World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
36
Citations
7887
World Ranking
11051
National Ranking
700

Overview

Miqing Li is affiliated with the University of Birmingham in the United Kingdom and has contributed extensively to research in the field of computer science. Their work primarily focuses on advanced multi-objective optimization algorithms, metaheuristic optimization algorithms research, and evolutionary algorithms and applications.

Their research spans several subfields of computer science, notably artificial intelligence, computational theory and mathematics, and software engineering. They have explored topics including advanced software engineering methodologies, software testing and debugging techniques, and software reliability and analysis research.

Among the recent papers authored by Miqing Li are:

  • What Weights Work for You? Adapting Weights for Any Pareto Front Shape in Decomposition-Based Evolutionary Multiobjective Optimisation (2020), published in Evolutionary Computation
  • How to Evaluate Solutions in Pareto-Based Search-Based Software Engineering: A Critical Review and Methodological Guidance (2020), published in IEEE Transactions on Software Engineering

Miqing Li has frequently collaborated with other researchers, including Xin Yao, Yi Xiang, Xiaowei Yang, Chao Bian, and Chao Qian. These collaborations have contributed to research outputs in various evolutionary computation and software engineering topics.

The scientist's work has appeared in several publication venues, with multiple contributions to arXiv (Cornell University), IEEE Transactions on Evolutionary Computation, Zenodo (CERN European Organization for Nuclear Research), ACM Transactions on Software Engineering and Methodology, and the Proceedings of the Genetic and Evolutionary Computation Conference Companion.

The topics most extensively covered in their research include:

  • Advanced Multi-Objective Optimization Algorithms
  • Metaheuristic Optimization Algorithms Research
  • Evolutionary Algorithms and Applications
  • Advanced Software Engineering Methodologies
  • Software Engineering Research
  • Software Testing and Debugging Techniques
  • Software Reliability and Analysis Research

Miqing Li's contributions offer insights across numerous aspects of optimization within software engineering and computational intelligence, reflecting a broad engagement with both theoretical and applied challenges in computer science.

Best Publications

  • A Grid-Based Evolutionary Algorithm for Many-Objective Optimization

    Shengxiang Yang;Miqing Li;Xiaohui Liu;Jinhua Zheng

  • Shift-Based Density Estimation for Pareto-Based Algorithms in Many-Objective Optimization

    Miqing Li;Shengxiang Yang;Xiaohui Liu

  • A benchmark test suite for evolutionary many-objective optimization

    Ran Cheng;Miqing Li;Ye Tian;Xingyi Zhang

  • Evolutionary Multi-Objective Workflow Scheduling in Cloud

    Zhaomeng Zhu;Gongxuan Zhang;Miqing Li;Xiaohui Liu

  • A Vector Angle-Based Evolutionary Algorithm for Unconstrained Many-Objective Optimization

    Yi Xiang;Yuren Zhou;Miqing Li;Zefeng Chen

  • Stable Matching-Based Selection in Evolutionary Multiobjective Optimization

    Ke Li;Qingfu Zhang;Sam Kwong;Miqing Li

  • Quality Evaluation of Solution Sets in Multiobjective Optimisation: A Survey

    Miqing Li;Xin Yao

  • Pareto or Non-Pareto: Bi-Criterion Evolution in Multiobjective Optimization

    Miqing Li;Shengxiang Yang;Xiaohui Liu

  • Bi-goal evolution for many-objective optimization problems

    Miqing Li;Shengxiang Yang;Xiaohui Liu

  • Diversity Comparison of Pareto Front Approximations in Many-Objective Optimization

    Miqing Li;Shengxiang Yang;Xiaohui Liu

  • Diversity Assessment of Multi-Objective Evolutionary Algorithms: Performance Metric and Benchmark Problems [Research Frontier]

    Ye Tian;Ran Cheng;Xingyi Zhang;Miqing Li

  • What weights work for you?: Adapting weights for any pareto front shape in decomposition-based evolutionary multiobjective optimisation

    Miqing Li;Xin Yao

  • Achieving balance between proximity and diversity in multi-objective evolutionary algorithm

    Ke Li;Sam Kwong;Jingjing Cao;Miqing Li

  • Evolutionary Multiobjective Optimization-Based Multimodal Optimization: Fitness Landscape Approximation and Peak Detection

    Ran Cheng;Miqing Li;Ke Li;Xin Yao

  • How to Read Many-Objective Solution Sets in Parallel Coordinates [Educational Forum]

    Miqing Li;Liangli Zhen;Xin Yao

  • SIP: Optimal Product Selection from Feature Models Using Many-Objective Evolutionary Optimization

    Robert M. Hierons;Miqing Li;Xiaohui Liu;Sergio Segura

  • Multi-objective evolutionary simulated annealing optimisation for mixed-model multi-robotic disassembly line balancing with interval processing time

    Yilin Fang;Yilin Fang;Hao Ming;Hao Ming;Miqing Li;Quan Liu;Quan Liu

  • An angle dominance criterion for evolutionary many-objective optimization

    Yuan Liu;Ningbo Zhu;Kenli Li;Miqing Li

  • Spread Assessment for Evolutionary Multi-Objective Optimization

    Miqing Li;Jinhua Zheng

  • Evolutionary many-objective optimization for mixed-model disassembly line balancing with multi-robotic workstations

    Yilin Fang;Yilin Fang;Quan Liu;Quan Liu;Miqing Li;Yuanjun Laili

  • A Comparative Study on Evolutionary Algorithms for Many-Objective Optimization

    Miqing Li;Shengxiang Yang;Xiaohui Liu;Ruimin Shen

Frequent Co-Authors

Xin Yao
Xin Yao Lingnan University
Shengxiang Yang
Shengxiang Yang De Montfort University
Xiaohui Liu
Xiaohui Liu Brunel University London
Yaochu Jin
Yaochu Jin Westlake University
Xingyi Zhang
Xingyi Zhang Anhui University
Robert M. Hierons
Robert M. Hierons University of Sheffield
Sam Kwong
Sam Kwong Lingnan University
Keqin Li
Keqin Li State University of New York at New Paltz
Kenli Li
Kenli Li Hunan University
Hisao Ishibuchi
Hisao Ishibuchi Southern University of Science and Technology

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