Adaptive Weighted Aggregation 2: More scalable AWA for multiobjective function optimization

Adaptive Weighted Aggregation (AWA) is a frame work of multi-starting optimization methods based on scalarization for solving multiobjective function optimization problems. It progressively generates new solutions to refine the approximation of the Pareto set or the Pareto front by the subdivision, and iteratively estimates the appropriate weight vector for scalarization in each search by the weight adaptation. Our recent study shows that AWA's solution set combinatorially increases for the number of objectives. In this paper, we propose a new subdivision and weight adaptation scheme of AWA to improve its scalability. Numerical experiments show the effectiveness of the proposed method.