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Gravitational evaluation algorithm for global optimization problem | ||
| International Journal of Nonlinear Analysis and Applications | ||
| مقاله 32، دوره 13، شماره 2، مهر 2022، صفحه 345-359 اصل مقاله (958.8 K) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22075/ijnaa.2022.6248 | ||
| نویسندگان | ||
| Hasanain Jalil Neamah* 1، 2، 3؛ Ali M. Almobarqaa4؛ Zainab Ali Abdulhusien1 | ||
| 1Faculty of Management, University of Warsaw, Poland | ||
| 2University of Information Technology and Communications, Baghdad, Iraq | ||
| 3Acharya Nagarjuna University, Guntur, India | ||
| 4Consultor of Development and Social Justice, Iraqi Parliament Council, Baghdad, Iraq | ||
| تاریخ دریافت: 17 آذر 1400، تاریخ بازنگری: 07 بهمن 1400، تاریخ پذیرش: 21 بهمن 1400 | ||
| چکیده | ||
| This work proposes a new metaheuristic technique that combines Differential evolution (DE) with gravity search in a consistent manner. Swarm intelligence benefits and the concept of tensile strength between two particles are combined to suggest superior meta-heuristic approaches for limitless optimization issues. The goal of this paper is to create a new algorithm that overcomes the shortcomings of the Gravitational search algorithm by leveraging the advantages of the Differential evolution algorithm in expanding search areas, overcoming early convergence problems, and improving the attractive algorithm's ability to converge towards the optimum. The GSA algorithm has been utilized in a search-oriented algorithm, whereas the Differential evolution algorithm is causing a high level of diversification in society, which leads to the establishment of search regions for the GSA algorithm. The effectiveness of the suggested approach was evaluated by solving a collection of 30 Real-Parameter Numerical Optimization problems that were presented at IEEE-CEC 2014. The findings are compared to 5 state-of-the-art unconstrained problem algorithms and 6 state-of-the-art unconstrained problem algorithms. The winner methods were also deduced from the results using the Wilcoxon signed test. | ||
| کلیدواژهها | ||
| gravitational search algorithm؛ global optimization؛ differential evolution؛ hybrid algorithms | ||
| مراجع | ||
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[1] H.A. Abbass, Marriage in honey bees optimization – a haplometrosis polygynous swarming approach, Evol. Comput. Proc. 2001 Congress, 2001, p. 207–214. [2] B. Alatas, Artificial chemical reaction optimization algorithm for global optimization, Expert Syst. Appl. 38 (2011), 13170–13180. [3] A. Askarzadeh and A. Rezazadeh, A new heuristic optimization algorithm for modeling of proton exchange membrane fuel cell: Bird mating optimizer, Int. J. Energy Res. 37 (2013), no. 10, 1196–1204. [4] M. Dorigo, M. Birattari and T. Stutzle, Ant colony optimization, IEEE Comput. Intell. Magaz. 1 (2006), no. 4, 28–39. [5] O.K. Erol and I. Eksin, A new optimization method: big bang–big crunch, Adv. Eng. Softw. 37 (2006), 106–111. [6] A.V. Fiacco and G.P. McCormick, Nonlinear programming: Sequential unconstrained minimization techniques, New York: Wiley, 1968. [7] R.A. Formato, Central force optimization: a new metaheuristic with applications in applied electromagnetics, Prog. Electromag. Res. 77, (2007) 425–491. [8] D. Fogel, Artificial intelligence through simulated evolution, Wiley-IEEE Press, 2009. [9] A.H. Gandomi, A.H. Alavi, H. Krill, Krill herd: A new bio-inspired optimization algorithm, Commun. Nonlinear Sci. Numer. Simul. 17 (2012), no. 12, 4831–4845. [10] A. Hatamlou, Black hole: a new heuristic optimization approach for data clustering, Inf. Sci. 222 (2013), 175–184. [11] N. Hansen, S.D. M¨uller and P. Koumoutsakos, Reducing the time complexity of the derandomized evolution strategy with covariance matrix adaptation (CMAES), Evol. Compu. 11 (2003), 1–18. [12] S. He, S.Q.H. Wu and J.R. Saunders, A novel group search optimizer inspired by animal behavioural ecology, IEEE Int. Conf. Evol. Comput. 2006, p 1272–1278. [13] S. He, Q. Henry Wu and J.R. Saunders, Group search optimizer: an optimization algorithm inspired by animal searching behavior, IEEE Trans. Evol. Comput. 13 (2009), no. 5, 973–990. [14] A. Kaveh and M. Khayatazad, A new meta-heuristic method: ray optimization, Comput. Struct. 112 (2012), 283–94. [15] A. Kaveh and S. Talatahari, A novel heuristic optimization method: charged system search, Acta Mech. 213 (2010), 267–89. [16] J. Kennedy and R. Eberhart, Particle swarm optimization, in Neural Networks, Proc. IEEE Int. Conf. 1995, p. 1942–1948. [17] J.R. Koza, Genetic programming, 1992. [18] D. Simon, Biogeography-based optimization, Evol. Comput. IEEE Trans. 12 (2008), 702–13. [19] J.J. Liang, B.Y. Qu and P.N. Suganthan, Problem definitions and evaluation criteria for the CEC 2014 special session and competition on single objective real-parameter numerical optimization, Comput. Intell. Lab. Zhengzhou University, Zhengzhou China and Technical Report, Nanyang Technological University, Singapore 635 (2013), 490. [20] X. Li, A new intelligent optimization-artificial fish swarm algorithm, PhD Thesis, Zhejiang University of Zhejiang, China, 2003. [21] X. Lu and Y. Zhou, A novel global convergence algorithm: bee collecting pollen algorithm, Int. Conf. Intell. Comput. Springer, Berlin, Heidelberg, 2008, p. 518–525. [22] S.A. Mirjalili, S.M. Mirjalili and A. Lewis, Grey wolf optimizer, Adv. Engin. Software 69 (2014), 46–61. [23] S.A. Mirjalili, Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm, Knowledge-Based Syst. 89 (2015), 228–249. [24] S.A. Mirjalili and A. Lewis, The whale optimization algorithm, Adv. Engin. Software 95 (2016), 51–67. [25] F.F. Moghaddam, R.F. Moghaddam and M. Cheriet, Curved space optimization: a random search based on general relativity theory, arXiv, preprint arXiv, 1208. 2214 2012. [26] A. Mucherino and O. Seref, A novel metaheuristic search for global optimization, Proc. Conf. Data Min. Syst. Anal. Optim. Biomed. 2007, p. 28–30. [27] W-T. Pan, A new fruit fly optimization algorithm: taking the financial distress model as an example, Knowl-Based Syst. 26 (2012), 69–74. [28] P.C. Pinto, T.A. Runkler and J.M. Sousa, Wasp swarm algorithm for dynamic MAX-SAT problems, Int. Conf. Adapt. Natural Comput. Algor. Springer, Berlin, Heidelberg, 2007, p. 350–357. [29] E. Rashedi, H. Nezamabadi-Pour and S. Saryazdi, a gravitational search algorithm, Inf. Sci. 179 (2009), 2232–2248. [30] G. Rechenberg, Evolution strategy, Comput. Intell. Imitat. Life 1 (1994). [31] M. Roth and W. Stephen Termite, A swarm intelligent routing algorithm for mobile wireless ad-hoc networks, Stigmergic Optim. 2005, p 155–184. [32] H.-P. Schwefel, Evolution and optimum seeking, New York: Wiley, 1995. [33] Y. Shiqin, J. Jianjun and Y. Guangxing, A dolphin partner optimization, IEEE WRI Glob. Cong. Intell. Syst. 1 (2009), 124–128. [34] H. Shah-Hosseini, Principal components analysis by the galaxy-based search algorithm: a novel metaheuristic for continuous optimisation, Int. J. Comput. Sci. Eng. 6 (2011), 132–40. [35] R. Storn and K. Price, Differential evolution: A simple and efficient heuristic for global optimization over continuous spaces, J. Glob. Optim. 11 (1997), 341–59. [36] X. Yao, Y. Liu and G. Lin, Evolutionary programming made faster. Evolut Comput, IEEE Trans. 3 (1999), 82–102. [37] X-S. Yang, A new metaheuristic bat-inspired algorithm, Nature Inspired Cooperative Strategies for Optimization (NICSO 2010), ed., Springer, 2010, p. 65–74. [38] X-S. Yang and S. Deb, Cuckoo search via L´evy flights, Nature Biologically Inspired Computing, NaBIC, World Congress, 2009, p. 210–14. [39] X-S. Yang, Firefly algorithm, stochastic test functions and design optimisation, Int. J. Bio-Inspired Comput. 2 (2010), 78–84. | ||
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