In this paper, an improved moth-flame optimization algorithm (IMFO) is presented to solve engineering problems. Two novel effective strategies composed of Levy flight and dimension-by-dimension evaluation are synchronously introduced into the moth-flame optimization algorithm (MFO) to maintain a great global exploration ability and effective balance between the global and local search. The search strategy of Levy flight is used as a regulator of the moth-position update mechanism of global search to maintain a good research population diversity and expand the algorithm’s global search capability, and the dimension-by-dimension evaluation mechanism is added, which can effectively improve the quality of the solution and balance the global search and local development capability. To substantiate the efficacy of the enhanced algorithm, the proposed algorithm is then tested on a set of 23 benchmark test functions. It is also used to solve four classical engineering design problems, with great progress. In terms of test functions, the experimental results and analysis show that the proposed method is effective and better than other well-known nature-inspired algorithms in terms of convergence speed and accuracy. Additionally, the results of the solution of the engineering problems demonstrate the merits of this algorithm in solving challenging problems with constrained and unknown search spaces.

中文翻译：

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工程问题的改进飞蛾火焰优化算法

在本文中，提出了一种改进的蛾焰优化算法（IMFO）来解决工程问题。飞蛾火焰优化算法（MFO）中同步引入了由 Levy 飞行和逐维评估组成的两种新颖的有效策略，以保持强大的全局探索能力和全局和局部搜索之间的有效平衡。将Levy Flight的搜索策略作为全局搜索的蛾位更新机制的调节器，保持了良好的研究种群多样性，扩展了算法的全局搜索能力，并加入了逐维评估机制，可以有效提升解决方案的质量，平衡全局搜索和本地开发能力。为了证实增强算法的有效性，然后在一组 23 个基准测试函数上测试所提出的算法。它还用于解决四个经典的工程设计问题，并取得了很大进展。在测试函数方面，实验结果和分析表明，该方法是有效的，并且在收敛速度和精度方面优于其他著名的自然启发算法。此外，工程问题的解决结果证明了该算法在解决具有约束和未知搜索空间的挑战性问题方面的优点。实验结果和分析表明，所提出的方法是有效的，并且在收敛速度和精度方面优于其他著名的自然启发算法。此外，工程问题的解决结果证明了该算法在解决具有约束和未知搜索空间的挑战性问题方面的优点。实验结果和分析表明，所提出的方法是有效的，并且在收敛速度和精度方面优于其他著名的自然启发算法。此外，工程问题的解决结果证明了该算法在解决具有约束和未知搜索空间的挑战性问题方面的优点。