A Hybrid Moth Flame Optimization Algorithm for Global Optimization

The Moth Flame Optimization (MFO) algorithm shows decent performance results compared to other meta-heuristic algorithms 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 Butt...

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Bibliographic Details
Published in:Journal of bionics engineering Vol. 19; no. 5; pp. 1522 - 1543
Main Authors: Sahoo, Saroj Kumar, Saha, Apu Kumar
Format: Journal Article
Language:English
Published: Singapore Springer Nature Singapore 01.09.2022
Subjects:
Artificial Intelligence
Biochemical Engineering
Bioinformatics
Biomaterials
Biomedical Engineering and Bioengineering
Biomedical Engineering/Biotechnology
Engineering
Research Article
Benchmark functions
Friedman rank test
Moth flame optimization algorithm
Bio-inspired
Butterfly optimization algorithm
ISSN:1672-6529, 2543-2141
Online Access:Get full text
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Summary:The Moth Flame Optimization (MFO) algorithm shows decent performance results compared to other meta-heuristic algorithms 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 proposed 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 algorithm 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.
ISSN:1672-6529
2543-2141
DOI:10.1007/s42235-022-00207-y