What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Algorithm
- Operating system
Milan Tuba spends much of his time researching Metaheuristic, Mathematical optimization, Swarm intelligence, Algorithm and Optimization problem.
Metaheuristic is the subject of his research, which falls under Artificial intelligence.
He studied Artificial intelligence and Pattern recognition that intersect with Brute-force search and Differential evolution.
Much of his study explores Mathematical optimization relationship to Deterministic algorithm.
His Swarm intelligence study combines topics in areas such as Multi-swarm optimization and Artificial bee colony algorithm.
His Algorithm research incorporates themes from Hyperparameter optimization, Kernel method, Structured support vector machine and Support vector machine.
His most cited work include:
- Improved bat algorithm applied to multilevel image thresholding. (97 citations)
- Modified cuckoo search algorithm for unconstrained optimization problems (91 citations)
- An upgraded artificial bee colony (ABC) algorithm for constrained optimization problems (89 citations)
What are the main themes of his work throughout his whole career to date?
Swarm intelligence, Metaheuristic, Mathematical optimization, Artificial intelligence and Algorithm are his primary areas of study.
His Swarm intelligence research is multidisciplinary, incorporating elements of Wireless sensor network, Optimization problem, Artificial bee colony algorithm and Benchmark.
His Metaheuristic research includes elements of Firefly algorithm, Ant colony optimization algorithms, Constrained optimization, Multi-swarm optimization and Robustness.
The Meta-optimization, Heuristic and Optimization algorithm research he does as part of his general Mathematical optimization study is frequently linked to other disciplines of science, such as Herding, therefore creating a link between diverse domains of science.
His study in Artificial intelligence is interdisciplinary in nature, drawing from both Machine learning, Computer vision and Pattern recognition.
His work on Quantization as part of general Algorithm study is frequently connected to Estimator, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them.
He most often published in these fields:
- Swarm intelligence (41.04%)
- Metaheuristic (40.57%)
- Mathematical optimization (32.08%)
What were the highlights of his more recent work (between 2017-2021)?
- Swarm intelligence (41.04%)
- Metaheuristic (40.57%)
- Artificial intelligence (31.60%)
In recent papers he was focusing on the following fields of study:
Milan Tuba mainly focuses on Swarm intelligence, Metaheuristic, Artificial intelligence, Optimization problem and Particle swarm optimization.
His Swarm intelligence study incorporates themes from MNIST database, Wireless sensor network, Cluster analysis and Benchmark.
Mathematical optimization and Algorithm are the main areas of his Metaheuristic studies.
Milan Tuba has included themes like Machine learning and Pattern recognition in his Artificial intelligence study.
His research integrates issues of Multi-objective optimization, Heuristics, Stochastic optimization and Motion planning in his study of Optimization problem.
The various areas that Milan Tuba examines in his Particle swarm optimization study include Computer engineering and Search algorithm.
Between 2017 and 2021, his most popular works were:
- Performance of Elephant Herding Optimization and Tree Growth Algorithm Adapted for Node Localization in Wireless Sensor Networks. (35 citations)
- Unmanned Combat Aerial Vehicle Path Planning by Brain Storm Optimization Algorithm (31 citations)
- An efficient ant colony optimization algorithm for the blocks relocation problem (27 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Algorithm
- Machine learning
Milan Tuba mainly investigates Swarm intelligence, Metaheuristic, Optimization problem, Artificial intelligence and Benchmark.
The concepts of his Swarm intelligence study are interwoven with issues in Wireless sensor network, Firefly algorithm and Robustness.
Metaheuristic is a subfield of Algorithm that Milan Tuba tackles.
His work in the fields of Algorithm, such as Ant colony optimization algorithms, overlaps with other areas such as Estimator and Probability density function.
His studies deal with areas such as Real-time computing and Motion planning as well as Optimization problem.
His research investigates the connection with Benchmark and areas like Mathematical optimization which intersect with concerns in Chaotic.
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