What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Mathematical optimization
- Machine learning
Seyedali Mirjalili spends much of his time researching Mathematical optimization, Benchmark, Particle swarm optimization, Algorithm and Genetic algorithm.
His works in Constrained optimization, Metaheuristic, Multi-objective optimization, Optimization problem and Gravitational search algorithm are all subjects of inquiry into Mathematical optimization.
His study on Test functions for optimization is often connected to Test case and Trigonometric functions as part of broader study in Optimization problem.
His Benchmark research includes elements of Convergence and Data mining.
The Particle swarm optimization study combines topics in areas such as Evolutionary algorithm, Evolution strategy and Heuristic.
His research in Algorithm focuses on subjects like Population-based incremental learning, which are connected to Global optimization.
His most cited work include:
- Grey Wolf Optimizer (3965 citations)
- The Whale Optimization Algorithm (2442 citations)
- Moth-flame optimization algorithm (1199 citations)
What are the main themes of his work throughout his whole career to date?
His scientific interests lie mostly in Mathematical optimization, Particle swarm optimization, Artificial intelligence, Algorithm and Metaheuristic.
His research on Mathematical optimization often connects related areas such as Benchmark.
His work deals with themes such as Genetic algorithm, Robustness and Heuristic, which intersect with Particle swarm optimization.
His Artificial intelligence research is multidisciplinary, incorporating elements of Machine learning and Pattern recognition.
His Optimization problem, Search algorithm and Bat algorithm study in the realm of Algorithm interacts with subjects such as Binary number.
Within one scientific family, Seyedali Mirjalili focuses on topics pertaining to Swarm intelligence under Metaheuristic, and may sometimes address concerns connected to Salp swarm algorithm.
He most often published in these fields:
- Mathematical optimization (46.67%)
- Particle swarm optimization (44.17%)
- Artificial intelligence (28.33%)
What were the highlights of his more recent work (between 2018-2020)?
- Artificial intelligence (28.33%)
- Algorithm (31.67%)
- Metaheuristic (23.33%)
In recent papers he was focusing on the following fields of study:
Seyedali Mirjalili mostly deals with Artificial intelligence, Algorithm, Metaheuristic, Particle swarm optimization and Mathematical optimization.
His studies in Artificial intelligence integrate themes in fields like Swarm intelligence, Machine learning and Optimisation algorithm.
His research integrates issues of Swarm behaviour and Feature selection in his study of Algorithm.
His Metaheuristic research incorporates elements of Optimization algorithm and Genetic algorithm.
As a part of the same scientific study, Seyedali Mirjalili usually deals with the Particle swarm optimization, concentrating on Curse of dimensionality and frequently concerns with Test functions for optimization, Generator, Euclidean distance and Bat algorithm.
Many of his studies involve connections with topics such as Benchmark and Mathematical optimization.
Between 2018 and 2020, his most popular works were:
- Harris hawks optimization: Algorithm and applications (565 citations)
- Equilibrium optimizer: A novel optimization algorithm (152 citations)
- Binary grasshopper optimisation algorithm approaches for feature selection problems (135 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Computer network
Seyedali Mirjalili focuses on Metaheuristic, Algorithm, Artificial intelligence, Benchmark and Genetic algorithm.
His Metaheuristic study incorporates themes from Mutation operator, Particle swarm optimization and Feature selection.
His Particle swarm optimization study improves the overall literature in Mathematical optimization.
His work in Artificial intelligence covers topics such as Machine learning which are related to areas like Optimization problem and Local optimum.
His Benchmark research includes themes of Simulated annealing and Cuckoo search.
His research in Genetic algorithm intersects with topics in Decision tree, Data stream mining, Data mining, Supervised learning and Crossover.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
