An improved moth flame optimization algorithm based on modified dynamic opposite learning strategy

Moth flame optimization (MFO) algorithm is a relatively new nature-inspired optimization algorithm based on the moth’s movement towards the moon. Premature convergence and convergence to local optima are the main demerits of the algorithm. To avoid these drawbacks, a modified dynamic opposite learni...

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Bibliographic Details
Published in:The Artificial intelligence review Vol. 56; no. 4; pp. 2811 - 2869
Main Authors: Sahoo, Saroj Kumar, Saha, Apu Kumar, Nama, Sukanta, Masdari, Mohammad
Format: Journal Article
Language:English
Published: Dordrecht Springer Netherlands 01.04.2023
Springer Nature B.V
Subjects:
Algorithms
Artificial Intelligence
Computer Science
Convergence
Design engineering
Learning
Learning strategies
Machine learning
Measurement
Measures
Optimization
Optimization algorithms
Performance enhancement
Rank tests
Robustness
Robustness (mathematics)
Statistical tests
Tests
World problems
Benchmark functions
Dynamic opposite learning strategy
Swarm intelligence
Moth flame optimization algorithm
Simple quadratic interpolation
ISSN:0269-2821, 1573-7462
Online Access:Get full text
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Summary:Moth flame optimization (MFO) algorithm is a relatively new nature-inspired optimization algorithm based on the moth’s movement towards the moon. Premature convergence and convergence to local optima are the main demerits of the algorithm. To avoid these drawbacks, a modified dynamic opposite learning-based MFO algorithm (m-DMFO) is presented in this paper, incorporating a modified dynamic opposite learning (DOL) strategy. To validate the performance of the proposed m-DMFO algorithm, it is tested via twenty-three benchmark functions, IEEE CEC’2014 test functions and compared with a wide range of optimization algorithms. Moreover, Friedman rank test, Wilcoxon rank test, convergence analysis, and diversity measurement have been conducted to measure the robustness of the proposed m-DMFO algorithm. The numerical results show that, the proposed m-DMFO algorithm achieved superior results in more than 90% occasions. The proposed m-DMFO achieves the best rank in Friedman rank test and Wilcoxon rank test respectively. In addition, four engineering design problems have been solved by the suggested m-DMFO algorithm. According to the results, it achieves extremely impressive results, which also illustrates that the algorithm is qualified in solving real-world problems. Analyses of numerical results, diversity measure, statistical tests and convergence results ensure the enhanced performance of the proposed m-DMFO algorithm.
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ISSN:0269-2821
1573-7462
DOI:10.1007/s10462-022-10218-0