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

摘要

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 epsilon 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. (C) 2018 Elsevier Inc. All rights reserved.