A Modernized Moth Flame Optimization Algorithm for Higher Dimensional Problems


  • Saroj Kumar Sahoo 2Department of Mathematics, National Institute of Technology Agartala
  • Apu Kumar Saha 2Department of Mathematics, National Institute of Technology Agartala


Moth Flame Optimization Algorithm, Swarm Intelligence, Fibonacci Search Method, Benchmark Functions


Moth flame optimization (MFO) algorithm is a relatively new nature-inspired optimization algorithm based on the moth’s movement towards the moon. Premature convergence and convergence to local optima are the main demerits of the basic MFO algorithm. To avoid these drawbacks, a new variant of MFO algorithm, namely a modernized MFO (M-MFO) algorithm is presented in this paper. Firstly, we added a self-adaptive levy distribution method before the position update phase of the MFO algorithm to enhance the search region. Secondly, we introduce a new type of parameter to strike a better balance between diversification and intensification. Third, we incorporate the Fibonacci search technique into the MFO algorithm after the position update phase to get around the problem of local optimal solutions and speed up convergence. The proposed M-MFO is verified by testing it on fifteen benchmark functions in higher dimensions (1000, for example), undergoing statistical experiments, and solving engineering design problems, and then comparing the results to those obtained by using other, more conventional optimization algorithms. The experimental results show that the proposed M-MFO algorithm outperforms competing stochastic algorithms in terms of solution quality and convergence rate. This encourages further research into topics such as multi-objective optimization, vehicle routing, job shop planning, and image segmentation.


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