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
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6.5K
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292
Authors
161
Institutions
Publications per year
2017–2026
29
Variants
Multi-start Methods
Parents
108
Lessons Learned from Quantitative Dynamical Modeling in Systems Biology
Andreas Raue, Marcel Schilling, Julie Bachmann et al. · PLoS ONE · 2013 · 361 citations · Full text
Life Sciences, Systems Biology, Biological Systems Modeling +13
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The University of Texas at Austin
Austin, United States
5 h-index
6 publications
841 citations

Cornell University
Ithaca, United States
4 h-index
6 publications
1.3K citations
The University of Texas at Dallas
Richardson, United States
4 h-index
8 publications
269 citations
Fudan University
Shanghai, China
4 h-index
16 publications
510 citations
University of California, Berkeley
Berkeley, United States
4 h-index
5 publications
198 citations

Universidad Pública de Navarra (UPNA)
Pamplona, Spain
3 h-index
3 publications
49 citations

Kaunas University of Technology
Kaunas, Lithuania
3 h-index
3 publications
184 citations
Texas State University
San Marcos, United States
3 h-index
3 publications
753 citations
1–8 of 161
5 publications · 1.6K citations
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4 publications · 174 citations
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4 publications · 208 citations
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3 publications · 201 citations
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3 publications · 96 citations
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