What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Algorithm
Ali Asghar Heidari focuses on Metaheuristic, Mathematical optimization, Benchmark, Algorithm and Feature selection.
The subject of his Metaheuristic research is within the realm of Artificial intelligence.
Ali Asghar Heidari interconnects Optimization algorithm and Swarm intelligence in the investigation of issues within Artificial intelligence.
His work in the fields of Local optimum overlaps with other areas such as Sine.
Ali Asghar Heidari combines topics linked to Global optimization with his work on Benchmark.
In general Algorithm, his work in SMA* is often linked to Photovoltaic system linking many areas of study.
His most cited work include:
- Harris hawks optimization: Algorithm and applications (565 citations)
- Harris hawks optimization: Algorithm and applications (565 citations)
- An efficient binary Salp Swarm Algorithm with crossover scheme for feature selection problems (240 citations)
What are the main themes of his work throughout his whole career to date?
Mathematical optimization, Benchmark, Local optimum, Artificial intelligence and Swarm behaviour are his primary areas of study.
His Mathematical optimization research focuses on Chaotic and how it connects with Algorithm.
His biological study spans a wide range of topics, including Salp swarm algorithm, Rate of convergence, Optimization problem and Swarm intelligence.
Ant colony optimization algorithms is closely connected to Image segmentation in his research, which is encompassed under the umbrella topic of Local optimum.
Within one scientific family, he focuses on topics pertaining to Machine learning under Artificial intelligence, and may sometimes address concerns connected to Metaheuristic algorithms.
His biological study deals with issues like Particle swarm optimization, which deal with fields such as Genetic algorithm and Artificial neural network.
He most often published in these fields:
- Mathematical optimization (79.75%)
- Benchmark (76.69%)
- Local optimum (68.10%)
What were the highlights of his more recent work (between 2020-2021)?
- Local optimum (68.10%)
- Mathematical optimization (79.75%)
- Benchmark (76.69%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Local optimum, Mathematical optimization, Benchmark, Swarm behaviour and Artificial intelligence.
His Local optimum study also includes fields such as
- Chaotic which is related to area like Algorithm,
- Image segmentation which is related to area like Entropy and Ant colony.
His study in the field of Local search, Global optimization and Optimization methods is also linked to topics like Trigonometric functions and Sine.
In his works, Ali Asghar Heidari conducts interdisciplinary research on Benchmark and Set.
Ali Asghar Heidari combines subjects such as Solver, Metaheuristic and Crossover with his study of Swarm behaviour.
His study explores the link between Artificial intelligence and topics such as Machine learning that cross with problems in Metaheuristic algorithms.
Between 2020 and 2021, his most popular works were:
- Evolutionary biogeography-based whale optimization methods with communication structure: Towards measuring the balance (23 citations)
- Evolutionary biogeography-based whale optimization methods with communication structure: Towards measuring the balance (23 citations)
- Chaotic random spare ant colony optimization for multi-threshold image segmentation of 2D Kapur entropy (23 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Algorithm
His scientific interests lie mostly in Local optimum, Benchmark, Set, Mathematical optimization and Swarm behaviour.
Local optimum is a subfield of Algorithm that Ali Asghar Heidari studies.
Benchmark is often connected to Rate of convergence in his work.
His work carried out in the field of Rate of convergence brings together such families of science as State and Data mining.
In general Mathematical optimization study, his work on Local search and Metaheuristic often relates to the realm of Dimension and Medical diagnosis, thereby connecting several areas of interest.
Dimension combines with fields such as Cuckoo search, Optimization problem, Engineering optimization and Scalability in his research.
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