Being prevalent in real-world scenarios, dynamic constrained multiobjective optimization problems (DCMOP) are hard to solve due to their continuously and slowly variable objectives and constraints. Although researches...
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Stochastic algorithms are critical in addressing complex rural pipe networks and non-convex stochastic optimization problems. With the development of artificial intelligence, large-scale optimization problems that can...
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In recent years, black-box distributed optimization (DBO) has been widely studied to solve complex optimization problems in multi-agent systems, such as hyperparameter optimization of distributed machine learning. How...
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Optimisation of parameters in Genetic algorithms (GA) can improve the speed and accuracy of the solution produced, but well optimised parameters are dependant on the problem being solved, and the substantial additiona...
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The Robust Satisficing (RS) model is an emerging approach to robust optimization, offering streamlined procedures and robust generalization across various applications. However, the statistical theory of RS remains un...
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The Robust Satisficing (RS) model is an emerging approach to robust optimization, offering streamlined procedures and robust generalization across various applications. However, the statistical theory of RS remains unexplored in the literature. This paper fills in the gap by comprehensively analyzing the theoretical properties of the RS model. Notably, the RS structure offers a more straightforward path to deriving statistical guarantees compared to the seminal Distributionally Robust optimization (DRO), resulting in a richer set of results. In particular, we establish two-sided confidence intervals for the optimal loss without the need to solve a minimax optimization problem explicitly. We further provide finite-sample generalization error bounds for the RS optimizer. Importantly, our results extend to scenarios involving distribution shifts, where discrepancies exist between the sampling and target distributions. Our numerical experiments show that the RS model consistently outperforms the baseline empirical risk minimization in small-sample regimes and under distribution shifts. Furthermore, compared to the DRO model, the RS model exhibits lower sensitivity to hyperparameter tuning, highlighting its practicability for robustness considerations. Copyright 2024 by the author(s)
This paper presents an improved Tree Seed optimization Algorithm, namely the Whale optimization Algorithm with Adaptive Search Strategy (WTSA), designed to address the limitations of traditional Tree Seed optimization...
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This paper investigates the challenges of path optimization in the field of tourism planning and proposes an innovative diversified objective function optimization method. By comprehensively considering the tourists&#...
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In engineering practice, multi-objective optimization problems (Multi-objective optimization Problems, MOPs) are common, but multi-objective optimization problems usually cannot be solved directly. In order to solve t...
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Based on the limitations of the Tree Seed Algorithm (TSA), such as its proneness to getting trapped in local optima and its relatively slow convergence speed, this study proposes an improved TSA optimization algorithm...
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When the parameters of material or size of structure are uncertain, the optimization result of the structure will also be uncertain. In this paper, interval mathematics is used to define the uncertain parameters, and ...
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