Abstract
Simulation-based optimization is an important tool in science, engineering, business and many other areas. Optimization of a real-world physical system often involves multiple (and sometimes conflicting) objectives. This gives rise to a situation where a set of optimal solutions, also known as the Pareto-optimal front (POF), is applicable. For non-trivial problems, the number of possible solutions is typically very large. This makes it impossible to apply an exhaustive search to find all possible solutions in the POF in a reasonable time. By applying heuristic search algorithms, such as evolutionary algorithms, it is possible to search for Pareto-optimal solutions without having to evaluate the entire search space. While heuristics can help to reduce the number of solutions that need to be evaluated, there is still the issue of having to perform multiple simulation replications due to the stochastic nature of many simulation models. Since simulation is time-consuming, it is important to implement a computing budget allocation scheme to ensure the simulation is completed within a reasonable time. The research presented in this paper examines the impact of dynamic computing budget allocation on the performance of evolutionary algorithms with respect to the quality of solutions. The results show that the use of dynamic computing budget allocation in combination with an integrated evolutionary algorithm has comparable performance while using less computing budget when compared to a standard approach.
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.
Similar content being viewed by others
References
Fujimoto, R., Lunceford, D., Page, E., Uhrmacher, A.: Grand Challenges for Modeling and Simulation: Dagstuhl report, Schloss Dagstuhl. Seminar No 02351 (2002)
Aydt, H., Turner, S., Cai, W., Low, Y.: Symbiotic Simulation Systems: An Extended Definition Motivated by Symbiosis in Biology. In: Proceedings of the 22st International Workshop on Principles of Advanced and Distributed Simulation, pp. 109–116 (2008)
Srinivas, N., Deb, K.: Multi-Objective Optimization Using Evolutionary Algorithms. Wiley (2001)
Trailovic, L., Pao, L.: Computing Budget Allocation for Optimization of Sensor Processing Order in Sequential Multi-sensor Fusion Algorithms. In: Proceedings of the 2001 American Control Conference, pp. 1841–1847 (2001)
Lee, L., Chew, E., Teng, S., Goldsman, D.: Optimal Computing Budget Allocation for Multi-Objective Simulation Models. In: Proceedings of the 2004 Winter Simulation Conference, pp. 586–594 (2004)
Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 6, 182–197 (2002)
Van Veldhuizen, D.: Multiobjective Evolutionary Algorithms: Classifications, Analyses, and New Innovations (1999)
Van Veldhuizen, A., Lamont, G.: Multiobjective Evolutionary Algorithms: Analyzing the State-of-the-Art 8. Massachusetts Institute of Technology (2000)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Tan, W.J., Turner, S.J., Aydt, H. (2012). A Comparison of Multi-objective Evolutionary Algorithms for Simulation-Based Optimization. In: Xiao, T., Zhang, L., Fei, M. (eds) AsiaSim 2012. AsiaSim 2012. Communications in Computer and Information Science, vol 325. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34387-2_8
Download citation
DOI: https://doi.org/10.1007/978-3-642-34387-2_8
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-34386-5
Online ISBN: 978-3-642-34387-2
eBook Packages: Computer ScienceComputer Science (R0)