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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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Main Authors:	Tan, J.D., Dahari, M., Koh, S.P., Koay, Y.Y., Abed, I.A.
Format:	Article
Published:	Elsevier 2017
Subjects:	TK Electrical engineering. Electronics Nuclear engineering
Online Access:	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
_version_	1643690447548186624
score	13.211869

A new experiential learning electromagnetism-like mechanism for numerical optimization

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