An adaptive transfer strategy guided by reference vectors for many-objective optimization problems

Many-objective optimization problems involve numerous objective functions, leading to larger and more intricate Pareto fronts. Conventional evolutionary algorithms struggle to sustain diversity as the objectives increase. The strategic distribution of ideal reference vectors across the objective space has partially mitigated this challenge. However, traditional selection methods based on Pareto dominance encounter reduced selection pressure in high-dimensional scenarios, often resulting in non-dominated solution set with significant diversity but prone to local optima. In addressing these issues, this study explores a reference vector-guided adaptive transfer evolutionary algorithm for solving many-objective optimization problems. This approach aims to maintain diversity through the reference vector mechanism while employing a score-based adaptive migration strategy to preserve individuals with superior convergence. The goal is to break free from local optima and converge toward the global Pareto front. The effectiveness of the proposed algorithm is extensively evaluated against seven other prominent evolutionary algorithms. Across 92 experiments conducted on 22 benchmark problems with up to 15 objectives, the results robustly demonstrate the competitiveness and efficacy of the researched algorithm compared to its counterparts.