{"id":6631,"date":"2026-08-16T21:45:45","date_gmt":"2026-08-16T13:45:45","guid":{"rendered":"\/jase\/?post_type=tkuisotope&p=6631"},"modified":"2026-08-23T15:23:24","modified_gmt":"2026-08-23T07:23:24","slug":"mtlbo-a-multi-objective-multi-course-teaching-learning-based-optimization-algorithm","status":"publish","type":"tkuisotope","link":"\/jase\/?tkuisotope=mtlbo-a-multi-objective-multi-course-teaching-learning-based-optimization-algorithm","title":{"rendered":"MTLBO: A Multi-objective Multi-course Teaching-Learning-based Optimization Algorithm"},"content":{"rendered":"\n
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<\/i> 2018<\/a><\/p>\n<\/div>\n\n\n\n

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<\/i> Volume 21, Issue 3<\/a><\/p>\n<\/div>\n\n\n\n

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Wenwen Lin1<\/sup><\/i><\/a>, Lei Wang1<\/sup>, Guangdong Tian2<\/sub>, Yuejun Zhang1<\/sup><\/p>\n\n\n\n

1<\/sup>Faculty of Mechanical Engineering & Mechanics, Ningbo University, Ningbo 315211, P.R. China<\/p>\n\n\n\n

2<\/sup>Transportation College, Jilin University, Changchun 130022, P.R. China<\/p>\n<\/div>\n\n\n\n

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Received:\u00a0December 12, 2017
Accepted:\u00a0January 27, 2018
Publication Date:\u00a0August 16, 2018<\/p>\n<\/div>\n<\/div>\n\n\n\n

\"\u4e0a\u50b3\u5716\u7247\"\n\n\n

An example of crowding distance calculation.<\/p>\n<\/div>\n<\/div>\n\n\n\n

<\/i> Copyright <\/strong>The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0)<\/a>, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.<\/p>\n\n\n\n

Download Citation:  BibTeX <\/a>| http:\/\/dx.doi.org\/10.6180\/jase.201809_21(3).0004<\/a>  <\/p>\n\n\n\n

Download PDF<\/a><\/p>\n\n\n\n

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This paper presents a multi-objective evolutionary algorithm, which is called multi-course teaching-learning-based optimization algorithm. It consists of four phases: teacher, leaner, teacher training, and exchange student phases. Three novel modifications are introduced into the basic teaching-learning-based optimization algorithm. It helps diversify evolutionary population, enhance stochastic search ability, and avoid premature convergence. Eight widely used benchmark problems are employed in order to investigate the performance of the proposed algorithm. The experimental results indicate that it finds better Pareto optimal solutions than some other state-of-the-art algorithms for the majority of problems.<\/p>\n\n\n\n

Keywords: Teaching-learning-based Optimization, Multi-objective Optimization, Evolutionary Algorithm, Heuristic Algorithm<\/em><\/p>\n\n\n\n

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