Cooperative tri-population based evolutionary algorithm for large-scale multi-objective optimization

The high dimensionality of decision variables in large-scale multi-objective optimization problems poses significant challenges for evolutionary algorithms, which often struggle to achieve efficient search, are prone to premature convergence, and require a substantial amount of computing resources to converge to the Pareto Front. To address these challenges, a cooperative tri-population based evolutionary algorithm is proposed in this paper. Firstly, the population is partitioned into three subpopulations according to the Pareto dominant relation and crowdedness, including a subpopulation with well-distributed nondominated individuals, a subpopulation with crowded non-dominated individuals, and a subpopulation with dominated individuals. Three distinct reproduction operators are then applied to each subpopulation. The first subpopulation uses fully informed search-based reproduction to locate the true Pareto Front, while the second subpopulation adopts segment learning-based reproduction to preserve elite segments and promote exploitation. Finally, directional exploration-based reproduction is used for the third subpopulation to explore the search space and promote diversity. The proposed algorithm is capable of exploring and exploiting superior solutions through co-evolution among diverse subpopulations. Experiments are performed on 36 LSMOP benchmarks with up to 50,000 decision variables to validate the effectiveness of the proposed algorithm, which demonstrates superior performance compared to five state-of-the-art algorithms in handling large-scale multi-objective optimization problems.