The moth search algorithm (MS) is a novel intelligent optimization algorithm based on moth population behavior, which can solve many problems in different fields. However, the algorithm is easy to fall into local optimization when solving complex optimization problems. This study develops a new hybrid moth search-fireworks algorithm (MSFWA) to solve numerical and constrained engineering optimization problems. The explosion and mutation operators from the fireworks algorithm are introduced into the MS, which not only preserves the advantages of fast convergence and strong exploitation capability of the algorithm, but also significantly enhances the exploration capability. The performance of the MSFWA is tested using 23 benchmark functions. The hybrid algorithm is superior to other highly advanced metaheuristic algorithms for most benchmark functions, demonstrating the characteristics of fast convergence and high stability. Finally, the ability of the MSFWA to solve practical constrained problems is evaluated on six well-known engineering application problems. Compared with other optimization algorithms, the MSFWA is very competitive in its solution of these complex and constrained practical problems.

中文翻译：

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基于飞蛾搜索和烟花算法的高效混合算法求解数值约束工程优化问题

蛾类搜索算法（Moth search algorithm，MS）是一种基于蛾类种群行为的新型智能优化算法，可以解决不同领域的许多问题。但该算法在求解复杂优化问题时容易陷入局部优化。本研究开发了一种新的混合飞蛾搜索烟花算法 (MSFWA) 来解决数值和受约束的工程优化问题。将烟花算法的爆炸和变异算子引入到MS中，既保持了算法收敛速度快、开发能力强的优点，又显着增强了探索能力。MSFWA 的性能使用 23 个基准函数进行测试。对于大多数基准函数，混合算法优于其他高度先进的元启发式算法，表现出收敛速度快和稳定性高的特点。最后，在六个著名的工程应用问题上评估了 MSFWA 解决实际约束问题的能力。与其他优化算法相比，MSFWA 在解决这些复杂且受约束的实际问题方面极具竞争力。