An enhanced moth flame optimization with mutualism scheme for function optimization

Saroj Kumar Sahoo, Apu Kumar Saha, Sushmita Sharma, Seyedali Mirjalili, Sanjoy Chakraborty

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish
JournalSoft Computing
DOIs
Publication statusAccepted/In press - 2022

Keywords

  • Algorithm
  • Benchmark functions
  • Friedman rank test
  • Genetic algorithm
  • Moth flame optimization
  • Mutualism phase
  • Optimization
  • Particle swarm optimization
  • Real-world problem

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