TL;DR
A dynamic Bayesian Gaussian mixture model based decision variable grouping evolutionary algorithm that consistently outperforms several state-of-the-art large-scale optimization algorithms with respect to convergence speed, optimization accuracy, and scalability.
Multi-objective Bayesian optimization (MOBO) is effective in identifying the Pareto fronts for expensive black-box problems. However, most current MOBO approaches are limited to low-dimensional decision space due to its exponential sampling complexity. This paper presents decision variable interaction analysis-based MO...
Large-scale multi-objective optimization problems involve high-dimensional decision variables, vast search spaces, and conflicting objectives, which often lead to slow convergence, premature convergence, and poorly distributed solutions when conventional evolutionary algorithms operate under limited evaluation budgets....
Optimizing decision timing is a core component of precise decision-making across multiple domains. However, traditional methods suffer from limitations such as over-reliance on a few core features, imbalance between cost and performance objectives, and weak resistance to interference. To address these issues, this pape...
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.
A heterogeneous population co-evolutionary algorithm (HPCEA) tailored for sparse LSMOPs is proposed, and extensive experiments against six state-of-the-art algorithms across eight benchmark suites and three real-world scenarios demonstrate HPCEA’s superiority.
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