Advanced Multi-Objective Optimization Algorithms
Advanced Multi-Objective Optimization Algorithms is a research topic within Computational Theory and Mathematics. Science Explorer counts 32k research works in it since 1951. 27.1% of them reached the world's top 10% most cited for their field and year.
This cluster of papers focuses on the application of evolutionary algorithms, surrogate modeling, and optimization techniques to solve multiobjective optimization problems. It covers topics such as genetic algorithms, Pareto fronts, Bayesian optimization, and the use of surrogate models like Kriging for engineering design.
- Evolutionary Algorithms
- Multiobjective Optimization
- Surrogate Modeling
- Genetic Algorithm
- Bayesian Optimization
- Pareto Front
- Kriging Metamodeling
- Particle Swarm Optimization
- Hypervolume Indicator
- Engineering Design
- Research works
- 32k fractional, since 1951
- In the world top 10%
- 8.8k per year above
- Top-10% rate
- 27.1% share of its works in the world top 10%
- Growth, 2013–17 → 2018–22
- +25% the tick is no change
Which countries lead Advanced Multi-Objective Optimization Algorithms research?
By volume, China and the United States publish the most (2.5k and 681 works in 2022–2025).
By volume, 2022–2025
- 1 China 2.5k works
- 2 United States 681 works
- 3 India 356 works
- 4 Germany 264 works
- 5 United Kingdom 245 works
- 6 Japan 233 works
- 7 France 190 works
- 8 Türkiye 134 works
- 9 Iran 132 works
- 10 South Korea 117 works
How concentrated that is
The same countries as shares of everything the list above accounts for. A node where two countries do two thirds of the work and one spread evenly across twelve read alike as a ranking and not at all alike here.
Shares of the rows listed above, not of the whole node.
Which institutions lead Advanced Multi-Objective Optimization Algorithms research?
By volume in 2022–2025, Northwestern Polytechnical University publishes the most Advanced Multi-Objective Optimization Algorithms research, followed by Southern University of Science and Technology and Dalian University of Technology.
By volume, 2022–2025
- 1 Northwestern Polytechnical University China 68 works
- 2 Southern University of Science and Technology China 56 works
- 3 Dalian University of Technology China 48 works
- 4 Xidian University China 46 works
- 5 Northeastern University China 46 works
- 6 Huazhong University of Science and Technology China 42 works
- 7 National University of Defense Technology China 36 works
- 8 Beihang University China 34 works
- 9 Shenzhen University China 32 works
- 10 Harbin Institute of Technology China 31 works
Who are the leading researchers in Advanced Multi-Objective Optimization Algorithms?
The most-cited researchers publishing on Advanced Multi-Objective Optimization Algorithms include George E. P. Box, Seyedali Mirjalili and Kalyanmoy Deb.
- 1 George E. P. Box 10k citations
- 2 Seyedali Mirjalili 7.6k citations
- 3 Kalyanmoy Deb 6.7k citations
- 4 Francisco Herrera 6.5k citations
- 5 Witold Pedrycz 5.4k citations
- 6 MengChu Zhou 4.4k citations
Ranked by citations received across their whole record, among researchers with at least three works on this topic.
Where is Advanced Multi-Objective Optimization Algorithms research done?
The largest centres of Advanced Multi-Objective Optimization Algorithms research in 2022–2025 are Beijing (China), Xi'an (China), Shanghai (China) and Wuhan (China). Among places with at least 20 works in it, it is an unusually large share of all research in Palaiseau and Xiangtan.
Largest cities, 2022–2025
Where it is the local speciality
- PalaiseauFR · 24.1 works8.3×
- XiangtanCN · 23.6 works7.8×
Location quotient: how much more of its research is in Advanced Multi-Objective Optimization Algorithms than the world average.
Where is the best place to study Advanced Multi-Objective Optimization Algorithms?
Among universities, judged by research, Torrens University Australia, Northwestern Polytechnical University and Fujian University of Technology score highest, combining excellence, specialisation, size, growth and international reach. Research strength is one signal when choosing where to study; it does not measure teaching.
One dot per university in the table below. The upper left is the interesting corner: small places doing unusually strong work.
| # | University | Score | Top 10% | Specialisation | Works | Growth |
|---|---|---|---|---|---|---|
| 1 | Torrens University Australia Australia | 68.7 | 73.4% | 53.3× | 8 | — |
| 2 | Northwestern Polytechnical University China | 64.3 | 32.2% | 9.2× | 68 | +90.2% |
| 3 | Fujian University of Technology China | 64.2 | 73.8% | 15.5× | 10 | — |
| 4 | Anhui University China | 63.1 | 41.8% | 8.4× | 25 | +246.2% |
| 5 | Southern University of Science and Technology China | 62.3 | 26.6% | 16.1× | 56 | — |
| 6 | De Montfort University United Kingdom | 60.5 | 45.4% | 24.5× | 15 | +74.3% |
| 7 | China University of Geosciences China | 60.4 | 48.3% | 9.5× | 28 | +9.9% |
| 8 | Wenzhou University China | 58.2 | 51.6% | 9.4× | 13 | +86.1% |
| 9 | Shenzhen University China | 57.9 | 30.2% | 6.7× | 32 | +211.5% |
| 10 | Xidian University China | 57.1 | 34.3% | 9.7× | 46 | -3.2% |
Universities only. Score blends excellence (30%), specialisation (25%), size (20%), growth (15%) and international reach (10%), 2015–2022; growth compares 2010–14 with 2015–19.
Is Advanced Multi-Objective Optimization Algorithms research growing?
Output in 2018–2022 was 25% higher than in 2013–2017, peaking in 2025. The fastest-growing topics are Advanced Multi-Objective Optimization Algorithms.
The same series as a ribbon — one cell per year, darker for more. The line above answers how much; this answers when.
Which topics inside it are moving
Growth and decline on one axis around a shared zero. Two lists side by side hide the thing that matters: whether the growth dwarfs the decline, or the other way round.