H-Index & Metrics Best Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science D-index 55 Citations 51,087 164 World Ranking 2200 National Ranking 213

Research.com Recognitions

Awards & Achievements

2016 - IEEE Fellow For contributions to particle swarm optimization algorithms

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Machine learning
  • Algorithm

Yuhui Shi mostly deals with Particle swarm optimization, Mathematical optimization, Artificial intelligence, Multi-swarm optimization and Evolutionary computation. His research integrates issues of Genetic algorithm, Swarm behaviour, Diversity, Benchmark and Evolutionary algorithm in his study of Particle swarm optimization. His Swarm intelligence, Multi-objective optimization and Continuous optimization study, which is part of a larger body of work in Mathematical optimization, is frequently linked to Power and Social animal, bridging the gap between disciplines.

In general Artificial intelligence, his work in Swarm robotics, Crossover and Mutation is often linked to Process linking many areas of study. His work on Imperialist competitive algorithm as part of general Multi-swarm optimization research is frequently linked to Inertia, thereby connecting diverse disciplines of science. His study looks at the relationship between Evolutionary computation and fields such as Tracking, as well as how they intersect with chemical problems.

His most cited work include:

  • A modified particle swarm optimizer (8098 citations)
  • Particle swarm optimization: developments, applications and resources (3208 citations)
  • Parameter Selection in Particle Swarm Optimization (2876 citations)

What are the main themes of his work throughout his whole career to date?

His primary areas of investigation include Mathematical optimization, Particle swarm optimization, Artificial intelligence, Swarm intelligence and Benchmark. Yuhui Shi regularly ties together related areas like Cluster analysis in his Mathematical optimization studies. Multi-swarm optimization is the focus of his Particle swarm optimization research.

His work on Imperialist competitive algorithm as part of general Multi-swarm optimization study is frequently linked to Inertia, bridging the gap between disciplines. His work deals with themes such as Machine learning and Pattern recognition, which intersect with Artificial intelligence. His Benchmark research is multidisciplinary, incorporating elements of Control theory, Optimization algorithm, Algorithm, Differential evolution and Selection.

He most often published in these fields:

  • Mathematical optimization (45.07%)
  • Particle swarm optimization (37.56%)
  • Artificial intelligence (35.68%)

What were the highlights of his more recent work (between 2018-2021)?

  • Artificial intelligence (35.68%)
  • Optimization problem (22.07%)
  • Mathematical optimization (45.07%)

In recent papers he was focusing on the following fields of study:

Artificial intelligence, Optimization problem, Mathematical optimization, Benchmark and Particle swarm optimization are his primary areas of study. His Artificial intelligence study combines topics in areas such as Swarm intelligence and Machine learning. His Optimization problem study integrates concerns from other disciplines, such as Aerodynamics, Control theory, Task, Evolutionary computation and Optimization algorithm.

His Evolutionary computation study combines topics from a wide range of disciplines, such as Data mining and Data analysis. His Mathematical optimization study frequently links to other fields, such as Process. His Particle swarm optimization research is within the category of Algorithm.

Between 2018 and 2021, his most popular works were:

  • Metaheuristic research: a comprehensive survey (109 citations)
  • Cost-sensitive feature selection using two-archive multi-objective artificial bee colony algorithm (46 citations)
  • Feature selection based on brain storm optimization for data classification (31 citations)

In his most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Machine learning
  • Algorithm

His primary areas of study are Optimization problem, Artificial intelligence, Benchmark, Swarm intelligence and Data mining. His Optimization problem study is related to the wider topic of Mathematical optimization. When carried out as part of a general Mathematical optimization research project, his work on Swarm robotics is frequently linked to work in Robot kinematics, therefore connecting diverse disciplines of study.

His Artificial intelligence research integrates issues from Machine learning and Particle swarm optimization. His study explores the link between Particle swarm optimization and topics such as Ant colony optimization algorithms that cross with problems in Metaheuristic algorithms, Adaptive neuro fuzzy inference system and Genetic programming. His Data mining research is multidisciplinary, incorporating perspectives in Node, Evolutionary computation and Cluster analysis.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Best Publications

A modified particle swarm optimizer

Y. Shi;R. Eberhart.
ieee international conference on evolutionary computation (1998)

14359 Citations

Particle swarm optimization: developments, applications and resources

Eberhart;Yuhui Shi.
congress on evolutionary computation (2001)

5709 Citations

Parameter Selection in Particle Swarm Optimization

Yuhui Shi;Russell C. Eberhart.
Evolutionary Programming (1998)

4618 Citations

Comparing inertia weights and constriction factors in particle swarm optimization

R.C. Eberhart;Y. Shi.
congress on evolutionary computation (2000)

3869 Citations

Comparison between Genetic Algorithms and Particle Swarm Optimization

Russell C. Eberhart;Yuhui Shi.
Evolutionary Programming (1998)

2197 Citations

Fuzzy adaptive particle swarm optimization

Yuhui Shi;R.C. Eberhart.
congress on evolutionary computation (2001)

1886 Citations

Tracking and optimizing dynamic systems with particle swarms

R.C. Eberhart;Yuhui Shi.
congress on evolutionary computation (2001)

1260 Citations

Orthogonal Learning Particle Swarm Optimization

Zhi-Hui Zhan;Jun Zhang;Yun Li;Yu-Hui Shi.
IEEE Transactions on Evolutionary Computation (2011)

694 Citations

Recent advances in particle swarm

Xiaohui Hu;Yuhui Shi;R. Eberhart.
congress on evolutionary computation (2004)

620 Citations

Implementation of evolutionary fuzzy systems

Yuhui Shi;R. Eberhart;Yaobin Chen.
IEEE Transactions on Fuzzy Systems (1999)

559 Citations

If you think any of the details on this page are incorrect, let us know.

Contact us

Best Scientists Citing Yuhui Shi

Jun Zhang

Jun Zhang

Chinese Academy of Sciences

Publications: 154

Andries P. Engelbrecht

Andries P. Engelbrecht

Stellenbosch University

Publications: 140

Zhi-Hui Zhan

Zhi-Hui Zhan

South China University of Technology

Publications: 81

Ajith Abraham

Ajith Abraham

Machine Intelligence Research Labs

Publications: 68

Mengjie Zhang

Mengjie Zhang

Victoria University of Wellington

Publications: 63

Jun Sun

Jun Sun

Jiangnan University

Publications: 62

Leonard Barolli

Leonard Barolli

Fukuoka Institute of Technology

Publications: 48

Bing Xue

Bing Xue

Victoria University of Wellington

Publications: 46

Swagatam Das

Swagatam Das

Indian Statistical Institute

Publications: 45

Ganesh K. Venayagamoorthy

Ganesh K. Venayagamoorthy

Clemson University

Publications: 43

Ali Asghar Heidari

Ali Asghar Heidari

National University of Singapore

Publications: 43

Carlos A. Coello Coello

Carlos A. Coello Coello

CINVESTAV

Publications: 42

Witold Pedrycz

Witold Pedrycz

University of Alberta

Publications: 39

Ying Tan

Ying Tan

Peking University

Publications: 39

Haibin Duan

Haibin Duan

Beihang University

Publications: 39

Oscar Castillo

Oscar Castillo

Instituto Tecnológico de Tijuana

Publications: 38

Something went wrong. Please try again later.