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Soft Computing
Published in: Soft Computing 4/2016

25-01-2015 | Methodologies and Application

An exact penalty function-based differential search algorithm for constrained global optimization

Authors: Jianjun Liu, K. L. Teo, Xiangyu Wang, Changzhi Wu

Published in: Soft Computing | Issue 4/2016

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Abstract

Differential search (DS) is a recently developed derivative-free global heuristic optimization algorithm for solving unconstrained optimization problems. In this paper, by applying the idea of exact penalty function approach, a DS algorithm, where an S-type dynamical penalty factor is introduced so as to achieve a better balance between exploration and exploitation, is developed for constrained global optimization problems. To illustrate the applicability and effectiveness of the proposed approach, a comparison study is carried out by applying the proposed algorithm and other widely used evolutionary methods on 24 benchmark problems. The results obtained clearly indicate that the proposed method is more effective and efficient over the other widely used evolutionary methods for most these benchmark problems.
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Literature
Ali MM, Zhu WX (2013) A penalty function-based differential evolution algorithm for constrained global optimization. Comput Optim Appl 54(3):707–739MathSciNetCrossRefMATH
Amir G, Xin-She Y, Amir A (2011) Mixed variable structural optimization using Firefly Algorithm. Comput Struct 89:2325–2336CrossRef
Civicioglu P (2012) Transforming geocentric Cartesian coordinates to geodetic coordinates by using differential search algorithm. Comput Geosci 46:229–247CrossRef
Civicioglu P (2013a) Backtracking search optimization algorithm for numerical optimization problems. Appl Math Comput 219(15):8121–8144
Civicioglu P (2013b) Artificial cooperative search algorithm for numerical optimization problems. Inf Sci 229:58–76
Coello CAC (2002) Theoretical and numerical constraint handling techniques used with evolutionary algorithms: a survey of the state of the art. Comput Methods Appl Mech Eng 191:1245–1287MathSciNetCrossRefMATH
Deb K (2000) An efficient constraint handling method for genetic algorithms. Comput Methods Appl Mech Eng 186(2):311–338
Deshpande AM, Phatnani GM, Kulkami AJ (2013) Constraint handling in firefly algorithm. 2013 IEEE international conference on cybernetics (CYBCONF). IEEE, pp 186–190
Efrén M-M, Coello Coello CA (2011) Constraint-handling in nature-inspired numerical optimization: past, present and future. Swarm Evol Comput 1:173–194
Farmani R, Wright JA (2003) Self-adaptive fitness formulation for constrained optimization. IEEE Trans Evol Comput 7:445–455CrossRef
Goswami DK, Chakraborty S (2014) Differential search algorithm-based parametric optimization of electrochemical micromachining processes. Int J Ind Eng Comput 5:41–54
Hamida SB, Schoenauer M (2002) ASCHEA: new results using adaptive segregational constraint handling. In: Proceedings of the congress on evolutionary computation 2002, CEC’2002, vol 1. IEEE Service Center, Piscataway, pp 884–889
Joines J, Houck C (1994a) On the use of non-stationary penalty functions to solve nonlinear constrained optimization problems with GAs. In: Fogel D (ed) Proceedings of the first IEEE conference on evolutionary computation. IEEE Press, Orlando, pp 579–584
Joines J, Houck C (1994b) On the use of non-stationary penalty functions to solve non-linear constrained optimization problems with GAs. In: Proceedings of the first IEEE international conference on evolutionary computation. IEEE Press, pp 579–584
Karaboga D, Akay B (2011) A modified artificial bee colony (ABC) algorithm for constrained optimization problems. Appl Soft Comput 11:3021–3031CrossRef
Koziel S, Michalewicz Z (1999) Evolutionary algorithms, homomorphous mappings, and constrained parameter optimization. Evol Comput 7:19–44CrossRef
Liang JJ, Runarsson TP, Mezura-Montes E (2006) Problem definitions and evaluation criteria for the CEC special session on constrained real-parameter optimization. J Appl Mech 2006:41
Mezura-Montes E, Coello CAC (2005) A simple multimembered evolution strategy to solve constrained optimization problems. IEEE Trans Evol Comput 9(1):1–17CrossRefMATH
Mezura-Montes E, Lopez-Ramirez BC (2007) Comparing bio-inspired algorithms in constrained optimization problems. In: IEEE congress on evolutionary computation, CEC 2007, pp 662–669
Mezura-Montes E, Miranda-Varela ME, Gómez-Ramón RC (2010) Differential evolution in constrained numerical optimization: an empirical study. Inf Sci 180(22):4223–4262
Runarsson TP, Yao X (2000) Stochastic ranking for constrained evolutionary optimization. IEEE Trans Evol Comput 4:284–294CrossRef
Runarsson TP, Yao X (2005) Search biases in constrained evolutionary optimization. IEEE Trans Syst Man Cybern Part C Appl Rev 35:233–243CrossRef
Tahk M-J, Sun B-C (2000) Coevolutionary augmented Lagrangian methods for constrained optimization. IEEE Trans Evol Comput 4(2):114–124CrossRef
Yang XS (2012) Swarm-based metaheuristic algorithms and no-free-lunch theorems. In: Parpinelli R, Lopes HS (eds) Theory and new applications of swarm intelligence. Intech Open Science, pp 1–16
Yen GG (2009) An adaptive penalty function for handling constraint in multi-objective evolutionary optimization. In: Mezura-Montes E (ed) Constraint-handling in evolutionary optimization, in: studies in computational intelligence series, vol 198. Springer, Berlin, pp 121-143. ISBN: 978-3-642-00618-0
Yeniay Ö (2005) Penalty function methods for constrained optimization with genetic algorithms. Math Comput Appl 10(1):45–56MathSciNet
Yu C, Teo KL, Zhang L, Bai Y (2010) A new exact penalty function method for continuous inequality constrained optimization problems. J Ind Manag Optim 6(4):895–910MathSciNetCrossRefMATH
Zahara E, Hu CH (2008) Solving constrained optimization problems with hybrid particle swarm optimization. Eng Optim 40(11):1031–1049MathSciNetCrossRef
Zahara E, Kao YT (2009) Hybrid Nelder–Mead simplex search and particle swarm optimization for constrained engineering design problems. Expert Syst Appl 36(2):3880–3886CrossRef
Zavala AEM, Aguirre AH, Diharce ERV (2005) Constrained optimization via particle evolutionary swarm optimization algorithm (PESO). In: Proceedings of the 2005 conference on genetic and evolutionary computation (GECCO’05), pp 209–216
Metadata
Title
An exact penalty function-based differential search algorithm for constrained global optimization
Authors
Jianjun Liu
K. L. Teo
Xiangyu Wang
Changzhi Wu
Publication date
25-01-2015
Publisher
Springer Berlin Heidelberg
Published in
Soft Computing / Issue 4/2016
Print ISSN: 1432-7643
Electronic ISSN: 1433-7479
DOI
https://doi.org/10.1007/s00500-015-1588-6

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