firefly Algorithm (FA). In this paper, a proven method, orthogonal learning, is fused with FA, specifically with its hybrid modification Firefly Particle Swarm Optimization (FFPSO). The parameters of the proposed Orthogonal Learning Firefly Algorithm are also initially thoroughly explored and tuned. The performance of the developed algorithm is examined and compared with canonical FA and above-mentioned FFPSO. Comparisons have been conducted on well-known CEC 2017 benchmark functions, and the results have been evaluated for statistical significance using the Friedman rank test.

中文翻译：

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正交学习萤火虫算法

这项原始工作的主要目的是对流行的群体元启发式萤火虫算法（FA）的控制参数调整，学习技术，内部群动力学和可能的混合策略之间的关系提供更深入的了解。在本文中，将一种行之有效的正交学习方法与FA融合在一起，特别是将其混合改进的萤火虫粒子群优化算法（FFPSO）融合在一起。最初对提出的正交学习萤火虫算法的参数也进行了彻底的探索和调整。检验了所开发算法的性能，并将其与规范FA和上述FFPSO进行了比较。已对著名的CEC 2017基准功能进行了比较，并使用弗里德曼等级检验对结果进行了统计显着性评估。