A Modernized Moth Flame Optimization Algorithm for Higher Dimensional Problems
Keywords:
Moth Flame Optimization Algorithm, Swarm Intelligence, Fibonacci Search Method, Benchmark FunctionsAbstract
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.
References
Abbassi, R., Abbassi, A., Heidari, A. A., & Mirjalili, S. (2019). An efficient salp swarm-inspired algorithm for parameters identification of photovoltaic cell models. Energy Conversion and Management, 179, 362–372.
McCarthy, J. F. (1989). Block-conjugate-gradient method. Physical Review D, 40(6), 2149.
Wu, G., Pedrycz, W., Suganthan, P. N., & Mallipeddi, R. (2015). A variable reduction strategy for evolutionary algorithms handling equality constraints. Applied Soft Computing, 37, 774–786.
Kennedy, J., Eberhart, R. Particle swarm optimization. In: Proceedings of ICNN’95—international conference on neural networks, Perth, Australia, 1995, 1942–1948.
Storn, R., & Price, K. (1997). Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization, 11(4), 341–359.
Yang, X.-S. (2012). Flower pollination algorithm for global optimization. International Conference on Unconventional Computing and Natural Computation, 240–249.
Arora, S., & Singh, S. (2015). Butterfly algorithm with levy flights for global optimization. 2015 International Conference on Signal Processing, Computing and Control (ISPCC), 220–224.
Mirjalili, S. (2015). Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm. Knowledge-Based Systems, 89, 228–249.
Singh, P., & Prakash, S. (2019). Optical network unit placement in Fiber-Wireless (FiWi) access network by Whale Optimization Algorithm. Optical Fiber Technology, 52, 101965.
Mohanty, B. (2019). Performance analysis of moth flame optimization algorithm for AGC system. International Journal of Modelling and Simulation, 39(2), 73–87.
Khairuzzaman, A. K. M., & Chaudhury, S. (2020). Modified moth-flame optimization algorithm-based multilevel minimum cross entropy thresholding for image segmentation: international journal of swarm intelligence research, 11(4), 123–139.
Gupta, D., Ahlawat, A. K., Sharma, A., & Rodrigues, J. J. P. C. (2020). Feature selection and evaluation for software usability model using modified moth-flame optimization. Computing, 102(6), 1503–1520.
Muduli, D., Dash, R., & Majhi, B. (2020). Automated breast cancer detection in digital mammograms: A moth flame optimization-based ELM approach. Biomedical Signal Processing and Control, 59, 101912.
Hongwei, L., Jianyong, L., Liang, C., Jingbo, B., Yangyang, S., & Kai, L. (2019). Chaos-enhanced moth-flame optimization algorithm for global optimization. Journal of Systems Engineering and Electronics, 30(6), 1144–1159.
Xu, Y., Chen, H., Luo, J., Zhang, Q., Jiao, S., & Zhang, X. (2019). Enhanced Moth-flame optimizer with mutation strategy for global optimization. Information Sciences, 492, 181–203.
Xu, Y., Chen, H., Heidari, A. A., Luo, J., Zhang, Q., Zhao, X., & Li, C. (2019). An efficient chaotic mutative moth-flame-inspired optimizer for global optimization tasks. Expert Systems with Applications, 129, 135–155.
Kaur, K., Singh, U., & Salgotra, R. (2020). An enhanced moth flame optimization. Neural Computing and Applications, 32(7), 2315–2349.
Tumar, I., Hassouneh, Y., Turabieh, H., & Thaher, T. (2020). Enhanced binary moth flame optimization as a feature selection algorithm to predict software fault prediction. IEEE Access, 8, 8041–8055.
Gu, W., & Xiang, G. (2021). Improved Moth Flame Optimization with Multioperator for solving real-world optimization problems. 2021 IEEE 5th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), 5, 2459–2462.
Ma, L., Wang, C., Xie, N., Shi, M., Ye, Y., & Wang, L. (2021). Moth-flame optimization algorithm based on diversity and mutation strategy. Applied Intelligence.
Sahoo, S. K., Saha, A. K., Sharma, S., Mirjalili, S., & Chakraborty, S. (2022). An enhanced moth flame optimization with mutualism scheme for function optimization. Soft Computing, 26(6), 2855–2882.
Yu, C., Heidari, A. A., & Chen, H. (2020). A quantum-behaved simulated annealing algorithm-based moth-flame optimization method. Applied Mathematical Modelling, 87, 1–19.
Yang, X.-S. (2010). Nature-inspired metaheuristic algorithms. Luniver press.
Ramaprabha, R. (2012). Maximum power point tracking of partially shaded solar PV system using modified Fibonacci search method with fuzzy controller. 12.
Yazıcı, İ., Yaylacı, E. K., Cevher, B., Yalçın, F., & Yüzkollar, C. (2021). A new MPPT method based on a modified Fibonacci search algorithm for wind energy conversion systems. Journal of Renewable and Sustainable Energy, 13(1), 013304.
Chen, C. L., Wang, X., Yu, H., Wang, M., Chen, H. (2021). Dealing with multi-modality using synthesis of Moth-flame optimizer with sine cosine mechanisms. Mathematics and Computers in Simulation, 188, 291–318.
Elsakaan, A. A., El-Sehiemy, R. A., Kaddah, S. S., & Elsaid, M. I. (2018). An enhanced moth-flame optimizer for solving non-smooth economic dispatch problems with emissions. Energy, 157, 1063–1078.
Milton F (1937). The use of ranks to avoid the assumption of normality implicit in the analysis of variance. Journal of the American Statistical Association. 32: 675–701.
