A5102005399- Metaheuristic Optimization Algorithms Research
- Advanced Multi-Objective Optimization Algorithms
- Evolutionary Algorithms and Applications
- Topology Optimization in Engineering
- Optimal Experimental Design Methods
- Advanced Algorithms and Applications
- Industrial Technology and Control Systems
- Advanced Manufacturing and Logistics Optimization
- Heat Transfer and Optimization
- Advanced Computational Techniques and Applications
- Educational Technology and Assessment
- Advanced Sensor and Control Systems
- Machine Learning in Bioinformatics
- Fractal and DNA sequence analysis
- Advanced Control Systems Optimization
- Machine Learning and ELM
- Wireless Sensor Networks and IoT
- Advanced Decision-Making Techniques
- Process Optimization and Integration
- Genomics and Phylogenetic Studies
- Vehicle Routing Optimization Methods
- Energy Efficient Wireless Sensor Networks
- Simulation and Modeling Applications
- Robotic Path Planning Algorithms
- Robotics and Sensor-Based Localization
Xiangtan University
2015-2024
Hengyang Normal University
2016-2023
Guiyang Medical University
2023
Guilin Medical University
2022-2023
Affiliated Hospital of Guizhou Medical University
2023
Guangdong Polytechnic of Science and Technology
2023
Hunan Institute of Technology
2019-2022
Hunan Entry-Exit Inspection and Quarantine Bureau
2018
Institute of Information Engineering
2006-2015
Ministry of Education of the People's Republic of China
2015
Balancing convergence and diversity plays a key role in evolutionary multiobjective optimization (EMO). Most current EMO algorithms perform well on problems with two or three objectives, but encounter difficulties their scalability to many-objective optimization. This paper proposes grid-based algorithm (GrEA) solve problems. Our aim is exploit the potential of approach strengthen selection pressure toward optimal direction while maintaining an extensive uniform distribution among solutions....
In large-scale multiobjective optimization, too many decision variables hinder the convergence search of evolutionary algorithms. Reducing range space will significantly alleviate this puzzle. With in mind, article proposes a fuzzy framework for optimization. The divides entire process into two main stages: 1) evolution and 2) precise evolution. evolution, we blur original solution to reduce algorithm so that population can quickly converge. degree fuzzification gradually decreases with...
There are usually multiple constraints in constrained multiobjective optimization. Those reduce the feasible area of optimization problems (CMOPs) and make it difficult for current algorithms (CMOEAs) to obtain satisfactory solutions. In order solve this problem, article studies relationship between constraints, then obtains priority according pareto front (PF) single constraint their common PF. Meanwhile, proposes a multistage CMOEA applies priority, which can save computing resources while...
In science and engineering, multiobjective optimization problems (MOPs) usually contain multiple complex constraints, which poses a significant challenge in obtaining the optimal solution. This article aims to solve challenges brought by constraints. First, this analyzes relationship between single-constrained Pareto front (SCPF) their common (PF) subconstrained PF (SubCPF). Next, we discussed SCPF, SubCPF, unconstraint (UPF)'s help constraining (CPF). Then, further discusses what kind of...
In multi-objective evolutionary algorithms (MOEAs), the diversity of Pareto front (PF) is significant. For good can provide more reasonable choices to decision-makers. The PF includes span and uniformity. this paper, we proposed a dynamic crowding distance (DCD) based maintenance strategy (DMS) (DCD-DMS), in which individualpsilas DCD are computed on difference degree between distances different objectives. computes individualspsila dynamically during process population maintenance. Through...