Abstract Firefly algorithm (FA) is a new random swarm search optimization algorithm that is modeled after movement and the mutual attraction of flashing fireflies. The number of fitness comparisons and attractions in the FA varies depending on the attraction model. A large number of attractions can induce search oscillations, while a small number of attractions can cause early convergence and a large number of fitness comparisons that can add to the computational time complexity. This study aims to offer H-GA–FA, a hybrid algorithm that combines two metaheuristic algorithms, the genetic algorithm (GA) and the FA, to overcome the flaws of the FA and combine the benefits of both algorithms to solve engineering design problems (EDPs). In this hybrid system, which blends the concepts of GA and FA, individuals are formed in the new generation not only by GA processes but also by FA mechanisms to prevent falling into local optima, introduce sufficient diversity of the solutions, and make equilibrium between exploration/exploitation trends. On the other hand, to deal with the violation of constraints, a chaotic process was utilized to keep the solutions feasible. The proposed hybrid algorithm H-GA–FA is tested by well-known test problems that contain a set of 17 unconstrained multimodal test functions and 7 constrained benchmark problems, where the results have confirmed the superiority of H-GA–FA overoptimization search methods. Finally, the performance of the H-GA–FA is also investigated on many EDPs. Computational results show that the H-GA–FA algorithm is competitive and better than other optimization algorithms that solve EDPs.

中文翻译：

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一种用于工程设计问题的遗传-萤火虫混合算法

摘要 萤火虫算法（FA）是一种新的随机群搜索优化算法，它以萤火虫的运动和闪烁的相互吸引为模型。FA 中的健身比较和吸引力的数量因吸引力模型而异。大量的景点会引起搜索振荡，而少量的景点会导致早期收敛和大量的适应度比较，这会增加计算时间复杂度。本研究旨在提供 H-GA-FA，一种结合了遗传算法（GA）和 FA 两种元启发式算法的混合算法，以克服 FA 的缺陷，并结合两种算法的优点来解决工程设计问题。 EDP​​）。在这个混合了 GA 和 FA 概念的混合系统中，新一代个体不仅通过 GA 过程而且通过 FA 机制形成，以防止陷入局部最优，引入足够的解决方案多样性，并在探索/开发趋势之间取得平衡。另一方面，为了处理违反约束的问题，使用混沌过程来保持解决方案的可行性。所提出的混合算法 H-GA-FA 通过包含 17 个无约束多模态测试函数和 7 个约束基准问题的众所周知的测试问题进行测试，结果证实了 H-GA-FA 过度优化搜索方法的优越性。最后，还在许多 EDP 上研究了 H-GA-FA 的性能。计算结果表明，H-GA-FA 算法具有竞争力，并且优于其他解决 EDP 的优化算法。