An improved teaching-learning-based optimization for constrained evolutionary optimization

•An efficient subpopulation strategy is designed to increase the diversity of the teacher phase.•A novel ranking differential vector strategy is presented to promote the convergence of the learner phase.•A dynamic weighted sum is formulated to achieve the tradeoff between constraints and objective f...

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Bibliographic Details
Published in:Information sciences 2018-08, Vol.456, p.131-144
Main Authors: Wang, Bing-Chuan, Li, Han-Xiong, Feng, Yun
Format: Article
Language:English
Subjects:
Constrained optimization
Constraints
Convergence
Diversity
Objective function
TLBO
Tradeoff
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Summary:•An efficient subpopulation strategy is designed to increase the diversity of the teacher phase.•A novel ranking differential vector strategy is presented to promote the convergence of the learner phase.•A dynamic weighted sum is formulated to achieve the tradeoff between constraints and objective function.•A simple yet effective restart strategy is presented to settle complicated constraints. When extending a global optimization technique for constrained optimization, we must balance not only diversity and convergence but also constraints and objective function. Based on these two criteria, the famous teaching-learning-based optimization (TLBO) is improved for constrained optimization. To balance diversity and convergence, an efficient subpopulation based teacher phase is designed to enhance diversity, while a ranking-differential-vector-based learner phase is proposed to promote convergence. In addition, how to select the teacher in the teacher phase and how to rank two solutions in the learner phase have a significant impact on the tradeoff between constraints and objective function. To address this issue, a dynamic weighted sum is formulated. Furthermore, a simple yet effective restart strategy is proposed to settle complicated constraints. By adopting the ε constraint-handling technique as the constraint-handling technique, a constrained optimization evolutionary algorithm, i.e., improved TLBO (ITLBO), is proposed. Experiments on a broad range of benchmark test functions reveal that ITLBO shows better or at least competitive performance against other constrained TLBOs and some other constrained optimization evolutionary algorithms.
ISSN:0020-0255
1872-6291
DOI:10.1016/j.ins.2018.04.083