A5100679900- Metaheuristic Optimization Algorithms Research
- Advanced Multi-Objective Optimization Algorithms
- Evolutionary Algorithms and Applications
- Vehicle Routing Optimization Methods
- Transportation Planning and Optimization
- Cryptography and Data Security
- Microgrid Control and Optimization
- Advanced Algorithms and Applications
- Cloud Data Security Solutions
- Transportation and Mobility Innovations
- Vehicular Ad Hoc Networks (VANETs)
- Ecology and Vegetation Dynamics Studies
- Electric Power System Optimization
- IoT and Edge/Fog Computing
- Video Surveillance and Tracking Methods
- Privacy-Preserving Technologies in Data
- Traffic control and management
- Industrial Technology and Control Systems
- Smart Grid Energy Management
- Energy Load and Power Forecasting
- Probabilistic and Robust Engineering Design
- Stochastic Gradient Optimization Techniques
- Automated Road and Building Extraction
- Forest, Soil, and Plant Ecology in China
- Reinforcement Learning in Robotics
Sun Yat-sen University
2015-2024
Northeastern University
2024
Qingdao University
2024
Northwestern Polytechnical University
2023-2024
Yulin University
2023-2024
Xi’an Jiaotong-Liverpool University
2022-2023
Lanzhou University
2023
Fuzhou University
2023
Beijing University of Civil Engineering and Architecture
2021
Beijing University of Technology
2021
The performance of differential evolution (DE) largely depends on its mutation strategy and control parameters. In this paper, we propose an adaptive DE (ADE) algorithm with a new DE/lbest/1 two-level parameter scheme. is variant the greedy DE/best/1 strategy. However, population mutated under guide multiple locally best individuals in instead one globally individual DE/best/1. This beneficial to balance between fast convergence diversity. scheme implemented mainly two steps. first step,...
Cloud workflow scheduling is a significant topic in both commercial and industrial applications. However, the growing scale of has made such problem increasingly challenging. Many current algorithms often deal with small- or medium-scale problems (e.g., less than 1000 tasks) face difficulties providing satisfactory solutions when dealing large-scale problems, due to curse dimensionality. To this aim, article proposes dynamic group learning distributed particle swarm optimization (DGLDPSO)...
Niching techniques have been widely incorporated into evolutionary algorithms (EAs) for solving multimodal optimization problems (MMOPs). However, most of the existing niching are either sensitive to parameters or require extra fitness evaluations (FEs) maintain niche detection accuracy. In this paper, we propose a new automatic technique based on affinity propagation clustering (APC) and design novel differential evolution (DE) algorithm, termed as DE (ANDE), MMOPs. proposed ANDE APC acts...
Multimodal optimization problem (MMOP), which targets at searching for multiple optimal solutions simultaneously, is one of the most challenging problems optimization. There are two general goals solving MMOPs. One to maintain population diversity so as locate global optima many possible, while other increase accuracy found. To achieve these goals, a novel dual-strategy differential evolution (DSDE) with affinity propagation clustering (APC) proposed in this paper. The novelties and...
Nowadays, large-scale optimization problems are ubiquitous in many research fields. To deal with such efficiently, this paper proposes a distributed differential evolution adaptive mergence and split (DDE-AMS) on subpopulations. The novel operators designed to make full use of limited population resource, which is important for optimization. They adaptively performed based the performance During evolution, once subpopulation finds promising region, current worst performing will merge into...
With the rapid development of e-commerce, logistics industry becomes a crucial component in e-commercial ecological chain. Impelled by both economical and environmental benefit, companies demand automated tools more urgently than ever. In this paper, dynamic logistic dispatching system is proposed. The underlying model vehicle routing problem which allows new orders being received as working day progress. feature, practical systems with traditional static models, but also challenging...
Multi-task optimization is a hot research topic in the field of evolutionary computation. This paper proposes an efficient surrogate-assisted multi-task framework (named SaEF-AKT) with adaptive knowledge transfer for optimization. In proposed SaEF-AKT, several tasks which are computationally expensive solved jointly each generation. Surrogate models built based on historical search information task to reduce number fitness evaluations. To improve efficiency, general similarity measure...
Data privacy and utility are two essential requirements in outsourced data storage. Traditional techniques for sensitive protection, such as encryption, affect the efficiency of query evaluation. By splitting attributes associations, database fragmentation can help protect improve utility. In this article, a distributed memetic algorithm (DMA) is proposed enhancing A balanced best random framework designed to achieve high optimization efficiency. order enhance global search, dynamic grouping...
Differential evolution (DE) is one of the most successful evolutionary algorithms (EAs) for global numerical optimization. Like other EAs, maintaining population diversity important DE to escape from local optima and locate a near-global optimum. Using multi-population algorithm representative method avoid early loss diversity. In this paper, we propose (MPDE) which manipulates multiple sub-populations. Different sub-populations in MPDE exchange information via novel mutation operation...
Economic dispatching of generating units in a power system can significantly reduce the energy cost system. However, economic dispatch (ED) problem is highly constrained, and often has disconnected feasible regions because various physical features. Enhancing population diversity critical for evolutionary approach to fully explore exploit regions. In this article, we propose density-enhanced multiobjective solve ED problem. An first transformed into tri-objective optimization problem, then...
Distributed cooperative co-evolution (DCC) is an effective way to solve large-scale optimization problems. The methodology can reduce the optimizing complexity by dividing a problem into small subcomponents, and distributed computation accelerate speed. However, existing DCC algorithms often encounter deficiency in overlapping problems that cannot be ideally divided due unavoidable overlaps between subcomponents. To address this issue, paper proposes algorithm called co-evolutionary...
Taxi dispatch is a critical issue for taxi company to consider in modern life. This paper formulates the problem into taxi-passenger matching model and proposes parallel ant colony optimization algorithm optimize model. As search space large, we develop region-dependent decomposition strategy divide conquer problem. To keep global performance, region defined deal with communications interactions between subregions. The experimental results verify that proposed effective, efficient,...
Pedestrian detection plays a pivotal role in various domains but is still challenging problem nowadays. In this study, we transform the multiple-pedestrian into multimodal optimization and then utilize estimation of distribution algorithm (MEDA) to optimize based on Histograms Oriented Gradients (HOG) feature Support Vector Machines (SVM). Specifically, adopt three-dimensional vector represent rectangular region an image also use it encode individuals. Then, state-of-the-art called MEDA...
In outsourcing data storage, privacy and utility are significant concerns. Techniques such as encryption can protect the of sensitive information but affect efficiency usage accordingly. By splitting attributes associations, database fragmentation privacy. meantime, be improved through grouping high affinity. this paper, a benefit-driven genetic algorithm is proposed to achieve better balance between for fragmentation. To integrate useful in different solutions, matching strategy designed....
There are increasing large-scale optimization problems in science and engineering nowadays. This paper proposes a diversity-based multi-population differential evolution (DB-MPDE) to maintain the population diversity, which is crucial for optimizations. The performance of algorithms sensitive exchanged information involved migration process. In our proposed DB-MPDE algorithm, diversity between sub-populations utilized determine information. Both diverse similar involved. Diverse helps lot...