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

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.

Salp swarm algorithm based on golden section and adaptive and its application in target tracking

“…The time complexity of the ISSA is the same as that of the original SSA. The specific experimental results are shown in In order to compare, in 19 benchmark test functions, the ISSA, SSA, chaotic salp swarm algorithm (CSSA) [35], mothflame optimization algorithm (MFO) [36], grasshopper optimization algorithm (GOA) [37] and ant lion optimization (ALO) [38] 30 times simulation experiment was carried out. The experimental results include the average and standard deviation of the optimal values obtained 30 times.…”