To solve dynamic multi-objective optimization problems (DMOPs), the optimization algorithms are required to track the movement of the Pareto set after the environmental changes effectively. Many prediction-based dynam...
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Extensive research on edge inference has devoted in optimizing service performance for users. However, recent studies have overlooked the desired utility of application service provider (ASP), which is crucial for ach...
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Multimodal multi-objective optimization problems have a many-to-one relationship between the decision space and the objective space. That is, distinct solutions in the decision space share the same objective value. Ho...
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Underwater multi-agent systems face critical hydrodynamic constraints that significantly degrade the performance of conventional constraint optimization algorithms in dynamic fluid environments. To meet the needs of u...
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With the rapid development of machine learning and large data technologies, large-scale optimization problems become more and more common, and traditional optimization algorithms face the challenges of computational c...
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Hyperparameter optimization on Machine Learning models is crucial for their correct refinement. For complex big models (such as Deep Learning models), in which a single training model is supposed to have a very high c...
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Balance control has been evaluated using center of pressure (CoP) and center of mass (CoM). One of the most common approaches in stabilometry is enclosing ellipse to 95% of data using principal component analysis (PCA...
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In response to the black box problem of optimizing corn planting parameters, and considering the shortcomings of traditional methods such as low fitting accuracy and susceptibility to local optima, a MBSHO-BPNN method...
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Multi-task optimization (MTO) algorithms aim to simultaneously solve multiple optimization tasks. Addressing issues such as limited optimization precision and high computational costs in existing MTO algorithms, this ...
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Multi-task optimization (MTO) algorithms aim to simultaneously solve multiple optimization tasks. Addressing issues such as limited optimization precision and high computational costs in existing MTO algorithms, this article proposes a multitask snake optimization (MTSO) algorithm. The MTSO algorithm operates in two phases: first, independently handling each optimization problem;second, transferring knowledge. Knowledge transfer is determined by the probability of knowledge transfer and the selection probability of elite individuals. Based on this decision, the algorithm either transfers elite knowledge from other tasks or updates the current task through self-perturbation. Experimental results indicate that, compared to other advanced MTO algorithms, the proposed algorithm achieves the most accurate solutions on multitask benchmark functions, the five-task and 10-task planar kinematic arm control problems, the multitask robot gripper problem, and the multitask car side-impact design problem. The code and data for this article can be obtained from: https://***/10.5281/zenodo.14197420. Copyright 2025 Li et al. Distributed under Creative Commons CC-BY 4.0
As global demand for sustainable energy solutions intensifies, reducing the environmental impact of energy systems while maintaining cost-effectiveness has become imperative, particularly for emerging energy carriers ...
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As global demand for sustainable energy solutions intensifies, reducing the environmental impact of energy systems while maintaining cost-effectiveness has become imperative, particularly for emerging energy carriers like hydrogen. This article presents a comprehensive review of life cycle assessment (LCA) and multi-objective optimization (MOO) in the context of green hydrogen (H2) systems, focusing on key contributions from 2019 to 2023. The review categorizes the literature into three areas: (i) Recent LCA studies on green H2 production, (ii) MOO studies that include LCA of renewable energy systems, (iii) Research integrating environmental criteria into MOO for green H2 systems. The review highlights significant gaps, particularly the limited depth of environmental detail in LCAs integrated into MOO, compared to standalone environmental LCAs. Additionally, the analysis reveals a lack of end-of-life considerations across all study categories. In terms of optimization, most research adopts a bi-objective framework, typically focusing on economic performance and a single environmental metric (often CO2 emissions), thereby constraining the development of more comprehensive, sustainable systems. The article concludes by offering recommendations: future LCAs of hydrogen systems should incorporate advanced optimization algorithms, broaden their scope to cradle-to-grave assessments, and utilize prospective methods for better anticipation of future environmental impacts.
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