What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Structural engineering
His primary areas of investigation include Mathematical optimization, Genetic programming, Benchmark, Algorithm and Optimization problem.
His Mathematical optimization research is multidisciplinary, incorporating perspectives in Krill and Robustness.
The Genetic programming study combines topics in areas such as Parametric statistics, Artificial neural network, Gene expression programming, Structural engineering and Nonlinear system.
The various areas that Amir H. Alavi examines in his Benchmark study include Constraint, Global optimization and Constrained optimization.
The concepts of his Optimization problem study are interwoven with issues in Differential evolution and Metaheuristic.
The Derivative-free optimization research Amir H. Alavi does as part of his general Metaheuristic study is frequently linked to other disciplines of science, such as Search-based software engineering, therefore creating a link between diverse domains of science.
His most cited work include:
- Krill herd: A new bio-inspired optimization algorithm (1015 citations)
- Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems (1014 citations)
- Mixed variable structural optimization using Firefly Algorithm (515 citations)
What are the main themes of his work throughout his whole career to date?
Amir H. Alavi spends much of his time researching Structural engineering, Genetic programming, Mathematical optimization, Artificial intelligence and Artificial neural network.
Amir H. Alavi has researched Structural engineering in several fields, including Compressive strength, Gene expression programming and Sensitivity.
His Genetic programming study combines topics from a wide range of disciplines, such as Algorithm, Geotechnical engineering, Parametric statistics and Soft computing.
His Mathematical optimization research integrates issues from Krill and Benchmark.
His Artificial intelligence study incorporates themes from Machine learning and Pattern recognition.
His Optimization problem course of study focuses on Metaheuristic and Engineering optimization.
He most often published in these fields:
- Structural engineering (24.42%)
- Genetic programming (21.66%)
- Mathematical optimization (17.97%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (18.89%)
- Energy harvesting (8.76%)
- Deep learning (4.61%)
In recent papers he was focusing on the following fields of study:
His main research concerns Artificial intelligence, Energy harvesting, Deep learning, Structural engineering and Structural health monitoring.
His studies in Artificial intelligence integrate themes in fields like Swarm intelligence, Machine learning and Pattern recognition.
His Swarm intelligence study incorporates themes from Optimization problem, Metaheuristic algorithms and Selection.
His Genetic programming research extends to Structural engineering, which is thematically connected.
His studies deal with areas such as Continuous optimization and Engineering optimization as well as Metaheuristic.
Continuous optimization is the subject of his research, which falls under Mathematical optimization.
Between 2018 and 2021, his most popular works were:
- A comprehensive review of krill herd algorithm: variants, hybrids and applications (59 citations)
- Behavior of crossover operators in NSGA-III for large-scale optimization problems (57 citations)
- New machine learning-based prediction models for fracture energy of asphalt mixtures (27 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Machine learning
His scientific interests lie mostly in Artificial intelligence, Swarm intelligence, Mathematical optimization, Machine learning and Metaheuristic.
His Hybrid machine and Noise study, which is part of a larger body of work in Artificial intelligence, is frequently linked to Sensing data and Obstacle, bridging the gap between disciplines.
The Mathematical optimization study combines topics in areas such as Sorting and Benchmark.
His Machine learning study combines topics from a wide range of disciplines, such as Robust optimization and Outbreak.
His Metaheuristic study combines topics in areas such as Krill herd algorithm and Engineering optimization.
His research integrates issues of Selection and Crossover in his study of Optimization problem.
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