An Energy-segmented Moth-flame Optimization Algorithm for Function Optimization and Performance Measures Analysis

Yuanfei Wei, Pengchuan Wang, Qifang Luo, Yongquan Zhou
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引用次数: 1

Abstract

The moth-flame optimization algorithm (MFO) is a novel metaheuristic algorithm for simulating the lateral positioning and navigation mechanism of moths in nature, and it has been successfully applied to various optimization problems. This paper segments the flame energy of MFO by introducing the energy factor from the Harris hawks optimization algorithm, and different updating methods are adopted for moths with different flame-detection abilities to enhance the exploration ability of MFO. A new energy-segmented moth-flame optimization algorithm (ESMFO) is proposed and is applied on 21 benchmark functions and an engineering design problem. The experimental results show that the ESMFO yields very promising results due to its enhanced exploration, exploitation, and convergence capabilities, as well as its effective avoidance of local optima, and achieves better performance than other the state-of-the-art metaheuristic algorithms in terms of the performance measures.
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一种用于功能优化和性能度量分析的能量分段蛾焰优化算法
飞蛾火焰优化算法(MFO)是一种模拟自然界飞蛾横向定位和导航机制的新型元启发式算法,已成功应用于各种优化问题。本文通过引入Harris hawks优化算法中的能量因子对MFO的火焰能量进行分割,并对具有不同火焰探测能力的飞蛾采用不同的更新方法,增强MFO的探测能力。提出了一种新的能量分段蛾焰优化算法(ESMFO),并应用于21个基准函数和一个工程设计问题。实验结果表明,ESMFO具有较强的探索、开发和收敛能力,并能有效避免局部最优,取得了较好的效果,在性能指标上优于其他最先进的元启发式算法。
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