Benchmarking Multi-objective Algorithms

Benchmarking Multi-Objective Algorithms is a systematic evaluation framework that differentiates itself by providing standardized problem instances, performance metrics, and comparative baselines to assess the effectiveness, efficiency, and robustness of algorithms in solving Multi-Objective Markov Decision Processes.

145

Publications

10.7K

Citations

425

Authors

233

Institutions

Publications per year

2017–2026

75

Authors

425

  1. YJ
    Yaochu Jin

    University of Surrey, United Kingdom

    6 publications · 3.3K citations

    6

    h-index

  2. PN
    Ponnuthurai Nagaratnam Suganthan

    Nanyang Technological University, Singapore

    6 publications · 704 citations

    6

    h-index

  3. DB
    Dimo Brockhoff

    Institut national de recherche en sciences et technologies du numérique, France

    6 publications · 158 citations

    6

    h-index

  4. XZ
    Xingyi Zhang

    Anhui University, China

    5 publications · 2.9K citations

    5

    h-index

  5. AP
    Andries P. Engelbrecht

    University of Pretoria, South Africa

    5 publications · 80 citations

    5

    h-index

1–5 of 425

Venues

  1. Swarm and Evolutionary Computation

    9 publications · 934 citations

    9

    h-index

  2. Proceedings of the Genetic and Evolutionary Computation Conference

    Conference · 6 publications · 115 citations

    6

    h-index

  3. arXiv (Cornell University)

    Repository · 5 publications · 351 citations

    5

    h-index

  4. Applied Soft Computing

    5 publications · 175 citations

    5

    h-index

  5. Engineering Optimization

    4 publications · 242 citations

    4

    h-index

1–5