Meta-Knowledge-Assisted Sampling with Variable Sorting for Large-Scale Multi-Objective Optimization

Large-scale multi-objective optimization problems (LSMOPs), characterized by hundreds or thousands of decision variables, present significant challenges for existing multi-objective evolutionary algorithms (MOEAs), which often struggle to balance the trade-off among diversity, convergence, and scalability in the vast decision spaces within limited computational resources. To address these challenges, this paper introduces a novel large-scale MOEA that incorporates two key innovations: the adaptive sorting mutation (ASM) and meta-knowledge-assisted sampling (MKAS) strategies. The ASM adaptively perturbs variable values through ascending and descending sorting mutations to enrich the population’s diversity and enhance the exploration of the search space. Meanwhile, the MKAS employs a neural network to learn and predict promising meta-knowledge, thus to dynamically guide the evolutionary efforts towards the Pareto set. Additionally, the competitive swarm optimizer is integrated to further refine the final population, providing a robust baseline for optimization. The performance of the proposed method is evaluated through extensive experiments on 325 LSMOPs with up to 10,000 decision variables and 9 objectives. The results demonstrate the superior convergence, diversity, and scalability of the proposed method compared to existing state-of-the-art algorithms, highlighting its potential as an effective solution for large-scale and complex multi-objective optimization tasks.