A new experiential learning electromagnetism-like mechanism for numerical optimization
Decision making; Population statistics; Decision-making mechanisms; Electromagnetism-like mechanism algorithms; Electromagnetism-like mechanisms; Experiential learning; Exploitation; Meta heuristics; Numerical optimizations; Optimization techniques; Optimization
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2023
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my.uniten.dspace-230472023-05-29T14:37:34Z A new experiential learning electromagnetism-like mechanism for numerical optimization Tan J.D. Dahari M. Koh S.P. Koay Y.Y. Abed I.A. 38863172300 36975118700 22951210700 57189626122 55568292900 Decision making; Population statistics; Decision-making mechanisms; Electromagnetism-like mechanism algorithms; Electromagnetism-like mechanisms; Experiential learning; Exploitation; Meta heuristics; Numerical optimizations; Optimization techniques; 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 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. � 2017 Elsevier Ltd Final 2023-05-29T06:37:34Z 2023-05-29T06:37:34Z 2017 Article 10.1016/j.eswa.2017.06.002 2-s2.0-85020317796 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85020317796&doi=10.1016%2fj.eswa.2017.06.002&partnerID=40&md5=e1c9d7430f2600d29c8ba88faa20b51a https://irepository.uniten.edu.my/handle/123456789/23047 86 321 333 Elsevier Ltd Scopus |
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Decision making; Population statistics; Decision-making mechanisms; Electromagnetism-like mechanism algorithms; Electromagnetism-like mechanisms; Experiential learning; Exploitation; Meta heuristics; Numerical optimizations; Optimization techniques; Optimization |
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38863172300 Tan J.D. Dahari M. Koh S.P. Koay Y.Y. Abed I.A. |
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Tan J.D. Dahari M. Koh S.P. Koay Y.Y. Abed I.A. |
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Tan J.D. Dahari M. Koh S.P. Koay Y.Y. Abed I.A. A new experiential learning electromagnetism-like mechanism for numerical optimization |
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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 Ltd |
publishDate |
2023 |
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1806426565993562112 |
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13.211869 |