Comparison of Metaheuristic Methods for Solving Constrained Problems
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Abstract
Optimization algorithms and metaheuristic algorithms are effective techniques for solving unconstrained and constrained engineering problems. Metaheuristic algorithms are iterative search processes that efficiently explore the solution space. These methods effectively and efficiently search for solutions that are close to the optimal solution. The purpose of this research is to examine the efficiency of three algorithms: Flower Pollination Algorithm (FPA), Elevator Kinematic Optimization (EKO), and Rider Optimization Algorithm (ROA) in solving unconstrained and constrained problems. To compare the efficiency of these algorithms, we use the average, standard deviation, processing time, and signal-to-noise ratio. The experimental results demonstrate that ROA significantly outperforms the other two metaheuristic methods in terms of precision and quality of results, as well as requiring fewer searches to achieve the best solution. In this paper, the ROA can find better optimal solutions, but it gives higher standard deviation values. Additionally, ROA is easier to apply and does not require parameter tuning, while the FPA and EKO methods need parameter tuning for optimal performance.
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References
Hao, J.K. and Solnon, C. 2020. Metaheuristics and artificial intelligence. In A Guided Tour of Artificial Intelligence Research. Springer, Berlin, 27–52.
Kaveh, M. and Mesgari, M.S. 2023. Application of metaheuristic algorithms for training neural networks and deep learning architectures: a comprehensive review. Neural Processing Letters, 55, 4519–4622, https://doi.org/10.1007/s11063-022-11055-6.
Pavai, G. and Geetha, T.V. 2019. New crossover operators using dominance and co-dominance principles for faster convergence of genetic algorithms. Soft Computing, 23, 3661–3686, https://doi.org/10.1007/s00500-018-3016-1.
Storn, R. and Price, K. 1997. Differential evolution - a simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization, 11(4), 341–359.
Kennedy, J. and Eberhart, R. 1995. Particle Swarm Optimization. Proceedings of IEEE International Conference on Neural Networks, 4, 1942–1948.
Dorigo, M., Maniezzo, V. and Colorni, A. 1996. Ant system: optimisation by a colony of cooperating agents. IEEE Transactions on Systems, Man, and Cybernetics Part B, 26, 29-41, http://doi.org/10.1109/3477.484436.
Hansen, P., Mladenović, N. and Moreno Pérez, J.A. 2008. Variable neighbourhood search. Metaheuristic procedures for training neutral networks methods and applications, 319-360, https://doi.org/10.1007/s10288-008-0089-1.
Yang, X.S. 2012. Flower pollination algorithm for global optimization. lecture notes in computer science, 7445, 240-249.
Luangpaiboon, P., Aungkulanon, P., Ruekkasaem, L. and Montemanni, R. 2022. An elevator kinematics optimization algorithm based on a large neighborhood search for optimizing simulated industrial problems. Proceedings of the 2022 9th International Conference on Industrial Engineering and Applications (Europe) (ICIEA-2022-Europe). Association for Computing Machinery, New York, NY, USA, 92–97.
Binu, D. and Kariyappa, B.S. 2019. RideNN: A new rider optimization algorithm-based neural network for fault diagnosis in analog circuits. IEEE Transactions on Instrumentation and Measurement, 68(1), 2–26, https://doi.org/10.1109/TIM.2018.2836058.
Glover, F. and Kochenberger, G.A. 2003. Handbook of metaheuristics. Springer New York, New York.
Bandyopadhyay, D. 2008. A simulated annealing-based multi objective optimization algorithm: AMOSA. IEEE Transactions on Evolutionary Computation, 12(3), 269-283, https://doi.org/10.1109/TEVC.2007.900837.
Cvijovic, D. and Klinowski, J. 1995. Taboo search: an approach to the multiple minima problem. Science, 267, 664-666, https://doi.org/10.1126/science.267.5198.664.
Yang, X.S. 2014. Nature Inspired Optimisation Algorithm. Elsevier Inc., London.
Kumpanya, D. 2018. Parameter identification of DC motor model by flower pollination algorithm. RMUTSB ACADEMIC JOURNAL, 6(2), 207-219.
Aungkulanon, P., Luangpaiboon, P. and Montemanni, R. 2018. An elevator kinematics optimisation method for aggregate production planning based on fuzzy MOLP model. International Journal of Mechanical Engineering and Robotics Research, 7(4), 422-427, https://doi.org/10.18178/ijmerr.7.4.422-427.
Luangpaiboon, P. and Juttijudata, S. 2023. Adaptive elevator kinematics optimization based dual response algorithm for determining proper levels in plaster milling process parameters. Scientific Reports, 13, 8855, https://doi.org/10.1038/s41598-023-35119-2.
Wang, G., Yuan, Y. and Guo, W. 2019. An improved rider optimization algorithm for solving engineering optimization problems. IEEE Access, 7, 80570–80576, https://doi.org/10.1109/ACCESS.2019.2923468.
Kumar, R., Rohitash, K. and Banyal. 2021. Rider Optimization Algorithm (ROA): An optimization solution for engineering problem. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 12(12), 3197-3201, https://doi.org/10.17762/turcomat.v12i12.7994.
Chai-Ead, N., Aungkulanon, P. and Luangpaiboon, P. 2012. Nature-Inspired Algorithms of Bees, Firefly and Bat for Noisy Non-Linear Optimisation Problems. IAENG Transactions on Engineering Technologies, 7, 62-77.
Pansare, V.B. and Kavade, M.V. 2012. Optimization of cutting parameters in multipass turning operation using ant colony algorithm. International Journal of Engineering Science & Advanced Technology, 2(4), 955-960, https://doi.org/10.1142/9789814390019_0005.
Shivakoti, I., Diyaley, S., Kibria, G. and Pradhan, B.B. 2012. Analysis of Material Removal Rate using Genetic Algorithm Approach. International Journal of Scientific & Engineering Research, 3(5), 1-6.
Khan, Z., Prasad, L.B. and Singhl, T. 1997. Machine Condition Optimisation by Genetic Algorithms and Simulated. Computers & Operations Research, 24(7), 647-657, https://doi.org/10.1016/S0305-0548(96)00077-9.
Mahdavi, M., Fesanghary, M. and Damangir, E. 2007. An improved harmony search algorithm for solving optimization problems. Applied Mathematics and Computation. 188(2), 1567-1579, https://doi.org/10.1016/j.amc.2006.11.033.
Ruekkasaem, L. and Aungkulanon, P. 2020. Comparison of forecasting methods for Aggregate Production Planning in Cleanroom Apparel Factory. Phranakhon Rajabhat Research Journal (Science and Technology), 15(1), 86-100.