Abstract
Dynamic optimisation problems (DOPs) have become a challenging research topic over the last two decades. In DOPs, at least one part of the problem changes as time passes. These changes may take place in the objective function(s) and/or constraint(s). In this paper, we propose a new type of DOP in which the boundaries of variables change as time passes. This is called a single objective unconstrained dynamic optimisation problem with known changeable boundaries (DOPKCBs). To solve DOPKCBs, we propose three repair strategies. These algorithms have been compared with other repairing techniques from the literature that have been previously used in static problems. In this paper, the results of the conducted experiments and the statistical analysis generally demonstrated that one of the proposed strategies, which uses the overall elite individual (OEI) as a repair strategy, obtained much better results than the other strategies.
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 subscriptionsReferences
Gendreau, M., Potvin, J.-Y., Bräysy, O., Hasle, G., Løkketangen, A.: Metaheuristics for the vehicle routing problem and extensions: a categorized bibliography. In: Golden, B., Raghavan, S., Wasil, E. (eds.) The Vehicle Routing Problem: Latest Advances and New Challenges. Springer, New York (2008)
Nocedal, J., Wright, S.J.: Numerical Optimization, 2nd edn. Springer, New York (2006)
Miettinen, K., Ruiz, F., Wierzbicki, A.P.: Introduction to multiobjective optimization: interactive approaches. In: Branke, J., Deb, K., Miettinen, K., Słowiński, R. (eds.) Multiobjective Optimization. LNCS, vol. 5252, pp. 27–57. Springer, Heidelberg (2008)
Bandyopadhyay, S., Saha, S.: some single - and multiobjective optimization techniques. In: Unsupervised Classification, pp. 17–58. Springer, Heidelberg (2013)
Dadkhah, K.: Static optimization. In: Foundations of Mathematical and Computational Economics, pp. 323–346. Springer, Berlin (2011)
Branke, J.: Evolutionary Optimization in Dynamic Environments. Kluwer, Dordrecht (2001)
Nguyen, T.T., Yangb, S., Branke, J.: Evolutionary dynamic optimization: a survey of the state of the art. Swarm Evol. Comput. 6, 1–24 (2012)
Cruz, C., González, J.R., Pelta, D.A.: Optimization in dynamic environments: a survey on problems, methods and measures. Soft. Comput. 15, 1427–1448 (2011)
Wen-Jun, Z., Xiao-Feng, X., De-Chun, B.: Handling boundary constraints for numerical optimization by particle swarm flying in periodic search space. In: Congress on Evolutionary Computation, CEC 2004, vol. 2302, pp. 2307–2311 (2004)
Shi, C., Yuhui, S., Quande, Q.: Experimental study on boundary constraint handling in particle swarm optimization: from population diversity perspective. In: Yuhui, S. (ed.) Recent Algorithms and Applications in Swarm Intelligence Research, pp. 96–124. IGI Global, Hershey (2013)
Padhye, N., Deb, K., Mittal, P.: An Efficient and Exclusively-Feasible Constrained Handling Strategy for Evolutionary Algorithms. Technical Report (2013)
Yang, S.: Genetic algorithms with memory- and elitism-based immigrants in dynamic environments. Evol. Comput. 16, 385–416 (2008)
Branke, J., Schmeck, H.: Designing evolutionary algorithms for dynamic optimization problems. In: Advances in Evolutionary Computing: Theory and Applications, pp. 239–262. Springer, Heidelberg (2003)
Cobb, H.G.: An investigation into the use of hypermutation as an adaptive operator in genetic algorithms having continuous, time-dependent nonstationary environments. Naval Research Laboratory (1990)
Goh, C.-K., Chen Tan, K.: A competitive-cooperative coevolutionary paradigm for dynamic multiobjective optimization. IEEE Trans. Evol. Comput. 13, 103–127 (2009)
Grefenstette, J.J.: Genetic algorithms for changing environments. In: Maenner, R., Manderick, B. (eds.) Parallel Problem Solving from Nature, vol. 2, pp. 137–144. North Holland, Amsterdam (1992)
Yang, S., Yao, X.: Experimental study on population-based incremental learning algorithms for dynamic optimization problems. Soft. Comput. 9, 815–834 (2005)
Nguyen, T.T., Yang, S., Branke, J., Yao, X.: Evolutionary dynamic optimization: methodologies. In: Yang, S., Yao, X. (eds.) Evolutionary Computation for DOPs. SCI, vol. 490, pp. 39–63. Springer, Heidelberg (2013)
Branke, J.: Memory enhanced evolutionary algorithms for changing optimization problems. In: Proceedings of the 1999 Congress on Evolutionary Computation, CEC 1999, vol. 1883, p. 1882 (1999)
Moser, I., Chiong, R.: Dynamic function optimization: the moving peaks benchmark. In: Alba, E., Nakib, A., Siarry, P. (eds.) Metaheuristics for Dynamic Optimization. SCI, vol. 433, pp. 37–62. Springer, Heidelberg (2013)
Li, C., Yang, S.: A generalized approach to construct benchmark problems for dynamic optimization. In: Li, X., et al. (eds.) SEAL 2008. LNCS, vol. 5361, pp. 391–400. Springer, Heidelberg (2008)
Liang, J.J., Suganthan, P.N., Deb, K.: Novel composition test functions for numerical global optimization. In: Proceedings 2005 IEEE, Swarm Intelligence Symposium, SIS 2005, pp. 68–75. (2005)
Li, C., Yang, S., Nguyen, T.T., Yu, E.L., Yao, X., Jin, Y., Beyer, H.-G., Suganthan, P.N.: Benchmark Generator for CEC 2009 Competition on Dynamic Optimization (2008)
Morrison, R.W.: Performance measurement in dynamic environments. In: GECCO Workshop on Evolutionary Algorithms for Dynamic Optimization Problems, pp. 5–8 (2003)
Yang, S., Nguyen, T.T., Li, C.: Evolutionary dynamic optimization: test and evaluation environments. In: Yang, S., Yao, X. (eds.) Evolutionary Computation for DOPs. SCI, vol. 490, pp. 3–37. Springer, Heidelberg (2013)
García, S., Molina, D., Lozano, M., Herrera, F.: A study on the use of non-parametric tests for analyzing the evolutionary algorithms’ behaviour: a case study on the CEC’2005 Special Session on Real Parameter Optimization. J. Heuristics 15, 617–644 (2009)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
AbdAllah, A.F.M., Essam, D.L., Sarker, R.A. (2016). Solving Dynamic Optimisation Problems with Known Changeable Boundaries. In: Ray, T., Sarker, R., Li, X. (eds) Artificial Life and Computational Intelligence. ACALCI 2016. Lecture Notes in Computer Science(), vol 9592. Springer, Cham. https://doi.org/10.1007/978-3-319-28270-1_3
Download citation
DOI: https://doi.org/10.1007/978-3-319-28270-1_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-28269-5
Online ISBN: 978-3-319-28270-1
eBook Packages: Computer ScienceComputer Science (R0)