A new experiential learning electromagnetism-like mechanism for numerical optimization

The Electromagnetism-like Mechanism algorithm (EM) is a population-based search algorithm which has shown good achievements in solving various types of complex numerical optimization problems so far. To date, the study on experience-based local search mechanism is relatively limited, and there is no...

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主要な著者: Tan, J.D., Dahari, M., Koh, S.P., Koay, Y.Y., Abed, I.A.
フォーマット: 論文
出版事項: Elsevier 2017
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オンライン・アクセス:http://eprints.um.edu.my/17542/
https://doi.org/10.1016/j.eswa.2017.06.002
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spelling my.um.eprints.175422017-07-20T06:59:37Z http://eprints.um.edu.my/17542/ A new experiential learning electromagnetism-like mechanism for numerical optimization Tan, J.D. Dahari, M. Koh, S.P. Koay, Y.Y. Abed, I.A. TK Electrical engineering. Electronics Nuclear engineering The Electromagnetism-like Mechanism algorithm (EM) is a population-based search algorithm which has shown good achievements in solving various types of complex numerical optimization problems so far. To date, the study on experience-based local search mechanism is relatively limited, and there is no study in the literature to integrate experience-based features into the EM. This work introduces an experience-learning feature into the EM for the first time. A new Experiential Learning Electromagnetism-like Mechanism algorithm (ELEM) is proposed in this paper. The ELEM is integrated with two new components. The first component is the particle memory concept which allows the particles to remember the details of their past search experience. The second component is the experience analysing and decision making mechanisms which enables the particles to adjust the settings for the coming iterations. Combining the advantages of this strong exploitation strategy and the powerful exploration mechanism of the EM, the proposed ELEM strikes a good balance in providing well diversified solutions with high accuracy. The results from extensive numerical experiments carried out using 21 challenging test functions show that ELEM is able to provide very competitive solutions and significantly outperforms other optimization techniques. It can thus be concluded from the results that the proposed ELEM performs well in solving high dimensional numerical optimization problems. Elsevier 2017 Article PeerReviewed Tan, J.D. and Dahari, M. and Koh, S.P. and Koay, Y.Y. and Abed, I.A. (2017) A new experiential learning electromagnetism-like mechanism for numerical optimization. Expert Systems with Applications, 86. pp. 321-333. ISSN 0957-4174 https://doi.org/10.1016/j.eswa.2017.06.002 DOI: 10.1016/j.eswa.2017.06.002
institution Universiti Malaya
building UM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaya
content_source UM Research Repository
url_provider http://eprints.um.edu.my/
topic TK Electrical engineering. Electronics Nuclear engineering
spellingShingle TK Electrical engineering. Electronics Nuclear engineering
Tan, J.D.
Dahari, M.
Koh, S.P.
Koay, Y.Y.
Abed, I.A.
A new experiential learning electromagnetism-like mechanism for numerical optimization
description The Electromagnetism-like Mechanism algorithm (EM) is a population-based search algorithm which has shown good achievements in solving various types of complex numerical optimization problems so far. To date, the study on experience-based local search mechanism is relatively limited, and there is no study in the literature to integrate experience-based features into the EM. This work introduces an experience-learning feature into the EM for the first time. A new Experiential Learning Electromagnetism-like Mechanism algorithm (ELEM) is proposed in this paper. The ELEM is integrated with two new components. The first component is the particle memory concept which allows the particles to remember the details of their past search experience. The second component is the experience analysing and decision making mechanisms which enables the particles to adjust the settings for the coming iterations. Combining the advantages of this strong exploitation strategy and the powerful exploration mechanism of the EM, the proposed ELEM strikes a good balance in providing well diversified solutions with high accuracy. The results from extensive numerical experiments carried out using 21 challenging test functions show that ELEM is able to provide very competitive solutions and significantly outperforms other optimization techniques. It can thus be concluded from the results that the proposed ELEM performs well in solving high dimensional numerical optimization problems.
format Article
author Tan, J.D.
Dahari, M.
Koh, S.P.
Koay, Y.Y.
Abed, I.A.
author_facet Tan, J.D.
Dahari, M.
Koh, S.P.
Koay, Y.Y.
Abed, I.A.
author_sort Tan, J.D.
title A new experiential learning electromagnetism-like mechanism for numerical optimization
title_short A new experiential learning electromagnetism-like mechanism for numerical optimization
title_full A new experiential learning electromagnetism-like mechanism for numerical optimization
title_fullStr A new experiential learning electromagnetism-like mechanism for numerical optimization
title_full_unstemmed A new experiential learning electromagnetism-like mechanism for numerical optimization
title_sort new experiential learning electromagnetism-like mechanism for numerical optimization
publisher Elsevier
publishDate 2017
url http://eprints.um.edu.my/17542/
https://doi.org/10.1016/j.eswa.2017.06.002
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score 13.250246