A Two-Phase Hybrid GWO:TP-AB Algorithm for Solving Optimization Problems
Optimization Problems
Keywords:
Metaheuristic, Hybrid Algorithm, Grey Wolf Optimizer, Two-Phase AB Algorithm, TP-AB Algorithm, Real-World Applications, Industrial OptimizationAbstract
INTRODUCTION: Population-based algorithms are popular stochastic algorithms used for solving optimization problems. Grey Wolf Optimizer (GWO) proposed in 2014 is one of the most studied algorithms in the past decade. Population-based two-phase trigonometric AB (TP-AB) is a recently proposed algorithm for handling optimization problems.
OBJECTIVES: The objective of this work is to propose one new hybrid algorithm combining the strengths of two better performing algorithms in two different phases. The performance is analysed using popular benchmarks and the results are compared with a few popular algorithms available in the literature.
METHODS: One new two-phase hybrid algorithm is designed by taking GWO in its first phase and the second phase of the TP-AB algorithm in the second phase. In the second phase, the Levy Strategy is introduced which was not in the original TP-AB algorithm.
RESULTS: The performance of the new hybrid GWO:TP-AB algorithm is analysed using 23 classic mathematical functions, 10 numbers of the CEC2019 dataset and 18 real-world engineering problems In addition, to demonstrate its capability to handle higher dimension problems, 13 scalable problems are solved. These include unimodal and multimodal instances with dimensions 30, 100, 500 and 1000.
CONCLUSION: The results demonstrate the better performance of the GWO:TP-AB algorithm when compared to several optimization algorithms of recent times.
References
[1] Abiodun T, Rampersad G, Brinkworth R. Driving industrial digital transformation. Journal of Computer Information Systems. 2023 Nov 2;63(6):1345-61.
[2] Abualigah L, Diabat A, Mirjalili S, Abd Elaziz M, Gandomi AH. The arithmetic optimization algorithm. Computer methods in applied mechanics and engineering. 2021 Apr 1;376:113609.
[3] Arora S, Singh H, Sharma M, Sharma S, Anand P. A new hybrid algorithm based on grey wolf optimization and crow search algorithm for unconstrained function optimization and feature selection. Ieee Access. 2019 Feb 18;7:26343-61.
[4] Baskar A. New simple trigonometric algorithms for solving optimization problems. Journal of Applied Science and Engineering. 2022 Mar;25(6):1257-72.
[5] Baskar A. Simple population-based algorithms for solving optimization problems. RAIRO-Operations Research. 2024 Jan 1;58(1):253-80.
[6] Baskar A, Xavior MA, Jeyapandiarajan P, Batako A, Burduk A. A novel two-phase trigonometric algorithm for solving global optimization problems. Annals of Operations Research. 2024 Mar 21:1-51.
[7] Bayzidi H, Talatahari S, Saraee M, Lamarche CP. Social network search for solving engineering optimization problems. Computational Intelligence and Neuroscience. 2021;2021(1):8548639.
[8] Chawla M, Duhan M. Levy flights in metaheuristics optimization algorithms–a review. Applied Artificial Intelligence. 2018 Nov 26;32(9-10):802-21.
[9] Cui Y, Shi R, Dong J. CLTSA: A novel tunicate swarm algorithm based on chaotic-Lévy flight strategy for solving optimization problems. Mathematics. 2022 Sep 19;10(18):3405.
[10] Deb K, Goyal M. Optimizing Engineering Designs Using a Combined Genetic Search. InICGA 1997 Jul (pp. 521-528).
[11] Jesus F, Gerard AF. The estimation of the Cobb-Douglas function: a retrospective view. East. Econ. J. 2005;31(3):427-45.
[12] Himmelblau DM. Applied nonlinear programming. McGraw-Hill; 2018 Nov 1.
[13] Holland JH. Genetic algorithms. Scientific american. 1992 Jul 1;267(1):66-73.
[14] Kennedy J, Eberhart R. Particle swarm optimization. InProceedings of ICNN'95-international conference on neural networks 1995 Nov 27 (Vol. 4, pp. 1942-1948). ieee.
[15] Kim JH. Harmony search algorithm: A unique music-inspired algorithm. Procedia engineering. 2016 Jan 1;154:1401-5.
[16] Kirkpatrick S, Gelatt Jr CD, Vecchi MP. Optimization by simulated annealing. science. 1983 May 13;220(4598):671-80.
[17] Lei W, Jiawei W, Zezhou M. Enhancing grey wolf optimizer with levy flight for engineering applications. IEEE Access. 2023 Jul 13;11:74865-97.
[18] Mirjalili S, Mirjalili SM, Lewis A. Grey wolf optimizer. Advances in engineering software. 2014 Mar 1;69:46-61.
[19] Mirjalili S, Lewis A. The whale optimization algorithm. Advances in engineering software. 2016 May 1;95:51-67.
[20] Price KV, Awad NH, Ali MZ, Suganthan PN. Problem definitions and evaluation criteria for the 100-digit challenge special session and competition on single objective numerical optimization. InTechnical report 2018 Nov. Singapore: Nanyang Technological University.
[21] Rao RV, Savsani VJ, Vakharia DP. Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Computer-aided design. 2011 Mar 1;43(3):303-15.
[22] Şenel FA, Gökçe F, Yüksel AS, Yiğit T. A novel hybrid PSO–GWO algorithm for optimization problems. Engineering with Computers. 2019 Oct;35:1359-73.
[23] Tangwaragorn P, Charoenruk N, Viriyasitavat W, Tangmanee C, Kanawattanachai P, Hoonsopon D, Pungpapong V, Pattanapanyasat RP, Boonpatcharanon S, Rhuwadhana P. Analyzing key drivers of digital transformation: A review and framework. Journal of Industrial Information Integration. 2024 Nov 1;42:100680.
[24] Tomar V, Bansal M, Singh P. Metaheuristic algorithms for optimization: A brief review. Engineering Proceedings. 2024 Mar 13;59(1):238.
[25] Tu B, Wang F, Huo Y, Wang X. A hybrid algorithm of grey wolf optimizer and harris hawks optimization for solving global optimization problems with improved convergence performance. Scientific Reports. 2023 Dec 21;13(1):22909.
[26] Vo N, Tang H, Lee J. A multi-objective Grey Wolf–Cuckoo Search algorithm applied to spatial truss design optimization. Applied Soft Computing. 2024 Apr 1;155:111435.
[27] https://in.mathworks.com/help/gads/solving-a-mixed-integer-engineering-design-problem-using-the-genetic-algorithm.html [Accessed on 11-11-2022].
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