Infill Criterion Ensemble in Multi-Objective Evolutionary Algorithm for Mixed-Variable Problems

Many real-world optimization problems involve mixed variables, multiple conflicting objectives, and computation-ally expensive evaluations. Such problems are called expensive mixed-variable multi-objective optimization problems (EMV-MOPs). Solving EMVMOPs is challenging due to a complex search space involving mixed variables, balancing conflicts among multiple objectives, and a limited number of function evaluations. In this work, we propose an infill criterion ensemble in surrogate-assisted multi-objective evolutionary algorithm, which primarily consists of two core components considering the convergence to non-dominated front, model uncertainty, and diversity. We use two indicators, ensemble through an adaptive weighted sum, to play a crucial role in enhancing diversity, while local search with hybrid operators contributes to improving local convergence. The experimental results show that our algorithm is competitive compared to other algorithms on benchmark problems.