Abstract Data-driven optimization has found many successful applications in the real world and received increased attention field of evolutionary optimization. Most existing algorithms assume that data used for are always available on a central server construction surrogates. This assumption, however, may fail to hold when must be collected distributed way subject privacy restrictions. paper ai...

Community structure is one of the most significant features in complex networks and community detection a crucial method to analyze structure. Existing representations have characteristics inflexibility easily generate invalid solutions. To address drawbacks, this paper proposed multi-objective evolutionary algorithm based on mixed encoding (MOGAME). The combines locus-based representation labe...

Multi-objective evolutionary algorithm based on decomposition (MOEA/D) has achieved great success in the field of multi-objective optimization. It decomposes a optimization problem into number scalar sub-problems. Each sub-problem is optimized by using information from its neighboring Therefore, neighborhood size each plays an important role MOEA/D. Different sizes are tested this paper. Experi...

This paper addresses the problem of controlling mutation strength in multi-objective evolutionary algorithms. Adaptive parameter control is one major issue in the field of evolutionary computation, and several methods have been proposed and applied successfully for single objective optimization problems. In this study we examine whether these results carry over to the multi-objective case and w...