A novel constraint-handling technique based on dynamic weights for constrained optimization problems
- 337 Downloads
Bi-objective constraint-handling technique may be one of the most promising constraint techniques for constrained optimization problems. It regards the constraints as an extra objective and using Pareto ranking as selection operator. These algorithms achieve a good convergence by utilizing potential infeasible individuals, but not be good at maintaining the diversity of the population. It is significant to balance the diversity of the population and the convergence of the algorithm. This paper proposes a novel constraint-handling technique based on biased dynamic weights for constrained evolutionary algorithm. The biased weights are used to select different individuals with low objective values and low degree of constraint violations. Furthermore, along with the evolution, more emphasis is placed on the individuals with lower objective values and lower degree of constraint violations by adjusting the biased weights dynamically, which forces the search to a promising feasible region. Thus, the proposed algorithm can keep a good balance between the convergence and the diversity of the population. Moreover, we compared the proposed algorithm with other state-of-the-art algorithms on 42 benchmark problems. The experimental results showed the reliability and stabilization of the proposed algorithm.
KeywordsConstraint-handling technique Evolutionary algorithm Tchebycheff approach Constrained optimization
This work was supported in part by the Natural Science Foundation of China under Grant 61673121, in part by the Projects of Science and Technology of Guangzhou under Grant 201508010008.
Compliance with ethical standards
Conflict of interest
No conflict of interest exits in the submission of this manuscript, and manuscript is approved by all authors for publication.
- Brest J, Boškovič B, Žumer V (2010) An improved self-adaptive differential evolution algorithm in single objective constrained real-parameter optimization. In: Evolutionary computation, pp 1–8Google Scholar
- Datta R, Deb K (2012) An adaptive normalization based constrained handling methodology with hybrid bi-objective and penalty function approach. In: 2012 IEEE congress on evolutionary computation. IEEE, pp 1–8Google Scholar
- Demšar J (2006) Statistical comparisons of classifiers over multiple data sets. J Mach Learn Res 7:1–30Google Scholar
- Gu F, Hl Liu, Tan KC (2012) A multiobjective evolutionary algorithm using dynamic weight design method. Int J Innov Comput Inf Control 8(5B):3677–3688Google Scholar
- Hinterding R, Michalewicz Z (1998) Your brains and my beauty: parent matching for constrained optimisation. In: Evolutionary computation proceedings, the 1998 IEEE international conference on computational intelligence. IEEE, pp 810–815Google Scholar
- Joines J, Houck CR et al (1994) On the use of non-stationary penalty functions to solve nonlinear constrained optimization problems with gas. In: Proceedings of the first IEEE conference on evolutionary computation, 1994. IEEE world congress on computational intelligence. IEEE, pp 579–584Google Scholar
- Kukkonen S, Lampinen J (2006) Constrained real-parameter optimization with generalized differential evolution. In: IEEE congress on evolutionary computation, pp 207–214Google Scholar
- Li X, Zhang G (2014) Biased multiobjective optimization for constrained single-objective evolutionary optimization. In: 2014 11th World congress on intelligent control and automation (WCICA). IEEE, pp 891–896Google Scholar
- Li Z, Liang JJ, He X, Shang Z (2010) Differential evolution with dynamic constraint-handling mechanism. In: Evolutionary computation, pp 1–8Google Scholar
- Liang J, Runarsson TP, Mezura-Montes E, Clerc M, Suganthan P, Coello CC, Deb K (2006) Problem definitions and evaluation criteria for the cec 2006 special session on constrained real-parameter optimization. J Appl Mech 41:8Google Scholar
- Mallipeddi R, Suganthan PN (2010) Problem definitions and evaluation criteria for the CEC 2010 competition on constrained real-parameter optimization. Nanyang Technological UniversityGoogle Scholar
- Mezura-Montes E, Coello CAC (2006) A survey of constraint-handling techniques based on evolutionary multiobjective optimization. In: Workshop paper at PPSNGoogle Scholar
- Michalewicz Z, Attia N (1994) Evolutionary optimization of constrained problems. In: Proceedings of the 3rd annual conference on evolutionary programming, Citeseer, pp 98–108Google Scholar
- Takahama T, Sakai S (2010) Constrained optimization by the \(\varepsilon \) constrained differential evolution with an archive and gradient-based mutation. In: Evolutionary computation, pp 389–400Google Scholar
- Tasgetiren MF, Suganthan P (2006) A multi-populated differential evolution algorithm for solving constrained optimization problem. In: IEEE congress on evolutionary computation (CEC 2006). IEEE, pp 33–40Google Scholar