The Moth Flame Optimization(MFO)algorithm shows decent performance results compared to other meta-heuristic algo-rithms for tackling non-linear constrained global optimization problems.However,it still suffers from obtaining quality solution and slow convergence speed.On the other hand,the Butterfly Optimization Algorithm(BOA)is a comparatively new algorithm which is gaining its popularity due to its simplicity,but it also suffers from poor exploitation ability.In this study,a novel hybrid algorithm,h-MFOBOA,is introduced,which integrates BOA with the MFO algorithm to overcome the shortcomings of both the algorithms and at the same time inherit their advantages.For performance evaluation,the pro-posed h-MFOBOA algorithm is applied on 23 classical benchmark functions with varied complexity.The tested results of the proposed algorithm are compared with some well-known traditional meta-heuristic algorithms as well as MFO variants.Friedman rank test and Wilcoxon signed rank test are employed to measure the performance of the newly introduced algo-rithm statistically.The computational complexity has been measured.Moreover,the proposed algorithm has been applied to solve one constrained and one unconstrained real-life problems to examine its problem-solving capability of both type of problems.The comparison results of benchmark functions,statistical analysis,real-world problems confirm that the proposed h-MFOBOA algorithm provides superior results compared to the other conventional optimization algorithms.

Department of Mathematics,National Institute of Technology,Agartala,Tripura 799046,India

[1]Saroj Kumar Sahoo;Apu Kumar Saha-.A Hybrid Moth Flame Optimization Algorithm for Global Optimization)[J].仿生工程学报（英文版）,2022(05):1522-1543

Moth,MFOBOA

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