Abstract
Moth-flame optimization (MFO) algorithm inspired by the transverse orientation of moths toward the light source is an effective approach to solve global optimization problems. However, the MFO algorithm suffers from issues such as premature convergence, low population diversity, local optima entrapment, and imbalance between exploration and exploitation. In this study, therefore, an improved moth-flame optimization (I-MFO) algorithm is proposed to cope with canonical MFO’s issues by locating trapped moths in local optimum via defining memory for each moth. The trapped moths tend to escape from the local optima by taking advantage of the adapted wandering around search (AWAS) strategy. The efficiency of the proposed I-MFO is evaluated by CEC 2018 benchmark functions and compared against other well-known metaheuristic algorithms. Moreover, the obtained results are statistically analyzed by the Friedman test on 30, 50, and 100 dimensions. Finally, the ability of the I-MFO algorithm to find the best optimal solutions for mechanical engineering problems is evaluated with three problems from the latest test-suite CEC 2020. The experimental and statistical results demonstrate that the proposed I-MFO is significantly superior to the contender algorithms and it successfully upgrades the shortcomings of the canonical MFO.
Highlights
In the majority of real-world optimization problems, a large number of decision variables are interacted with together, which is a very time-consuming process for finding an exact solution [1,2,3,4,5,6,7]
If the current position of Entropy 2021, 23, 1637 each moth is not better than its memory, the moth is considered to be a trapped moth, and the adapted wandering around search (AWAS) strategy is employed to possibly free it from local optima by performing some random short flights, which leads to amelioration of the premature convergence
The proposed improved moth-flame optimization (I-Moth-flame optimization (MFO)) algorithm is boosted using a moth memory mechanism and the adapted wandering around search (AWAS) strategy to
Summary
In the majority of real-world optimization problems, a large number of decision variables are interacted with together, which is a very time-consuming process for finding an exact solution [1,2,3,4,5,6,7]. Grey wolf optimizer (GWO) [58], chimp optimization algorithm (ChOA) [59], and gorilla troops optimizer (GTO) [60] are inspired by the behavior of terrestrial animals to solve optimization problems Despite their simplicity and broad use, they may suffer from common drawbacks such as low population diversity, sinking into local optimum, and premature convergence problems. MFO and its variants cannot satisfy the needs of the optimization process for challenging problems, and they still suffer from some weaknesses such as low population diversity [97,98], premature convergence, local optima trapping [99,100], and imbalance between exploration and exploitation [101].
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