Multi-start Optimization

Multi-Start Optimization is a global optimization strategy that belongs to the class of iterative search methods, distinguished by its systematic initiation of multiple local optimization runs from diverse starting points to increase the likelihood of converging to a global optimum.

108

Publications

6.5K

Citations

292

Authors

161

Institutions

Publications per year

2017–2026

29

Variants

Multi-start Methods

Authors

292

  1. LS
    Leon S. Lasdon

    The University of Texas at Austin, United States

    4 publications · 792 citations

    4

    h-index

  2. JF
    Javier Faulín

    Universidad Pública de Navarra (UPNA), Spain

    4 publications · 96 citations

    4

    h-index

  3. XZ
    Xuan Zeng

    Fudan University, China

    4 publications · 117 citations

    4

    h-index

  4. DZ
    Dian Zhou

    Fudan University, China

    4 publications · 117 citations

    4

    h-index

  5. JP
    John Plummer

    The University of Texas at Austin, United States

    4 publications · 792 citations

    4

    h-index

1–5 of 292

Venues

  1. INFORMS journal on computing

    5 publications · 1.6K citations

    5

    h-index

  2. Journal of Global Optimization

    4 publications · 174 citations

    4

    h-index

  3. Computers & Operations Research

    4 publications · 208 citations

    4

    h-index

  4. Mathematical Programming

    3 publications · 201 citations

    3

    h-index

  5. Evolutionary Computation

    3 publications · 96 citations

    3

    h-index

1–5