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

• 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; Machine learning; Optimization; Optimization algorithms; Performance enhancement; Rank tests; Robustness (mathematics); Statistical tests; Benchmark functions; Dynamic opposite learning strategy; Swarm intelligence; Moth flame optimization algorithm; Simple quadratic interpolation
• ISSN: 0269-2821; 1573-7462
• DOI: 10.1007/s10462-022-10218-0

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.