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An enhanced moth flame optimization with mutualism scheme for function optimization
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AI
DOI：10.1007/s00500-021-06560-0.png)
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Abstract
En 中文
Nature-inspired meta-heuristics have demonstrated superior efficiency in the solution of complicated nonlinear optimization problems than conventional techniques. In this article, an enhanced moth flame optimization (EMFO) is designed using the mutualism phase from the symbiotic organism search (SOS) algorithm. The suggested approach is examined on 36 classical benchmark functions taken from literature. The outputs of EMFO are compared with the latest meta-heuristic algorithms and variants of the MFO algorithm. The comparison results indicate that our proposed method is competitive from the compared methods. Also, the Friedman rank test is used to evaluate the new algorithm's efficiency, and it is found that the rank of EMFO is superior. Finally, EMFO is being applied to solve seven real-world problems, and the outcomes of the proposed algorithm were found to be satisfactory.
Keywords:
Optimization
Moth flame optimization
Mutualism phase
Benchmark functions
Friedman rank test
Real-world problem
Algorithm
Particle swarm optimization
Genetic algorithm
Journal
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2.5
Papers:
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Citations:
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