Abstract
Sine cosine algorithm (SCA), a population-based optimization algorithm, is recently developed to solve optimization problems. In SCA, mathematical functions sine and cosine are utilized to fluctuate the candidate solutions towards or outwards the best solution. SCA gets trapped in local optima and suffers from premature convergence for some problems due to a lack of exploration of the search space. In this paper, SCA is improved by incorporating an opposition-based learning (OBL) scheme called centroid opposition-based computing (COBC) in it to enhance its exploration ability. The proposed algorithm is termed as COBSCA in this paper. It is applied to solve 28 CEC2013 benchmark problems. The results of COBSCA are compared with SCA and opposition-based SCA (OBSCA). The experimental results demonstrate that the COBSCA statistically outperforms others in solving most of the problems in the CEC2013 benchmark suite.
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References
Mirjalili S (2016) SCA: a Sine Cosine Algorithm for solving optimization problems. Knowl-Based Syst. https://doi.org/10.1016/j.knosys.2015.12.022
Elaziz MA, Oliva D, Xiong S (2017) An improved Opposition-Based Sine Cosine Algorithm for global optimization. Expert Syst Appl 90:484–500
Qu C, Zeng Z, Dai J, Yi Z, He W (2018) A modified Sine-Cosine Algorithm based on neighborhood search and greedy levy mutation. Comput Intell Neurosci 2018. Article ID 4231647, 19 pp. https://doi.org/10.1155/2018/4231647
Meshkat M, Parhizgar M (2017) A novel weighted update position mechanism to improve the performance of sine cosine algorithm. In: 2017 5th Iranian joint congress on fuzzy and intelligent systems (CFIS). IEEE, pp 166–171
Gupta S, Deep K, Mirjalili S, Kim JH (2020) A modified Sine Cosine Algorithm with novel transition parameter and mutation operator for global optimization. Expert Syst Appl. https://doi.org/10.1016/j.eswa.2020.113395
Gupta S, Deep K, Engelbrecht AP (2020) A memory guided sine cosine algorithm for global optimization. Eng Appl Artif Intell 93:103718
Gupta S, Deep K (2020) A novel hybrid sine cosine algorithm for global optimization and its application to train multilayer perceptrons. Appl Intell 50:993–1026. https://doi.org/10.1007/s10489-019-01570-w
Huang H, Feng X, Heidari AA, Xu Y, Wang M, Liang G, Chen H, Cai X (2020) Rationalized Sine Cosine Optimization with efficient searching patterns. IEEE Access. https://doi.org/10.1109/ACCESS.2020.2983451
Bairathi D, Gopalani D (2017) Opposition-Based Sine Cosine Algorithm (OSCA) for training feed-forward neural networks. In: Proceedings of 13th international conference on signal-image technology and internet-based systems (SITIS). IEEE, pp 438–444. https://doi.org/10.1109/SITIS.2017.78
Tizhoosh HR (2005) Opposition-based learning: a new scheme for machine intelligence. In: Proceedings of the international conference on computational intelligence for modelling, control and automation, and international conference on intelligent agents, web technologies and internet commerce (CIMCA-IAWTIC’05), vol 1. IEEE, pp 695–701
Al-Qunaieer FS, Tizhoosh HR, Rahnamayan S (2010) Opposition based computing—a survey. In: Proceedings of international joint conference on neural networks (IJCNN), pp 1–7
Xu Q, Wang L, Wang N, Hei X, Zhao L (2014) A review of opposition-based learning from 2005 to 2012. Eng Appl Artif Intell 29:1–12
Rojas-Morales N, Rojas M-CR, Ureta EM (2017) A survey and classification of Opposition-Based Metaheuristics. Comput Ind Eng 110:424–435
Mahdavi S, Rahnamayana S, Deb K (2018) Opposition based learning: a literature review. Swarm Evol Comput 39:1–23
Rahnamayan S, Jesuthasan J, Bourennani F, Salehinejad H, Naterer GF (2014) Computing opposition by involving entire population. In: Proceedings of IEEE congress on evolutionary computation (CEC), pp 1800–1807
Liang JJ, Qu BY, Suganthan PN, Hernández-Díaz AG (2013) Problem definitions and evaluation criteria for the CEC 2013 special session on real-parameter optimization. http://www.ntu.edu.sg/home/EPNSugan/index files/CEC2013/CEC2013.htm
Derrac J, Garcia S, Molina D, Herrera F (2011) A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm Evol Comput 1:3–18
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Si, T., Bhattacharya, D. (2021). Sine Cosine Algorithm with Centroid Opposition-Based Computation. In: Gao, XZ., Kumar, R., Srivastava, S., Soni, B.P. (eds) Applications of Artificial Intelligence in Engineering. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-33-4604-8_9
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