Surrogate-Assisted Neighborhood Search With Only a Few Weight Vectors for Expensive Large-Scale Multiobjective Binary Optimization

Abstract

Large-scale multiobjective binary optimization problems (MBOPs) often occur in real-world applications, where the function evaluation can only be performed through computationally expensive simulations, which renders standard exact and heuristic methods ineffective. Aggregation-based surrogate-assisted multiobjective evolutionary algorithms have been developed and shown to be promising for solving such problems. They define a set of uniformly distributed weight vectors as search directions, on which all search resources are placed to perform the evolution. However, the Pareto fronts of large-scale MBOPs are discrete and nonuniform. As a result, many weight vectors are useless, thus a lot of search resources are wasted. To address this challenge, we propose a surrogate-assisted neighborhood search (SANS) for expensive large-scale multiobjective binary optimization. SANS uses only a few weight vectors to save search resources while maintaining an adequate diversity. To further utilize the limited search resources, a Q-learning-based method is designed to dynamically allocate search resources to weight vectors. Furthermore, a surrogate-assisted variable neighborhood search is developed to speed up the search without getting trapped in a local optimum prematurely. To robustly and reliably predict the quality of the found solutions, global and local surrogate models are trained by different training samples and then work collaboratively. The experimental results have demonstrated the superiority of SANS over seven state-of-the-art algorithms on the MBOPs with up to 1000 decision variables using only 500 real solution evaluations.