Abstract
As an appendix which is designed to embed in one of the complete swarm intelligence algorithms, the novel strategy, named dynamic-search-spaces (DS) is proposed to deal with the premature convergence of those algorithms. For realizing the decrement of search space, the differences or the distances between individual sites and the site of the global performance are to form the threshold of the self-adaption system. Once the value reached by calculating the quotient of sum of those sitting near the global performance and others over a stated percentage, the system is working to readjust the borders of search space by the site of the global performance. After each readjustment, the re-initialize to distribute individuals in the whole search space should be achieved to enhance individuals’ vitality which prove away from the premature convergence. Meanwhile, the simpler verifications are provided. The improvements of results are exhibited embedding in the genetic algorithm, the particle swarm optimization and the differential evolution. This dynamic search space scheme can be embedded in most of swarm intelligence algorithms easily abstract environment.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Zhang, J., Xin, B., Chen, J.: Hybridizing differential evolution and particle swarm optimization to design powerful optimizers: a review and taxonomy. IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 42(5), 744–767 (2012)
Poli, R.: Analysis of the publications on the applications of particle swarm optimisation. J. Artif. Evol. Appl. 2008, 1–10 (2008)
Khare, A., Rangnekar, S.: A review of particle swarm optimization and its applications in solar photovoltaic system. Appl. Soft Comput. 13(5), 2997–3006 (2013)
Ghaemi, R., Sulaiman, N., Ibrahim, H., Mustapha, N.: A review: accuracy optimization in clustering ensembles using genetic algorithms. Artif. Intell. Rev. 35(4), 287–318 (2011)
Huang, J.H., Chen, T.Y.: Application of data mining in a global optimization algorithm. Adv. Eng. Softw. 66(12), 24–33 (2013)
Ortiz, E.: Improving the training time of support vector regression algorithms through novel hyper-parameters search space reductions. Neurocomputing 72, 3683–3691 (2009)
Bland, J.A., Nolle, L.: Self-adaptive stepsize search for automatic optimal design. Knowl.-Based Syst. 29(3), 75–82 (2012)
Tizhoosh, H.R.: Opposition-based learning: a new scheme for machine intelligence. In: International Conference on Computational Intelligence for Modelling, Control and Automation, 2005 and International Conference on Intelligent Agents, Web Technologies and Internet Commerce, pp. 695–701 (2005)
Tang, K., Li, X., Suganthan, P.N., Yang, Z., Weise, T.: Benchmark functions for the cec2010 special session and competition on large-scale global optimization. Nature Inspired Computation and Applications Laboratory (2010)
Goldberg, D.E., Sastry, K.: Genetic algorithms in Search, Optimization and Machine Learning. Addison-Wesley Longman, Boston (1989)
Zhang, W.S., Li, K., Yu, X.: Improved evolutionary algorithm and its application to solving complex optimization problems. Appl. Res. Comput. 29(4), 1223–1226 (2012)
Zhang, W.J., Xie, X.F., Bi, D.C.: Handling boundary constraints for numerical optimization by particle swarm flying in periodic search space. In: Congress on Evolutionary Computation, CEC2004, vol. 2. IEEE (2004)
Helwig, S., Branke, J., Mostaghim, S.: Experimental analysis of bound handling techniques in particle swarm optimization. IEEE Trans. Evol. Comput. 17(2), 259–271 (2013)
Gandomi, A.H., Yang, X.-S.: Evolutionary boundary constraint handling scheme. Neural Comput. Appl. 21(6), 1449–1462 (2012)
Chu, W., Gao, X., Sorooshian, S.: Handling boundary constraints for particle swarm optimization in high-dimensional search space. Inf. Sci. 181(20), 4569–4581 (2011)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer Science+Business Media Singapore
About this paper
Cite this paper
Zhang, SP., Bi, W., Wang, XJ. (2016). A Dynamic Search Space Strategy for Swarm Intelligence. In: Li, K., Li, J., Liu, Y., Castiglione, A. (eds) Computational Intelligence and Intelligent Systems. ISICA 2015. Communications in Computer and Information Science, vol 575. Springer, Singapore. https://doi.org/10.1007/978-981-10-0356-1_11
Download citation
DOI: https://doi.org/10.1007/978-981-10-0356-1_11
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-0355-4
Online ISBN: 978-981-10-0356-1
eBook Packages: Computer ScienceComputer Science (R0)