Abstract
Backtracking Search Algorithm (BSA) is a novel global optimization algorithm for solving real-valued numerical optimization problems. In this paper, several opposition-based BSAs are proposed and compared comprehensively. Its key character is that a candidate solution and its corresponding opposite solution are considered simultaneously to achieve an optimal approximation. The simulation results on 58 widely used benchmark problems demonstrate that, the opposition-based learning method can significantly improve the performance of original BSA. In addition, the proposed algorithm performance has evident positive correlation with the utilization rate of opposite points.
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References
Jr, I.F., Yang, X.S., Fister, I., Brest, J., Fister, D.: A brief review of nature-inspired algorithms for optimization. Elektrotehniški Vestnik 80, 116–122 (2013)
Civicioglu, P.: Backtracking search optimization algorithm for numerical optimization problems. Appl. Math. Comput. 219, 8121–8144 (2013)
Askarzadeh, A., Coelho, L.S.: A backtracking search algorithm combined with Burger’s chaotic map for parameter estimation of PEMFC electrochemical model. Int. J. Hydrogen Energy 39, 11165–11174 (2014)
Kolawole, S.O., Duan, H.: Backtracking search algorithm for non-aligned thrust optimization for satellite formation. In: IEEE International Conference on Control and Automation, pp. 738–743 (2014)
Guney, K., Durmus, A., Basbug, S.: Backtracking search optimization algorithm for synthesis of concentric circular antenna arrays. Int. J. Antennas Propag. 2014, 1–11 (2014)
Muralidharan, R., Athinarayanan, V., Mahanti, G.K., Mahanti, A.: QPSO versus BSA for failure correction of linear array of mutually coupled parallel dipole antennas with fixed side lobe level and VSWR. Adv. Electr. Eng. 2014, 1–7 (2014)
Duan, H., Luo, Q.: Adaptive backtracking search algorithm for induction magnetometer optimization. IEEE Trans. Magn. 50, 6001206 (2014)
El-Fergany, A.: Optimal allocation of multi-type distributed generators using backtracking search optimization algorithm. Electr. Power Ener. Syst. 64, 1197–1205 (2015)
Modiri-Delshad, M., Rahim, N.A.: Solving non-convex economic dispatch problem via backtracking search algorithm. Energy 77, 372–381 (2014)
Tizhoosh, H.R.: Opposition-based learning: a new scheme for machine intelligence. In: International Conference on Computational Intelligence for Modelling, Control and Automation, and International Conference on Intelligent Agents, Web Technologies and Internet Commerce, pp. 695–701 (2005)
Xu, Q.Z., Wang, L., Wang, N., Hei, X.H., Zhao, L.: A review of opposition-based learning from 2005 to 2012. Eng. Appl. Artif. Intell. 29, 1–12 (2014)
Rahnamayan, S., Tizhoosh, H.R., Salama, M.M.A.: Quasi-oppositional differential evolution. In: IEEE Congress on Evolutionary Computation, pp. 2229–2236 (2007)
Ergezer, M., Simon, D., Du, D.W.: Oppositional biogeography-based optimization. In: IEEE International Conference on Systems, Man and Cybernetics, pp. 1009–1014 (2009)
Wang, H., Wu, Z.J., Liu, Y., Wang, J., Jiang, D.Z., Chen, L.L.: Space transformation search: a new evolutionary technique. In: ACM/SIGEVO Summit on Genetic and Evolutionary Computation, pp. 537–544 (2009)
Xu, Q.Z., Wang, L., He, B.M., Wang, N.: Opposition-based differential evolution using the current optimum for function optimization. J. Appl. Sci. 29, 308–315 (2011). (in Chinese)
Rahnamayan, S., Tizhoosh, H.R., Salama, M.M.A.: Opposition-based differential evolution. IEEE Trans. Evol. Comput. 12, 64–79 (2008)
Xu, Q.Z.: Research on the Artificial Co-computing Model and Its Applications. Xi’an University of Technology, Xi’an (2011)
Pearson, K.: Notes on regression and inheritance in the case of two parents. Proc. Roy. Soc. London 58, 240–242 (1895)
Acknowledgments
This work was supported in part by the National Natural Science Foundation of China (Nos. 61375089 and 61305083).
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Xu, Q., Guo, L., Wang, N., Xu, L. (2015). Opposition-Based Backtracking Search Algorithm for Numerical Optimization Problems. In: He, X., et al. Intelligence Science and Big Data Engineering. Big Data and Machine Learning Techniques. IScIDE 2015. Lecture Notes in Computer Science(), vol 9243. Springer, Cham. https://doi.org/10.1007/978-3-319-23862-3_22
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DOI: https://doi.org/10.1007/978-3-319-23862-3_22
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