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A Multi-objective Generalized Teacher-Learning-Based-Optimization Algorithm
https://orcid.org/http://orcid.org/0000-0002-5982-3138
Teaching–Learning-Based Optimization (TLBO) was developed to solve single-objective optimization problems. It is inspired by the theory of teaching–learning mechanism, which works better for unimodal problems. However, TLBO’s exploration is weak, and hence its performance is not better for multimodal problems. To solve multimodal problems and maintain good exploration, we made significant changes to the operators of basic TLBO. The changes we made are to add multiple teachers in the Teacher phase and Euclidean distance in the Student phase. With this modification to TLBO, we developed a multi-objective variant of TLBO to produce more diverse solutions to solve multi-objective optimization problems. The proposed algorithm, named multi-objective generalized TLBO (MOGTLBO), is tested on standard benchmark test problems. The simulated result of the proposed algorithm is compared with the other existing multi-objective optimization algorithms, and it is found that MOGTLBO performs better comparatively.
A Multi-objective Generalized Teacher-Learning-Based-Optimization Algorithm
https://orcid.org/http://orcid.org/0000-0002-5982-3138
Teaching–Learning-Based Optimization (TLBO) was developed to solve single-objective optimization problems. It is inspired by the theory of teaching–learning mechanism, which works better for unimodal problems. However, TLBO’s exploration is weak, and hence its performance is not better for multimodal problems. To solve multimodal problems and maintain good exploration, we made significant changes to the operators of basic TLBO. The changes we made are to add multiple teachers in the Teacher phase and Euclidean distance in the Student phase. With this modification to TLBO, we developed a multi-objective variant of TLBO to produce more diverse solutions to solve multi-objective optimization problems. The proposed algorithm, named multi-objective generalized TLBO (MOGTLBO), is tested on standard benchmark test problems. The simulated result of the proposed algorithm is compared with the other existing multi-objective optimization algorithms, and it is found that MOGTLBO performs better comparatively.
A Multi-objective Generalized Teacher-Learning-Based-Optimization Algorithm
https://orcid.org/http://orcid.org/0000-0002-5982-3138
Teaching–Learning-Based Optimization (TLBO) was developed to solve single-objective optimization problems. It is inspired by the theory of teaching–learning mechanism, which works better for unimodal problems. However, TLBO’s exploration is weak, and hence its performance is not better for multimodal problems. To solve multimodal problems and maintain good exploration, we made significant changes to the operators of basic TLBO. The changes we made are to add multiple teachers in the Teacher phase and Euclidean distance in the Student phase. With this modification to TLBO, we developed a multi-objective variant of TLBO to produce more diverse solutions to solve multi-objective optimization problems. The proposed algorithm, named multi-objective generalized TLBO (MOGTLBO), is tested on standard benchmark test problems. The simulated result of the proposed algorithm is compared with the other existing multi-objective optimization algorithms, and it is found that MOGTLBO performs better comparatively.
A Multi-objective Generalized Teacher-Learning-Based-Optimization Algorithm
J. Inst. Eng. India Ser. B
Ram, Satya Deo Kumar (author) / Srivastava, Shashank (author) / Mishra, K. K. (author)
Journal of The Institution of Engineers (India): Series B ; 103 ; 1415-1430
2022-10-01
16 pages
Article (Journal)
Electronic Resource
English
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