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An estimation of distribution algorithm with filtering and learning

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posted on 2021-03-25, 11:23 authored by Lixin Tang, Xiangman Song, Jiyin LiuJiyin Liu, Chang Liu
Estimation of distribution algorithm (EDA) is an efficient population-based stochastic search technique. Since it was proposed, many attempts have been made to improve its performance in the context of nonlinear continuous optimization. However, the success of EDA depends on the accuracy of modeling, the effectiveness of sampling, and the ability of exploration. An effective EDA often needs to take some measures to adjust the model and to guide sampling. In this article, we propose a novel EDA which applies the idea of Kalman filtering to revise the modeling data and a learning strategy to improve sampling. The filtering scheme modifies the modeling data set using an estimation error matrix based on historic solution data. During the sampling process, the learning strategy determines the region to sample next based on the sampling outcomes so far, instead of completely random sampling. The proposed EDA also employs a multivariate probabilistic model based on copula function and can quickly reach the promising area in which the optimal solution is likely to be located. A collection of general benchmark functions are used to test the performance of the proposed algorithm. Computational experiments show that the EDA is effective.

Funding

National Key Research and Development Program of China under Grant 2016YFB0901900

Major International Joint Research Project of the National Natural Science Foundation of China under Grant 71520107004

Major Program of National Natural Science Foundation of China under Grant 71790614

Fund for Innovative Research Groups of the National Natural Science Foundation of China under Grant 71621061

111 Project under Grant B16009

History

School

  • Business and Economics

Department

  • Business

Published in

IEEE Transactions on Automation Science and Engineering

Volume

18

Issue

3

Pages

1478 - 1491

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Version

  • AM (Accepted Manuscript)

Rights holder

© IEEE

Publisher statement

© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Acceptance date

2020-06-05

Publication date

2020-12-22

Copyright date

2021

ISSN

1545-5955

eISSN

1558-3783

Language

  • en

Depositor

Prof Jiyin Liu. Deposit date: 18 March 2021

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