A dynamic dual-population co-evolutionary algorithm for constrained multi-objective optimization problems

A novel multi-population evolutionary algorithm based on two-stage division and diversity enhancement for constrained multi-objective optimization

A two-stage multi-population evolutionary algorithm with dynamic collaboration for constrained multi-objective optimization problems

Multi-objective narwhal optimizer: a novel algorithm for multi-criterion optimization

The proposed Multi-Objective Narwhal Optimizer (MONO), a Pareto-based extension of the recently developed Narwhal Optimizer, incorporates Pareto dominance, external archive management, adaptive multi-leader guidance, and crowding-distance-based diversity preservation to effectively balance convergence and exploration throughout the search process.

Information-assisted solution generation based dual population constrained multi-objective evolutionary algorithm

A general hybrid framework for many-objective optimization: integrating local search into reference-vector-based evolutionary algorithms

Results show that integrating local search significantly enhances performance, while a principled method for setting hybrid parameters ensures robustness and reproducibility, highlighting the potential of combining mathematical programming techniques with evolutionary algorithms for high-dimensional many-objective optimization problems.

A Two-Stage Many-Objective Evolutionary Algorithm with Survival-Ratio-Based Switching

Many-objective optimization problems (MaOPs) suffer from weakened selection pressure as the number of objectives increases, making it difficult to balance convergence and diversity. Existing two-stage and multi-stage many-objective evolutionary algorithms (MaOEAs) often determine stage transitions using preset schedules or indirect population criteria that may not reflect changes in search productivity. This paper proposes TSEA-SR, a two-stage MaOEA with survival-ratio-based switching, in which the stage transition is determined by offspring survival after environmental selection. Candidate solutions are first pre-screened according to the hyper-dominance degree. The first stage uses the Euclidean norm to promote centripetal convergence, whereas the second uses the harmonic mean to expand coverage of boundary and extreme regions. Pre-screening and competition organized by reference vectors are retained throughout the search, and the transition is triggered only when low offspring survival persists across generations. Experiments on 80 benchmark instances and 12 real-world combinatorial optimization instances compare TSEA-SR with six state-of-the-art MaOEAs. The results demonstrate the competitive performance of TSEA-SR across the evaluated problems, while ablation studies further support the complementary roles of the two stages and the effectiveness of determining the transition from offspring survival.