What is he best known for?
The fields of study Shahryar Rahnamayan is best known for:
- Particle swarm optimization
- Metaheuristic
- Mathematical optimization
In his study, Shahryar Rahnamayan carries out multidisciplinary Mathematical optimization and Global optimization research.
By researching both Global optimization and Metaheuristic, he produces research that crosses academic boundaries.
Shahryar Rahnamayan combines Metaheuristic and Multi-swarm optimization in his research.
His Multi-swarm optimization study frequently draws connections to other fields, such as Mathematical optimization.
His work on Artificial intelligence is being expanded to include thematically relevant topics such as Artificial bee colony algorithm.
His Artificial intelligence research extends to Artificial bee colony algorithm, which is thematically connected.
His Algorithm study often links to related topics such as Firefly algorithm.
His Firefly algorithm study frequently draws connections to other fields, such as Algorithm.
He undertakes multidisciplinary studies into Opposition (politics) and Politics in his work.
His most cited work include:
- Opposition-Based Differential Evolution (1283 citations)
- Enhancing particle swarm optimization using generalized opposition-based learning (350 citations)
- Diversity enhanced particle swarm optimization with neighborhood search (331 citations)
What are the main themes of his work throughout his whole career to date
His multidisciplinary approach integrates Artificial intelligence and Computer vision in his work.
Computer vision and Artificial intelligence are two areas of study in which he engages in interdisciplinary research.
He brings together Mathematical optimization and Metaheuristic to produce work in his papers.
His Algorithm study frequently links to adjacent areas such as Particle swarm optimization.
While working in this field, Shahryar Rahnamayan studies both Politics and Opposition (politics).
His study brings together the fields of Law and Opposition (politics).
Shahryar Rahnamayan performs integrative study on Law and Politics in his works.
His studies link Benchmark (surveying) with Geodesy.
His Benchmark (surveying) study frequently draws parallels with other fields, such as Geodesy.
Shahryar Rahnamayan most often published in these fields:
- Artificial intelligence (62.50%)
- Mathematical optimization (55.36%)
- Algorithm (50.00%)
What were the highlights of his more recent work (between 2019-2022)?
- Feature selection (60.00%)
- Pattern recognition (psychology) (60.00%)
- Artificial intelligence (60.00%)
In recent works Shahryar Rahnamayan was focusing on the following fields of study:
While the research belongs to areas of Pareto principle, Shahryar Rahnamayan spends his time largely on the problem of Mathematical optimization, intersecting his research to questions surrounding Multi-objective optimization.
By researching both Multi-objective optimization and Mathematical optimization, he produces research that crosses academic boundaries.
In most of his Algorithm studies, his work intersects topics such as Optimization problem and Sorting.
Shahryar Rahnamayan brings together Feature selection and Data mining to produce work in his papers.
In his works, he conducts interdisciplinary research on Data mining and Algorithm.
With his scientific publications, his incorporates both Artificial intelligence and Speech recognition.
Shahryar Rahnamayan performs multidisciplinary study on Speech recognition and Artificial intelligence in his works.
As part of his studies on Machine learning, he often connects relevant areas like Selection (genetic algorithm).
His research links Machine learning with Selection (genetic algorithm).
Between 2019 and 2022, his most popular works were:
- Analysis, modeling, and multi-objective optimization of machining Inconel 718 with nano-additives based minimum quantity coolant (20 citations)
- Reference-point-based multi-objective optimization algorithm with opposition-based voting scheme for multi-label feature selection (19 citations)
- Multi-objective optimization and innovization-based knowledge discovery of sustainable machining process (7 citations)
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