An improved decomposition-based multiobjective evolutionary algorithm with a better balance of convergence and diversity

In decomposition-based multiobjective evolutionary algorithms (MOEAs), a good balance between convergence and diversity is very important to the performance of an algorithm. However, only the aggregation functions enough to achieve a good balance, especially in high-dimensional objective space. So we considered using the value of related acute angle between a solution and a direction vector as an other consider index. This idea is implemented to enhance the famous decomposition-based algorithm, i.e., MOEA/D. The enhanced algorithm is compared to its predecessor and other state-of-the-art algorithms on a several well-known test suites. Our experimental results show that the proposed algorithm performs better than its predecessor in keeping a better balance between the convergence and diversity, and also as effective as other state-of-the-art algorithms. (C) 2017 Published by Elsevier B.V.