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...
详细信息
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...
详细信息
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 ...
详细信息
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
A multi-strategy enhanced version of the escape algorithm (mESC, for short) is proposed to address the challenges of balancing exploration and development stages and low convergence accuracy in the escape algorithm (E...
详细信息
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...
详细信息
Due to the advantages of low emissions and high energy efficiency, hybrid electric vehicles (HEVs) have garnered significant attention in the automotive industry. However, addressing the multi-variable, nonlinear, and...
详细信息
Due to the advantages of low emissions and high energy efficiency, hybrid electric vehicles (HEVs) have garnered significant attention in the automotive industry. However, addressing the multi-variable, nonlinear, and multidimensional optimization challenges arising from the collaborative operation of multiple power sources remains a complex and unresolved issue in HEVs. It is imperative to build an efficient system to achieve an optimal design. Many researchers have already explored various design optimization methods for HEVs. The review aims to integrate and critically analyze the current research status of optimal design methods from three perspectives, offering insights into future developments. It provides valuable guidance and reference for professionals developing, designing, and optimizing HEVs.
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...
详细信息
In addition to achieving record efficiencies of ≈20%, organic photovoltaics (OPV) has to overcome several additional challenges. These include researching environmentally friendly solvents, improving stability, yield...
详细信息
This article introduces a global approach to magnetic topology optimization for hard magnetic materials, aiming to achieve a specific stray field distribution. The proposed method utilizes a hybrid optimization algori...
详细信息
暂无评论