A5016231511- Metaheuristic Optimization Algorithms Research
- Advanced Multi-Objective Optimization Algorithms
- Evolutionary Algorithms and Applications
- Vehicle Routing Optimization Methods
- Metabolomics and Mass Spectrometry Studies
- Privacy-Preserving Technologies in Data
- Neural Networks and Applications
- Machine Learning and ELM
- Photovoltaic System Optimization Techniques
- Transportation and Mobility Innovations
- Advanced Image and Video Retrieval Techniques
- Gut microbiota and health
- Domain Adaptation and Few-Shot Learning
- Cancer, Hypoxia, and Metabolism
- Transportation Planning and Optimization
- Radar Systems and Signal Processing
- Face and Expression Recognition
- Advanced Algorithms and Applications
- Robotic Path Planning Algorithms
- Advanced Proteomics Techniques and Applications
- Advanced SAR Imaging Techniques
- Scheduling and Optimization Algorithms
- Text and Document Classification Technologies
- Control and Dynamics of Mobile Robots
- Stochastic Gradient Optimization Techniques
Nanjing University of Information Science and Technology
2019-2025
Heilongjiang University of Chinese Medicine
2019-2024
Chongqing Normal University
2024
Southwest Petroleum University
2024
Chaohu University
2024
Chengdu University of Information Technology
2022-2023
Tianjin Hospital
2023
Zhejiang University
2021-2022
Zhejiang Lab
2022
Harbin Institute of Technology
2021
Seeking multiple optima simultaneously, which multimodal optimization aims at, has attracted increasing attention but remains challenging. Taking advantage of ant colony (ACO) algorithms in preserving high diversity, this paper intends to extend ACO deal with optimization. First, combined current niching methods, an adaptive continuous algorithm is introduced. In algorithm, parameter adjustment developed, takes the difference among niches into consideration. Second, accelerate convergence, a...
In pedagogy, teachers usually separate mixed-level students into different levels, treat them differently and teach in accordance with their cognitive learning abilities. Inspired from this idea, we consider particles the swarm as propose a level-based optimizer (LLSO) to settle large-scale optimization, which is still considerably challenging evolutionary computation. At first, strategy introduced, separates number of levels according fitness values treats differently. Then, new exemplar...
Taking the advantage of estimation distribution algorithms (EDAs) in preserving high diversity, this paper proposes a multimodal EDA. Integrated with clustering strategies for crowding and speciation, two versions algorithm are developed, which operate at niche level. Then these equipped three distinctive techniques: 1) dynamic cluster sizing strategy; 2) an alternative utilization Gaussian Cauchy distributions to generate offspring; 3) adaptive local search. The affords potential balance...
Large-scale optimization has become a significant yet challenging area in evolutionary computation. To solve this problem, paper proposes novel segment-based predominant learning swarm optimizer (SPLSO) through letting several particles guide the of particle. First, strategy is proposed to randomly divide whole dimensions into segments. During update, variables different segments are evolved by from exemplars while ones same segment exemplar. Second, accelerate search speed and enhance...
Surrogate-assisted evolutionary algorithms (SAEAs) have become one popular method to solve complex and computationally expensive optimization problems. However, most existing SAEAs suffer from performance degradation with the dimensionality increasing. To this issue, article proposes a classifier-assisted level-based learning swarm optimizer on basis of (LLSO) gradient boosting classifier (GBC) improve robustness scalability SAEAs. Particularly, strategy in LLSO has tight correspondence...
The rapid development of online social networks not only enables prompt and convenient dissemination desirable information but also incurs fast wide propagation undesirable information. A common way to control the spread pollutants is block some nodes, such a strategy may affect service quality network leads high cost if too many nodes are blocked. This paper considers node selection problem as biobjective optimization find subset be blocked so that effect maximized while minimized. To solve...
Large-scale optimization with high dimensionality and computational cost becomes ubiquitous nowadays. To tackle such challenging problems efficiently, devising distributed evolutionary computation algorithms is imperative. this end, paper proposes a swarm optimizer based on special master-slave model. Specifically, in optimizer, the master mainly responsible for communication slaves, while each slave iterates to traverse solution space. An asynchronous adaptive strategy request-response...
Deep convolutional neural network (CNN) shows excellent effectiveness on hyperspectral image (HSI) classification. However, the architecture design of CNN requires abundant expert knowledge and experience, which poses great prohibition to its wide application in real-world engineering. To alleviate issue, this article proposes an evolving block-based (EB-CNN) search optimal based genetic algorithm (GA) automatically. Specifically, two kinds basic blocks with totally six different...
In high dimensional environment, the interaction among particles significantly affects their movements in searching vast solution space and thus plays a vital role assisting particle swarm optimization (PSO) to attain good performance. To this end, paper designs random contrastive (RCI) strategy for PSO, resulting RCI-PSO, tackle large-scale problems (LSOPs) effectively efficiently. Unlike existing mechanisms low-dimensional problems, RCI randomly chooses several different peers from current...
This paper presents a random-based dynamic grouping strategy (RDG) for cooperative coevolution to deal with large scale multi-objective optimization problems (MOPs) by decomposing the whole dimension into several groups of variables an equal size. First, decomposer pool containing different group sizes is designed. Then, size dynamically selected probability in evolution process. The each computed based on historical performance measured C-metric, common metric optimization. Under size,...
High-dimensional problems are ubiquitous in many fields, yet still remain challenging to be solved. To tackle such with high effectiveness and efficiency, this article proposes a simple efficient stochastic dominant learning swarm optimizer. Particularly, optimizer not only compromises diversity convergence speed properly, but also consumes as little computing time space possible locate the optima. In optimizer, particle is updated when its two exemplars randomly selected from current...
Optimization problems become increasingly complicated in the era of big data and Internet Things, which significantly challenges effectiveness efficiency existing optimization methods. To effectively solve this kind problems, paper puts forward a stochastic cognitive dominance leading particle swarm algorithm (SCDLPSO). Specifically, for each particle, two personal best positions are first randomly selected from those all particles. Then, only when position is dominated by at least one ones,...
Abstract High-dimensional optimization problems are increasingly pervasive in real-world applications nowadays and become harder to optimize due interacting variables. To tackle such effectively, this paper designs a random elite ensemble learning swarm optimizer (REELSO) by taking inspiration from human observational theory. First, partitions particles the current into two exclusive groups: group consisting of top best non-elite containing rest based on their fitness values. Next, it...
The photovoltaic (PV) water electrolysis method currently stands as the most promising approach for green hydrogen production. rapid iteration of technologies has significantly affected on technical and economic evaluation In this work, production three advanced silicon is systematically compared first time under climatic conditions Kucha region. All-weather stable control system with optimal charging discharging strategies constructed to realize efficient energy Seven machine learning (ML)...