An Improved Moth-Flame Optimization algorithm with hybrid search phase

In order to solve real-life problems, several metaheuristic optimization algorithms have been developed. The Moth-Flame Optimization (MFO) algorithm is a search algorithm based on a mechanism called transverse orientation. In this mechanism, the moths tend to maintain a fixed angle with respect to t...

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Vydáno v:Knowledge-based systems Ročník 191; s. 105277
Hlavní autoři: Pelusi, Danilo, Mascella, Raffaele, Tallini, Luca, Nayak, Janmenjoy, Naik, Bighnaraj, Deng, Yong
Médium: Journal Article
Jazyk:angličtina
Vydáno: Amsterdam Elsevier B.V 05.03.2020
Elsevier Science Ltd
Témata:
Algorithms
Butterflies & moths
Constrained optimization
Convergence
Degeneration
Evolutionary algorithms
Exploitation
Heuristic methods
Moon
Moth flame optimization
Moths
Nature-inspired algorithms
Optimization
Optimization algorithms
Search algorithms
Moth flame optimization
Evolutionary algorithms
Nature-inspired algorithms
Constrained optimization
ISSN:0950-7051, 1872-7409
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Shrnutí:In order to solve real-life problems, several metaheuristic optimization algorithms have been developed. The Moth-Flame Optimization (MFO) algorithm is a search algorithm based on a mechanism called transverse orientation. In this mechanism, the moths tend to maintain a fixed angle with respect to the moon. MFO suffers from the degeneration of the global search capability and convergence speed. To overcome these imperfections, an Improved Moth-Flame Optimization (IMFO) algorithm is proposed. The main novelty of the proposed approach is the definition of a hybrid phase between exploration and exploitation. This phase is characterized by a fitness depended weight factor for updating the moths positions. IMFO is tested on selected benchmark functions, CEC2014 test functions and 6 design problems, and compared with recent well-known optimization algorithms. The results show that IMFO achieves the best results with respect to the comparison algorithms in terms of search capability and convergence performances. •Definition of a hybrid phase to achieve a good trade-off between exploration and exploitation phases.•Introduction of a dynamic crossover mechanism for reproducing flames to enhance the population diversity.•Definition of fitness depended weight factor for moths positions calculations to improve the exploitation phase.
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ISSN:0950-7051
1872-7409
DOI:10.1016/j.knosys.2019.105277