Abstract
This work investigates a bio-inspired adaptive sampling immune optimization algorithm to solve linear or nonlinear chance-constrained optimization problems without any noisy information. In this optimizer, an efficient adaptive sampling detection scheme is developed to detect individual’s feasibility, while those high-quality individuals in the current population can be decided based on the reported sample-allocation scheme; a clonal selection-based time-varying evolving mechanism is established to ensure the evolving population strong population diversity and noisy suppression as well as rapidly moving toward the desired region. The comparative experiments show that the proposed algorithm can effectively solve multi-modal chance-constrained programming problems with high efficiency.
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
Liu, B.: Theory and practice of uncertain programming. STUDFUZZ, vol. 239. Springer, Heidelberg (2009)
Luedtke, J., Ahmed, S.: A sample approximation approach for optimization with probabilistic constraints. SIAM J. Optim. 19, 674–699 (2008)
Nemirovski, A., Shapiro, A.: Scenario approximations of chance constraints. In: Calafior, G., Dabbene, F. (eds.) Probabilistic and Randomized Methods for Design under Uncertainty, pp. 3–48. Springer, London (2005)
Zhang, Z.H.: Noisy immune optimization for chance-constrained programming problems. Applied Mechanics and Materials 48, 740–744 (2011)
Poojari, C.A., Varghese, B.: Genetic algorithm based technique for solving chance constrained problems. European Journal of Operational Research 185(3), 1128–1154 (2008)
Dasgupta, D., Yu, S., Nino, F.: Recent advances in artificial immune systems: models and applications. Applied Soft Computing 11(2), 1574–1587 (2011)
Zhao, Q., Yang, R., Duan, F.: An immune clonal hybrid algorithm for solving stochastic chance-constrained programming. Journal of Computational Information Systems 8(20), 8295–8302 (2012)
Zhang, Z.H., Wang, L., Liao, M.: Adaptive sampling immune algorithm solving joint chance-constrained programming. Journal of Control Theory and Application 11(2), 237–246 (2013)
Chen, C.H.: Efficient sampling for simulation-based optimization under uncertainty. In: Proceedings of the Fourth International Symposium on Uncertainty Modeling and Analysis (ISUMA 2003), pp. 386–391. IEEE Press, New York (2003)
Varghese, B., Poojari, C.A.: Genetic algorithm based technique for solving chance constrained problems arising in risk management. Technical Report, Carisma (2004)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Yang, K., Zhang, Z. (2015). Adaptive Sampling Detection Based Immune Optimization Approach and Its Application to Chance Constrained Programming. In: Sun, H., Yang, CY., Lin, CW., Pan, JS., Snasel, V., Abraham, A. (eds) Genetic and Evolutionary Computing. Advances in Intelligent Systems and Computing, vol 329. Springer, Cham. https://doi.org/10.1007/978-3-319-12286-1_3
Download citation
DOI: https://doi.org/10.1007/978-3-319-12286-1_3
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-12285-4
Online ISBN: 978-3-319-12286-1
eBook Packages: EngineeringEngineering (R0)