A5053043471- Metaheuristic Optimization Algorithms Research
- Evolutionary Algorithms and Applications
- Advanced Multi-Objective Optimization Algorithms
- Matrix Theory and Algorithms
- Electromagnetic Scattering and Analysis
- Power Systems and Renewable Energy
- Optimization and Packing Problems
- Optimization and Search Problems
- Transportation Planning and Optimization
- Advanced Manufacturing and Logistics Optimization
- Vehicle Routing Optimization Methods
- Traffic control and management
- Advanced Algorithms and Applications
- Robotic Path Planning Algorithms
- Traffic Prediction and Management Techniques
- Electromagnetic Simulation and Numerical Methods
- Antenna Design and Optimization
- Hydraulic and Pneumatic Systems
- Artificial Immune Systems Applications
- Evolution and Genetic Dynamics
- Electrical Fault Detection and Protection
- Advanced Optimization Algorithms Research
- Advanced Thermoelectric Materials and Devices
- Insect Pheromone Research and Control
- Satellite Communication Systems
North China Electric Power University
2025
First Affiliated Hospital of Zhengzhou University
2025
Xi'an Jiaotong University
2013-2024
Shanghai Huayi Group (China)
2024
University of Electronic Science and Technology of China
2008-2014
China Electronics Technology Group Corporation
2012
Inner Mongolia University
2007
Inner Mongolia University of Technology
2006
Particle swarm optimization (PSO) has been proved to be an effective tool for function optimization. Its performance depends heavily on the characteristics of employed exemplars. This necessitates considering both fitness and distribution exemplars in designing PSO algorithms. Following this idea, we propose a novel variant, called scatter learning algorithm (SLPSOA) multimodal problems. SLPSOA contains some new algorithmic features while following basic framework PSO. It constructs exemplar...
Intrahepatic cholangiocarcinoma (iCCA) and other subtypes of primary liver cancer (PLC) have overlapping clinical manifestations radiological characteristics. The objective this study was to evaluate the efficacy deep learning (DL) radiomics analysis, performed using computed tomography (CT) magnetic resonance imaging (MRI), in diagnosing iCCA within PLC. 178 pathologically confirmed PLC patients (training cohort: test cohort = 124: 54) who underwent both CT MRI examinations enrolled....
As a typical model-based evolutionary algorithm, estimation of distribution algorithm (EDA) possesses unique characteristics and has been widely applied in global optimization. However, the commonly used Gaussian EDA (GEDA) usually suffers from premature convergence, which severely limits its search efficiency. This paper first systematically analyzes reasons for deficiency traditional GEDA, then tries to enhance performance by exploiting evolution direction, finally develops new GEDA...
Cooperative coevolution (CC) has shown great potential for solving large-scale optimization problems (LSOPs). However, traditional CC algorithms often waste part of the computation resource (CR) as they equally allocate CR among all subproblems. The recently developed contribution-based improve ones to a certain extent by adaptively allocating according some heuristic rules. Different from existing works, this paper explicitly constructs mathematical model allocation (CRA) problem in and...
The micro thermoelectric device (m-TED) boasts features such as adjustable volume, straightforward structure, and precise, rapid temperature control, positioning it the only current solution for managing of microelectronic systems. It is extensively utilized in 5G optical modules, laser lidars, infrared detection. Nevertheless, size m-TED diminishes, growing proportion interface damages device's operational reliability, constraining advancement m-TED. In this study, we used commercially...
Decomposition plays a significant role in cooperative coevolution (CC), which shows great potential large-scale black-box optimization (LSBO). However, current learning-based decomposition algorithms require many fitness evaluations (FEs) to detect variable interdependencies and encounter the difficulty of threshold setting. To address these issues, this study proposes an efficient adaptive differential grouping (EADG) algorithm. Instead homogeneously tackling different types LSBO instances,...
Many complex networks have been shown to community structures. Detecting those structures is very important for understanding the organization and function of networks. Because this problem NP-hard, it appropriate resort evolutionary algorithms. Chemical reaction optimization (CRO) a novel algorithm inspired by interactions among molecules during chemical reactions. In paper, we propose CRO variant named dual-representation (DCRO) address detection problem. DCRO encodes solution in two...
Abstract By remarkably reducing real fitness evaluations, surrogate-assisted evolutionary algorithms (SAEAs), especially hierarchical SAEAs, have been shown to be effective in solving computationally expensive optimization problems. The success of SAEAs mainly profits from the potential benefit their global surrogate models known as “blessing uncertainty” and high accuracy local models. However, performance leaves room for improvement on high-dimensional problems since now it is still...
Satellites offer many services through communication with stations, such as tracking, navigation, telecommand uplink, earth observation, etc. How to coordinate these is referred the satellite range scheduling problem (SRSP). In research, it found that only resources (referring time slots of stations) requested by more than one simultaneously influence results. These are called critical and selected elements, which makes some jobs optimally served in advance be decomposable into a multi-stage...
Taking "divide-and-conquer" as a basic idea, cooperative coevolution (CC) has shown promising prospect in large scale global optimization. However, its high requirement on the decomposition accuracy can hardly be satisfied practice. Directing against this issue, study proposes bi-hierarchical (BHCC), which tolerate certain degree of error. Besides cooperation among sub-problems conventional CC, BHCC introduces kind between and overall problem. By systematically exploiting excellent...
In this paper, we present an ant colony optimization (ACO) approach to solve the multiple-choice multidimensional knapsack problem (MMKP). This concerns many real life problems, and is hard due its strong constraints NP-hard property. The ACO given in paper follows algorithmic scheme of max-min system, but has some new features with respect characteristics MMKP. First, a single-group-oriented solution construction method proposed, which allows ants generate solutions efficiently. Second,...
Estimation of distribution algorithm (EDA) is a kind typical model-based evolutionary (EA). Although possessing competitive advantages in theoretical analysis, current EDAs may encounter premature convergence due to the rapid shrinkage search range and relatively low sampling efficiency. Focusing on continuous with Gaussian models, this paper proposes novel probability density estimator which can adaptively enlarge variances thus endow EDA flexible behavior. For estimated density, reflecting...