H-Index & Metrics Best Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science D-index 30 Citations 3,768 171 World Ranking 8876 National Ranking 2

Overview

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.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Best Publications

Modified cuckoo search algorithm for unconstrained optimization problems

Milan Tuba;Milos Subotic;Nadezda Stanarevic.
ECC'11 Proceedings of the 5th European conference on European computing conference (2011)

166 Citations

An upgraded artificial bee colony (ABC) algorithm for constrained optimization problems

Ivona Brajevic;Milan Tuba.
Journal of Intelligent Manufacturing (2013)

155 Citations

Improved bat algorithm applied to multilevel image thresholding.

Adis Alihodzic;Milan Tuba.
The Scientific World Journal (2014)

140 Citations

An ant colony optimization algorithm with improved pheromone correction strategy for the minimum weight vertex cover problem

Raka Jovanovic;Milan Tuba.
soft computing (2011)

131 Citations

Artificial Bee Colony (ABC) Algorithm for Constrained Optimization Improved with Genetic Operators

Nebojsa Bacanin;Milan Tuba.
Studies in Informatics and Control (2012)

129 Citations

Ant colony optimization algorithm with pheromone correction strategy for the minimum connected dominating set problem

Raka Jovanovic;Milan Tuba.
Computer Science and Information Systems (2013)

112 Citations

Firefly algorithm for cardinality constrained mean-variance portfolio optimization problem with entropy diversity constraint.

Nebojsa Bacanin;Milan Tuba.
The Scientific World Journal (2014)

111 Citations

Cuckoo Search and Firefly Algorithm Applied to Multilevel Image Thresholding

Ivona Brajevic;Milan Tuba.
(2014)

98 Citations

Improved seeker optimization algorithm hybridized with firefly algorithm for constrained optimization problems

Milan Tuba;Nebojsa Bacanin.
Neurocomputing (2014)

87 Citations

Adjusted Fireworks Algorithm Applied to Retinal Image Registration

Eva Tuba;Milan Tuba;Edin Dolicanin.
Studies in Informatics and Control (2017)

81 Citations

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