Modified teaching-learning-based optimization algorithm for multi-objective optimization problems

Abstract

When solving multi-objective optimization problems, an important issue is how to promote convergence and distribution simultaneously. To address the above issue, a novel optimization algorithm, named as multi-objective modified teaching-learning-based optimization (MOMTLBO), is proposed. Firstly, a grouping teaching strategy based on pareto dominance relationship is proposed to strengthen the convergence efficiency. Afterward, a diversified learning strategy is presented to enhance the distribution. Meanwhile, differential operations are incorporated to the proposed algorithm. By the above process, the search ability of the algorithm can be encouraged. Additionally, a set of well-known benchmark test functions including ten complex problems proposed for CEC2009 is used to verify the performance of the proposed algorithm. The results show that MOMTLBO exhibits competitive performance against other comparison algorithms. Finally, the proposed algorithm is applied to the aerodynamic optimization of airfoils.

Research on the development of college students’ sports program based on multi-objective optimization algorithm

“…As one of the most cited algorithms in the field of evolutionary multi-objective optimization, NSGA-II has a very good performance in low-dimensional multi-objective optimization problems by virtue of its concise and effective selection and evolution mechanisms with efficient solution efficiency [19].…”