Aug 2026· Journal of King Saud University: Computer and Information Sciences· Vol 38· 0 citations· 45 references
TL;DR
An objective-wise variable analysis method that first evaluates the sensitivity of each objective to all decision variables, and then comprehensively aggregates the sensitivity information across multiple objectives to estimate the overall importance of decision variables is proposed.
Abstract
Sparse large-scale multiobjective optimization problems are widely encountered in scientific research and engineering applications. However, the enormous search space and the intrinsic sparsity of the Pareto-optimal solutions substantially increase the difficulty of the optimization process. Different from existing approaches that directly rely on all objective values, this paper proposes an objective-wise variable analysis method. Specifically, the analysis method first evaluates the sensitivity of each objective to all decision variables, and then comprehensively aggregates the sensitivity information across multiple objectives to estimate the overall importance of decision variables. As a result, critical variables can be accurately identified to generate initial population, enabling the population to focus on the critical decision subspace efficiently. Furthermore, a non-coordinated mask-real evolution strategy is proposed to fully exploit the potential of mask vector and reduce the interference of real vectors. In this strategy, mask vector and real component evolve at separate update frequencies. This non-coordinate updating mechanism enables the mask structure to better match the corresponding real number vector. Experimental results demonstrate that the proposed algorithm significantly outperforms several state-of-the-art algorithms on the SMOP benchmark suite and exhibits promising potential in four practical real-world applications.
Large-scale sparse multiobjective optimization problems (LSSMOPs) involve a large number of decision variables and Pareto optimal solutions with only a few nonzero variables. However, as the number of decision variables grows, it becomes increasingly challenging to accurately identify the nonzero variables, and optimization performance is adversely affected. To address these issues, this paper proposes an evolutionary algorithm for LSSMOPs. Specifically, we propose a new initialization method capable of generating scores that accurately reflect the importance of variables, and an initial mask vector template that can locate nonzero variables. This leads to the generation of a high-quality initial population. Additionally, this paper introduces a new strategy to calculate the mutation probability for each variable and a novel optimization for real variables based on the Pareto-guided normal distribution, enabling the population to avoid being trapped in local optima and quickly converge to the global optimum. Experimental results from eight benchmark problems and three real-world applications demonstrate that the proposed algorithm achieves superior performance compared with state-of-the-art algorithms.
Jia-Lin Mai, Min-Rong Chen, Guoqiang Zeng et al.· 0 citations
This study introduces a novel constrained multi-objective evolutionary algorithm, termed DPCME, which employs two interacting populations that exchange information, enabling effective global exploration and reducing the risk of convergence to local optima.
Sparse large-scale multi-objective optimization problems (sparse LSMOPs) are highly challenging due to the curse of dimensionality and the inherent sparsity of Pareto optimal solutions. Although co-evolutionary algorithms demonstrate significant potential, existing methods are frequently constrained by static resource allocation and undifferentiated variable grouping strategies. These limitations induce a severe mismatch between computational budgets and evolutionary states, making it difficult to effectively eliminate redundant noise variables while activating critical ones. To bridge these gaps, this paper proposes a heterogeneous population co-evolutionary algorithm (HPCEA) tailored for sparse LSMOPs. First, an adaptive population division strategy is introduced to dynamically adjust the scales of convergence and diversity sub-populations based on real-time evolutionary states, facilitating the on-demand allocation of computational resources. Second, a heterogeneous variable grouping strategy is designed: a correlation grouping approach captures structural correlation patterns in sparse space within the convergence sub-population, whereas a structural grouping approach unearths potential sparse patterns for the diversity sub-population. Finally, guided by these distinct architectures and sparse importance scores, heterogeneous genetic operators precisely manipulate variables to balance rapid convergence and broad exploration. Extensive experiments against six state-of-the-art algorithms across eight benchmark suites and three real-world scenarios demonstrate HPCEA’s superiority.
Unknown authors· Complex & Intelligent Sy...· 0 citations
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.
Regina C. L. C. de Sousa, Dênis E. C. Vargas, Elizabeth F. Wanner et al.· Journal of Heuristics· 0 citations
By employing a difficulty-aware weighting scheme, the approach biases aggregation toward higher-dimensional instances, enabling a more discriminative assessment of scalability, robustness, and performance.
Regina C. L. C. de Sousa, Dênis E. C. Vargas, Elizabeth F. Wanner et al.· Annual Conference on Genetic...· 0 citations