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Estimation distribution algorithms on constrained optimization problems

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  • Gao, Shujun
  • de Silva, Clarence W.

Abstract

Estimation distribution algorithm (EDA) is an evolution technique that uses sampling to generate the offspring. Most developed EDAs focus on solving the optimization problems which only have the constraints of variable boundaries. In this paper, EDAs are proposed for solving the constrained optimization problems (COPs) involving various types of constraints. In particular, a modified extreme elitism selection method is designed for EDAs to handle the constraints. This selection extrudes the role of some top best solutions to pull the mean vector of the Gaussian distribution towards these best solutions and makes EDAs form a primary evolutionary direction. The EDAs based on five different Gaussian distribution with this selection are evaluated using a set of benchmark functions and some engineering design problems. It is found that for solving these problems, the EDA that is based on a single multivariate Gaussian distribution model with the modified extreme elitism selection outperforms the other EDAs and some state-of-the-art techniques.

Suggested Citation

  • Gao, Shujun & de Silva, Clarence W., 2018. "Estimation distribution algorithms on constrained optimization problems," Applied Mathematics and Computation, Elsevier, vol. 339(C), pages 323-345.
    • Handle: RePEc:eee:apmaco:v:339:y:2018:i:c:p:323-345
    DOI: 10.1016/j.amc.2018.07.037
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    References listed on IDEAS

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    1. Santana, Roberto & Bielza, Concha & Larrañaga, Pedro & Lozano, Jose A. & Echegoyen, Carlos & Mendiburu, Alexander & Armañanzas, Rubén & Shakya, Siddartha, 2010. "Mateda-2.0: A MATLAB Package for the Implementation and Analysis of Estimation of Distribution Algorithms," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 35(i07).
    2. Manoj Dhadwal & Sung Jung & Chang Kim, 2014. "Advanced particle swarm assisted genetic algorithm for constrained optimization problems," Computational Optimization and Applications, Springer, vol. 58(3), pages 781-806, July.
    3. Fuqing Zhao & Zhongshi Shao & Junbiao Wang & Chuck Zhang, 2016. "A hybrid differential evolution and estimation of distribution algorithm based on neighbourhood search for job shop scheduling problems," International Journal of Production Research, Taylor & Francis Journals, vol. 54(4), pages 1039-1060, February.
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    Cited by:

    1. Chou, Jui-Sheng & Truong, Dinh-Nhat, 2021. "A novel metaheuristic optimizer inspired by behavior of jellyfish in ocean," Applied Mathematics and Computation, Elsevier, vol. 389(C).
    2. Qiongfang Li & Yao Du & Zhennan Liu & Zhengmo Zhou & Guobin Lu & Qihui Chen, 2022. "Drought prediction in the Yunnan–Guizhou Plateau of China by coupling the estimation of distribution algorithm and the extreme learning machine," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 113(3), pages 1635-1661, September.
    3. Mohd Shareduwan Mohd Kasihmuddin & Mohd. Asyraf Mansor & Md Faisal Md Basir & Saratha Sathasivam, 2019. "Discrete Mutation Hopfield Neural Network in Propositional Satisfiability," Mathematics, MDPI, vol. 7(11), pages 1-21, November.

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