What is he best known for?
The fields of study he is best known for:
- Mathematical optimization
- Artificial intelligence
- Algorithm
His primary scientific interests are in Mathematical optimization, Evolutionary algorithm, Optimization problem, Multi-objective optimization and Genetic algorithm.
His Evolutionary computation, Multi-swarm optimization, Constrained optimization, Metaheuristic and Meta-optimization investigations are all subjects of Mathematical optimization research.
His Evolutionary algorithm research incorporates elements of Rate of convergence and Optimal design.
He regularly links together related areas like Point in his Optimization problem studies.
The Multi-objective optimization study combines topics in areas such as Pareto principle and Population-based incremental learning.
His Genetic algorithm research focuses on subjects like Stochastic optimization, which are linked to Computational intelligence.
His most cited work include:
- Society and civilization: An optimization algorithm based on the simulation of social behavior (360 citations)
- A Swarm Metaphor for Multiobjective Design Optimization (245 citations)
- A Decomposition-Based Evolutionary Algorithm for Many Objective Optimization (211 citations)
What are the main themes of his work throughout his whole career to date?
Tapabrata Ray mainly focuses on Mathematical optimization, Evolutionary algorithm, Optimization problem, Multi-objective optimization and Evolutionary computation.
His study in Constrained optimization, Metaheuristic, Meta-optimization, Local search and Memetic algorithm is carried out as part of his Mathematical optimization studies.
Swarm behaviour is closely connected to Multi-swarm optimization in his research, which is encompassed under the umbrella topic of Meta-optimization.
His Evolutionary algorithm research integrates issues from Genetic algorithm, Algorithm, Differential evolution, Rate of convergence and Engineering design process.
His Genetic algorithm study frequently links to related topics such as Sorting.
His Optimization problem study combines topics in areas such as Machine learning, Kriging, Pareto principle and Artificial intelligence.
He most often published in these fields:
- Mathematical optimization (66.91%)
- Evolutionary algorithm (44.24%)
- Optimization problem (34.20%)
What were the highlights of his more recent work (between 2016-2021)?
- Mathematical optimization (66.91%)
- Evolutionary algorithm (44.24%)
- Optimization problem (34.20%)
In recent papers he was focusing on the following fields of study:
Tapabrata Ray mostly deals with Mathematical optimization, Evolutionary algorithm, Optimization problem, Multi-objective optimization and Operations research.
His biological study spans a wide range of topics, including Function and Range.
His Evolutionary algorithm study incorporates themes from Quality, Computational fluid dynamics, Local search, Differential evolution and Ranking.
His studies in Optimization problem integrate themes in fields like Evolutionary computation, Global optimization and Kriging.
His Multi-objective optimization study integrates concerns from other disciplines, such as Approximation algorithm and Engineering optimization.
His Operations research research is multidisciplinary, incorporating elements of Genetic programming and Maximization.
Between 2016 and 2021, his most popular works were:
- Consolidated optimization algorithm for resource-constrained project scheduling problems (35 citations)
- Bridging the Gap: Many-Objective Optimization and Informed Decision-Making (31 citations)
- An Enhanced Decomposition-Based Evolutionary Algorithm With Adaptive Reference Vectors (30 citations)
In his most recent research, the most cited papers focused on:
- Mathematical optimization
- Artificial intelligence
- Algorithm
Tapabrata Ray spends much of his time researching Mathematical optimization, Evolutionary algorithm, Optimization problem, Evolutionary computation and Multi-objective optimization.
He has researched Mathematical optimization in several fields, including Bridging and Computational fluid dynamics.
His Evolutionary algorithm research is multidisciplinary, incorporating perspectives in Quality, Optimization algorithm and Differential evolution.
His Optimization problem study introduces a deeper knowledge of Algorithm.
Tapabrata Ray interconnects Pareto principle, Metaheuristic, Imperialist competitive algorithm and Bilevel optimization in the investigation of issues within Evolutionary computation.
His research integrates issues of Algorithm design, Curvature, Approximation algorithm and Selection in his study of Multi-objective optimization.
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