Genetic Algorithms-Based Techniques for Solving Dynamic Optimization Problems with Unknown Active Variables and Boundaries
In this paper, we consider a class of dynamic optimization problems in which the number of active variables and their boundaries vary as time passes (DOPUAVBs). We assume that such changes in different time periods are not known to decision makers due to certain internal and external factors. Here, we propose three variants of genetic algorithm to deal with a dynamic problem class. These proposed algorithms are compared with one another, as well as with a standard genetic algorithm based on the best of feasible generations and feasibility percentage. Experimental results and statistical tests clearly show the superiority of our proposed algorithms. Moreover, the proposed algorithm, which simultaneous addresses two sub-problems of such dynamic problems, shows superiority to other algorithms in most cases.
KeywordsActive Best of feasible generations Dynamic optimization problems Feasibility percentage Genetic algorithms Mask detection
- 1.AbdAllah, A. F. M., Essam, D. L., & Sarker, R.A. (2014). Solving dynamic optimisation problem with variable dimensions. In SEAL 2014. Dunedin, New Zealand: Springer International Publishing.Google Scholar
- 2.Coello Coello, C. A., & Mezura Montes, E. (2002). Constraint-handling in genetic algorithms through the use of dominance-based tournament selection. Advanced Engineering Informatics, 16(3), 193–203.Google Scholar
- 3.Li, C., Yang, S., Nguyen, T. T., Yu, E. L., Yao, X., Jin, Y., et al. (2009). Benchmark generator for CEC’ 2009 competition on dynamic optimization.Google Scholar
- 4.Surjanovic, S., & Bingham, D. (2015). Virtual library of simulation experiments: Test functions and Datasets. January 2015 [cited 2016 April 20]. Retreived from: http://www.sfu.ca/~ssurjano.
- 5.Adorio, E. P., & Diliman, U. P. MVF—Multivariate test functions library in C for unconstrained global optimization.Google Scholar
- 6.Padhye, N., Deb, K., & Mittal, P. An efficient and exclusively-feasible constrained handling strategy for evolutionary algorithms.Google Scholar
- 7.Morrison, R. W. (2003) Performance measurement in dynamic environments. In GECCO workshop on evolutionary algorithms for dynamic optimization problems (pp. 5–8).Google Scholar
- 8.Yang, S., Nguyen, T. T., & Li, C. (2013). Evolutionary dynamic optimization: Test and evaluation environments. In S. Yang & X. Yao (Eds.), Evolutionary computation for dynamic optimization problems (pp. 3–37). Berlin Heidelberg: Springer.Google Scholar
- 9.Corder, G. W., & Foreman, D. I. (2009) Nonparametric statistics for non-statisticians: A step-by-step approach. Wiley.Google Scholar