Abstract
A self-adaptive interior penalty method is proposed for the constrained optimization problems by using interior penalty method to handle constraints. A set of interior penalty rules are designed to evaluate feasible solutions and infeasible solutions separately. A self-adaptive penalty factor method is proposed to prevent the interior penalty method from being sensitive to the values of penalty factor and to minimize the interior penalty function value of the optimal solution. As an instance of implementation, a different evolution algorithm is improved by means of the method proposed in this paper, based on which 10 benchmark problems are tested. The numerical solution results indicate that the performance of the method is better than four existing state-of-the-art techniques.
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References
Mezura-Montes, E., Coello, C.: Constraint-handling in nature-inspired numerical optimization: past, present and future. Swarm and Evolutionary Computation 1(4), 173–194 (2011)
Kramer, O.: A review of constraint-handling techniques for evolution strategies. Applied Computational Intelligence and Soft Computing 1, 1–11 (2010)
Tessema, B., Yen, G.: An adaptive penalty formulation for constrained evolutionary optimization. IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans 39(3), 565–578 (2009)
Deb, K.: An efficient constraint handling method for genetic algorithms. Computer Methods in Applied Mechanics and Engineering 186(2-4), 311–338 (2000)
Mezura-Montes, E.: Constraint-handling in evolutionary optimization. Springer, Heidelberg (2009)
Wright, M.: The interior-point revolution in optimization: history, recent developments, and lasting consequences. Bulletin of the American Mathematical Society 42(1), 39–56 (2005)
Wright, M.: The interior-point revolution in constrained optimization. High-Performance Algorithms and Software in Nonlinear Optimization, 359–381 (1998)
Storn, R., Price, K.: Differential Evolution–A Simple and Efficient Heuristic for global Optimization over Continuous Spaces. Journal of Global Optimization 11(4), 341–359 (1997)
Runarsson, T., Yao, X.: Stochastic ranking for constrained evolutionary optimization. IEEE Transactions on Evolutionary Computation 4(3), 284–294 (2000)
Mezura-Montes, E., Coello Coello, C.A., Tun-Morales, E.I.: Simple feasibility rules and differential evolution for constrained optimization. In: Monroy, R., Arroyo-Figueroa, G., Sucar, L.E., Sossa, H. (eds.) MICAI 2004. LNCS (LNAI), vol. 2972, pp. 707–716. Springer, Heidelberg (2004)
Mezura-Montes, E., Coello, C.: A simple multimembered evolution strategy to solve constrained optimization problems. IEEE Transactions on Evolutionary Computation 9(1), 1–17 (2005)
Wang, Y., Zixing, C.: An Adaptive Tradeoff Model for Constrained Evolutionary Optimization. IEEE Transactions on Evolutionary Computation 12(1), 80–92 (2008)
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Chenggang, C., Xiaofei, Y., Tingyu, G. (2014). A Self-adaptive Interior Penalty Based Differential Evolution Algorithm for Constrained Optimization. In: Tan, Y., Shi, Y., Coello, C.A.C. (eds) Advances in Swarm Intelligence. ICSI 2014. Lecture Notes in Computer Science, vol 8795. Springer, Cham. https://doi.org/10.1007/978-3-319-11897-0_37
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DOI: https://doi.org/10.1007/978-3-319-11897-0_37
Publisher Name: Springer, Cham
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