Abstract
In order to solve large scale continuous optimization problems, Self-adaptive DE (SaDE) is enhanced by incorporating the JADE mutation strategy and hybridized with modified multi-trajectory search (MMTS) algorithm (SaDE-MMTS). The JADE mutation strategy, the “DE/current-to-pbest” which is a variation of the classic “DE/current-to-best”, is used for generating mutant vectors. After the mutation phase, the binomial (uniform) crossover, the exponential crossover as well as no crossover option are used to generate each pair of target and trial vectors. By utilizing the self-adaptation in SaDE, both trial vector generation strategies and their associated control parameter values are gradually self-adapted by learning from their previous experiences in generating promising solutions. Consequently, suitable offspring generation strategy along with associated parameter settings will be determined adaptively to match different phases of the search process. MMTS is applied frequently to refine several diversely distributed solutions at different search stages satisfying both the global and the local search requirement. The initialization of step sizes is also defined by a self-adaption during every MMTS step. The success rates of both SaDE and the MMTS are determined and compared, consequently, future function evaluations for both search algorithms are assigned proportionally to their recent past performance. The proposed SaDE-MMTS is employed to solve the 20 numerical optimization problems for the CEC’2010 Special Session and Competition on Large Scale Global Optimization and competitive results are presented.
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
Preview
Unable to display preview. Download preview PDF.
References
Brest, J., Greiner, S., Boskovic, B., Mernik, M., Zumer, V.: Self-adapting control parameters in differential evolution: A comparative study on numerical benchmark problems. IEEE Transactions on Evolutionary Computation 10(6), 646–657 (2006)
Das, S., Abraham, A., Chakraborty, U.K., Konar, A.: Differential evolution using a neighborhood based mutation operator. IEEE Trans. on Evolutionary Computation 13(3), 526–553 (2009)
Herrera, F., Lozano, M., Molina, D.: Test Suite for the Special Issue of Soft Computing on Scalability of Evolutionary Algorithms and other Metaheuristics for Large Scale Continuous Optimization Problems, http://sci2s.ugr.es/eamhco/CFP.php
Huang, V.L., Qin, A.K., Suganthan, P.N.: Self-adaptive differential evolution algorithm for constrained real-parameter optimization. In: Proc. IEEE Congr. Evol. Comput. (CEC 2006), pp. 17–24 (July 2006)
Price, K.V.: An Introduction to Differential Evolution. In: Corne, D., Dorigo, M., Glover, F. (eds.) New Ideas in Optimization, pp. 79–108. McGraw-Hill, London (1999)
Price, K., Storn, R., Lampinen, J.: Differential Evolution - A Practical Approach to Global Optimization. Springer, Berlin (2005)
Pétrowski, A.: A clearing procedure as a niching method for genetic algorithms. In: Proc. of the IEEE Int. Conf. on Evolutionary Computation, New York, USA, pp. 798–803 (1996)
Qin, A.K., Huang, V.L., Suganthan, P.N.: Differential evolution algorithm with strategy adaptation for global numerical optimization. IEEE Trans on Evolutionary Computation 13(2), 398–417 (2009)
Qin, A.K., Suganthan, P.N.: Self-adaptive differential evolution algorithm for numerical optimization. In: Proc. IEEE Congr. Evol. Comput. (CEC 2005), Edinburgh, Scotland, pp. 1785–1791. IEEE Press, Los Alamitos (2005)
Rahnamayan, S., Tizhoosh, H.R., Salama, M.M.A.: Opposition-Based Differential Evolution. IEEE Trans. on Evolutionary Computation 12(1), 64–79 (2008)
Storn, R., Price, K.V.: Differential evolution-A simple and efficient heuristic for global optimization over continuous Spaces. Journal of Global Optimization 11, 341–359 (1997)
Tseng, L.Y., Chen, C.: Multiple trajectory search for multiobjective optimization. In: Proc. IEEE Congr. Evol. Comput (CEC 2007), pp. 3609–3616 (2007)
Tseng, L.Y., Chen, C.: Multiple trajectory search for large scale global optimization. In: Proc. IEEE Congr. Evol. Comput. (CEC 2008), pp. 3052–3059 (2008)
Tang, K., Yao, X., Suganthan, P.N., MacNish, C., Chen, Y.P., Chen, C.M., Yang, Z.: Benchmark Functions for the CEC 2008 Special Session and Competition on Large Scale Global Optimization, Technical Report, Nature Inspired Computation and Applications Laboratory, USTC, China, & Nanyang Technological University, Singapore (November 2007)
Zhang, J.Q., Sanderson, A.C.: JADE: Adaptive Differential Evolution with Optional External Archive. IEEE Trans on Evolutionary Computation 13(5), 945–958 (2009)
Tang, K., Li, X.D., Suganthan, P.N., Yang, Z.Y., Weise, T.: Benchmark Functions for the CEC 2010 Special Session and Competition on Large-Scale Global Optimization (2010), http://nical.ustc.edu.cn/cec10ss.php , http://www3.ntu.edu.sg/home/EPNSugan/
Zhao, S.Z., Liang, J.J., Suganthan, P.N., Tasgetiren, M.F.: Dynamic Multi-swarm Particle Swarm Optimizer with Local Search for Large Scale Global Optimization. In: Proc. IEEE Congr. Evol. Comput (CEC 2010), Hong Kong, pp. 3845–3852 (June 2008)
Zhao, S.Z., Suganthan, P.N., Das, S.: Self-adaptive Differential Evolution with Multi-trajectory Search for Large Scale Optimization. Soft Computing (accepted)
Korosec, P., Tashkova, K., Silc, J.: The Differential Ant-Stigmergy Algorithm for Large-Scale Global Optimization. In: Proc. IEEE Congr. Evol. Comput. (CEC 2010), pp. 4288–4295 (2010)
Wang, H., Wu, Z., Rahnamayan, S., Jiang, D.: Sequential DE Enhanced by Neighborhood Search for Large Scale Global Optimization. In: Proc. IEEE Congr. Evol. Comput. (CEC 2010), pp. 4056–4062 (2010)
Wang, Y., Li, B.: Two-stage based Ensemble Optimization for Large-Scale Global Optimization. In: Proc. IEEE Congr. Evol. Comput. (CEC 2010), pp. 4488–4495 (2010)
Molina, D., Lozano, M., Herrera, F.: MA-SW-Chains: Memetic Algorithm Based on Local Search Chains for Large Scale Continuous Global Optimization. In: Proc. IEEE Congr. Evol. Comput. (CEC 2010), pp. 3153–3160 (2010)
Brest, J., Zamuda, A., Fister, I., Maucec, M.S.: Large Scale Global Optimization using Self-adaptive Differential Evolution Algorithm. In: Proc. IEEE Congr. Evol. Comput. (CEC 2010), pp. 3097–3104 (2010)
Omidvar, M.N., Li, X., Yao, X.: Cooperative Co-evolution with Delta Grouping for Large Scale Non-separable Function Optimization. In: Proc. IEEE Congr. Evol. Comput. (CEC 2010), pp. 1762–1769 (2010)
Chen, S.: Locust Swarms for Large Scale Global Optimization of Nonseparable Problems. Kukkonen, Benchmarking the Classic Differential Evolution Algorithm on Large-Scale Global Optimization
Yang, Z., Tang, K., Yao, X.: Large Scale Evolutionary Optimization Using Cooperative Coevolution. Information Sciences 178(15), 2985–2999 (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zhao, SZ., Suganthan, P.N., Das, S. (2010). Self-adaptive Differential Evolution with Modified Multi-Trajectory Search for CEC’2010 Large Scale Optimization. In: Panigrahi, B.K., Das, S., Suganthan, P.N., Dash, S.S. (eds) Swarm, Evolutionary, and Memetic Computing. SEMCCO 2010. Lecture Notes in Computer Science, vol 6466. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17563-3_1
Download citation
DOI: https://doi.org/10.1007/978-3-642-17563-3_1
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-17562-6
Online ISBN: 978-3-642-17563-3
eBook Packages: Computer ScienceComputer Science (R0)