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An Improved Moth-Flame Optimization algorithm with hybrid search phase
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DOI：10.1016/j.knosys.2019.105277.png)
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Abstract
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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. (C) 2019 Elsevier B.V. All rights reserved.
Keywords:
Evolutionary algorithms
Moth flame optimization
Constrained optimization
Nature-inspired algorithms
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