What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Algorithm
- Machine learning
Shahryar Rahnamayan spends much of his time researching Mathematical optimization, Global optimization, Differential evolution, Optimization problem and Algorithm.
His Mathematical optimization research is multidisciplinary, incorporating perspectives in Rate of convergence and Benchmark.
The study incorporates disciplines such as Control parameters, Ode and Task in addition to Differential evolution.
His research ties Evolutionary computation and Algorithm together.
His study in the field of Evolution strategy also crosses realms of Opposition.
Shahryar Rahnamayan interconnects Artificial neural network, Soft computing and Artificial bee colony algorithm in the investigation of issues within Evolutionary algorithm.
His most cited work include:
- Opposition-Based Differential Evolution (1130 citations)
- Diversity enhanced particle swarm optimization with neighborhood search (277 citations)
- Enhancing particle swarm optimization using generalized opposition-based learning (247 citations)
What are the main themes of his work throughout his whole career to date?
His scientific interests lie mostly in Mathematical optimization, Differential evolution, Benchmark, Artificial intelligence and Optimization problem.
His work in the fields of Mathematical optimization, such as Global optimization, Metaheuristic, Multi-objective optimization and Particle swarm optimization, overlaps with other areas such as Initialization.
His studies deal with areas such as Evolutionary algorithm, Evolutionary computation, Ode and Mutation as well as Differential evolution.
In his study, which falls under the umbrella issue of Benchmark, Local optimum is strongly linked to Premature convergence.
He has included themes like Computer vision and Pattern recognition in his Artificial intelligence study.
Shahryar Rahnamayan studies Optimization problem, focusing on Multi-swarm optimization in particular.
He most often published in these fields:
- Mathematical optimization (42.49%)
- Differential evolution (39.90%)
- Benchmark (31.09%)
What were the highlights of his more recent work (between 2019-2021)?
- Benchmark (31.09%)
- Optimization problem (21.76%)
- Artificial intelligence (31.09%)
In recent papers he was focusing on the following fields of study:
Shahryar Rahnamayan mainly investigates Benchmark, Optimization problem, Artificial intelligence, Multi-objective optimization and Algorithm.
His work deals with themes such as Evolutionary algorithm, Particle swarm optimization, Differential evolution and Feature selection, which intersect with Benchmark.
His study with Optimization problem involves better knowledge in Mathematical optimization.
His study deals with a combination of Mathematical optimization and Scheme.
He combines subjects such as Machine learning, Somatic cell, Cancer gene and Pattern recognition with his study of Artificial intelligence.
Shahryar Rahnamayan studied Algorithm and Mutation that intersect with Face and Parallel coordinates.
Between 2019 and 2021, his most popular works were:
- Many-level Image Thresholding using a Center-Based Differential Evolution Algorithm (6 citations)
- A Novel Center-based Differential Evolution Algorithm (4 citations)
- Reference-point-based multi-objective optimization algorithm with opposition-based voting scheme for multi-label feature selection (3 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Algorithm
Shahryar Rahnamayan mainly focuses on Artificial intelligence, Benchmark, Differential evolution, Feature and Multi-objective optimization.
The Artificial intelligence study combines topics in areas such as Machine learning and Pattern recognition.
His study brings together the fields of Optimization problem and Benchmark.
Shahryar Rahnamayan's looking at Optimization problem as part of his Mathematical optimization and Algorithm and Optimization problem study.
His Differential evolution research is multidisciplinary, relying on both Local optimum, Feedforward neural network, Metaheuristic, Gradient descent and Linear programming.
He has researched Multi-objective optimization in several fields, including Sorting, Genetic algorithm and Optimal control.
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