Abstract
Backtracking search algorithm is a novel population-based stochastic technique. This paper proposes an improved backtracking search algorithm for constrained optimization problems. The proposed algorithm is combined with differential evolution algorithm and the breeder genetic algorithm mutation operator. The differential evolution algorithm is used to accelerate convergence at later iteration process, and the breeder genetic algorithm mutation operator is employed for the algorithm to improve the population diversity. Using the superiority of feasible point scheme and the parameter free penalty scheme to handle constrains, the improved algorithm is tested on 13 well-known benchmark problems. The results show the improved backtracking search algorithm is effective and competitive for constrained optimization problems.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Holland, J.H.: Adaptation in natural and artificial systems. University of Michigan Press, Ann Arbor (1975)
Storn, R., Price, K.V.: Differential evolution: a simple and efficient adaptive scheme for global optimization over continuous spaces, Technical Report TR-95-012, Berkeley, CA (1995)
Dorigo, M., Maniezzo, V., Colorni, A.: The ant system: optimization by a colony of cooperating agents. IEEE Trans. on Systems. Man, and Cybernetics 26, 29–41 (1996)
Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: Proceedings of the IEEE International Conference on Neural Networks, pp. 1942–1948. IEEE Press (1995)
Eberhart, R.C., Kennedy, J.: A new optimizer using particle swarm theory. In: Proc. of the 6th International Symposium on Micro Machine and Human Science, pp. 39–43. IEEE Press (1995)
Karaboga, D.: An idea based on honey bee swarm for numerical optimization. Technical Report TR-06, Erciyes University, Engineering Faculty, Computer Engineering Department (2005)
Cui, Z.H., Cai, X.J.: Using social cognitive optimization algorithm to solve nonlinear equations. In: Proc. 9th IEEE Int. Conf. on Cog. Inf., pp. 199–203 (2010)
Chen, Y.J., Cui, Z.H., Zeng, J.H.: Structural optimization of Lennard-Jones clusters by hybrid social cognitive optimization algorithm. In: Proc. of 9th IEEE Int. Conf. on Cog. Inf., pp. 204–208 (2010)
Cui, Z., Shi, Z., Zeng, J.: Using social emotional optimization algorithm to direct orbits of chaotic systems. In: Panigrahi, B.K., Das, S., Suganthan, P.N., Dash, S.S. (eds.) SEMCCO 2010. LNCS, vol. 6466, pp. 389–395. Springer, Heidelberg (2010)
Wei, Z.H., Cui, Z.H., Zeng, J.C.: Social cognitive optimization algorithm with reactive power optimization of power system. In: Proc. of 2010 Int. Conf. Computational Aspects of Social Networks, pp. 11–14 (2010)
Xu, Y., Cui, Z., Zeng, J.: Social emotional optimization algorithm for nonlinear constrained optimization problems. In: Panigrahi, B.K., Das, S., Suganthan, P.N., Dash, S.S. (eds.) SEMCCO 2010. LNCS, vol. 6466, pp. 583–590. Springer, Heidelberg (2010)
Yang, X.S.: A new metaheuristic bat-inspired algorithm. Springer, Berlin (2010)
Yang, X.S.: Nature-Inspried Metaheuristic Algorithms, 2nd edn. Luniver Press, Frome (2010)
Geem, Z.W., Kim, J.H., Loganathan, G.V.: A new heuristic optimization algorithm: Harmony search. Simulation 76, 60–68 (2001)
Simon, D.: Biogeography-Based Optimization. IEEE Transactions on Evolutionary Computation 12, 702–713 (2008)
He, S., Wu, Q.H., Saunders, J.R.: A novel group search optimizer inspired by animal behavioral ecology. In: Proceedings of IEEE Congress on Evolutionary Computation, pp. 16–21 (2006)
Civicioglu, P.: Backtracking search optimization algorithm for numerical optimization problems. Applied Mathematics and Computation 219, 8121–8144 (2013)
Mezura-Montes, E., Miranda-Varela, M., Gómez-Ramón, R.: Differential evolution in constrained numerical optimization: An empirical study. Information Sciences 180, 4223–4262 (2010)
Neri, F., Tirronen, V.: Recent advances in differential evolution: a survey and experimental analysis. Artificial Intelligence Review 33, 61–106 (2010)
Wang, Y., Cai, Z., Guo, G., Zhou, Y.: Multiobjective optimization and hybrid evolutionary algorithm to solve constrained optimization problems. IEEE Transaction on Systems 37, 560–575 (2007)
Jia, G., Wang, Y., Cai, Z., Jin, Y.: An improved (μ+λ)-constrained differential evolution for constrained optimization. Information Sciences 222, 302–322 (2013)
Runarsson, T.P., Yao, X.: Stochastic ranking for constrained evolutionary optimization. IEEE Transactions on Evolutionary Computation 4, 284–294 (2000)
Wang, L., Zhong, Y.: A modified group search optimiser for constrained optimisation problems. Int. J. Modelling, Identification and Control 18, 276–283 (2013)
Tessema, B., Yen, G.: A self-adaptive penalty function based algorithm for constrained optimization. In: Proceedings 2006 IEEE Congress on Evolutionary Computation, pp. 246–253 (2006)
Runarsson, T.P., Yao, X.: Search biases in constrained evolutionary optimization. IEEE Transactions on Systems, Man, and Cybernetics 35, 233–243 (2005)
Mezura-Montes, E., Coello Coello, C.A.: A simple multimembered evolution strategy to solve constrained optimization problems. IEEE Transactions on Evolutionary Computation 9, 1–17 (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Zhao, W., Wang, L., Yin, Y., Wang, B., Wei, Y., Yin, Y. (2014). An Improved Backtracking Search Algorithm for Constrained Optimization Problems. In: Buchmann, R., Kifor, C.V., Yu, J. (eds) Knowledge Science, Engineering and Management. KSEM 2014. Lecture Notes in Computer Science(), vol 8793. Springer, Cham. https://doi.org/10.1007/978-3-319-12096-6_20
Download citation
DOI: https://doi.org/10.1007/978-3-319-12096-6_20
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-12095-9
Online ISBN: 978-3-319-12096-6
eBook Packages: Computer ScienceComputer Science (R0)