A Large-Scale Multi-objective Brain Storm Optimization Algorithm Based on Direction Vectors and Variance Analysis

Large-scale multi-objective optimization problems (LSMOPs) can lead to the conventional reproduction operator being inefficient for searching. Therefore, we propose a large-scale multi-objective brain storm optimization algorithm based on direction vectors and variance analysis (LMOBSO-DV) to enhance the efficiency of tackling LSMOPs. Specifically, we adopt brain storm optimization (BSO) algorithm using reference vectors to divide the population into subpopulations and guide the individuals i) in each subpopulation to search in promising directions and 2) between subpopulations to maintain diversity. We also design a new mutation operator. On a widely used LSMOPs test suites with 1000 decision variables, 2 objectives, and 3 objectives, we evaluate LMOBSO-DV's effectiveness in comparison to other several state-of-the-art algorithms. The results of the experiment show that our proposed approach, LMOBSO-DV, outperforms the other studied algorithms.