What is he best known for?
The fields of study he is best known for:
- Mathematical optimization
- Algorithm
- Artificial intelligence
His primary areas of investigation include Mathematical optimization, Variable neighborhood search, Metaheuristic, Heuristics and Local search.
His Mathematical optimization study frequently links to related topics such as Variable.
Variable neighborhood search is a subfield of Algorithm that he studies.
His Metaheuristic study incorporates themes from Global optimization, Search algorithm, Class, Travelling salesman problem and Operations research.
Nenad Mladenović usually deals with Heuristics and limits it to topics linked to Heuristic and Bibliography.
His biological study spans a wide range of topics, including Best-first search and Branch and bound.
His most cited work include:
- Variable neighborhood search (2786 citations)
- Variable neighborhood search: Principles and applications (1476 citations)
- Variable neighbourhood search: methods and applications (583 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of study are Mathematical optimization, Variable neighborhood search, Metaheuristic, Heuristic and Heuristics.
His Mathematical optimization study integrates concerns from other disciplines, such as Variable and Algorithm.
His Variable research is multidisciplinary, incorporating perspectives in Descent and Benchmark.
Explained sum of squares and Combinatorics is closely connected to Cluster analysis in his research, which is encompassed under the umbrella topic of Variable neighborhood search.
His Metaheuristic research is multidisciplinary, relying on both Global optimization and Operations research.
His research in Heuristic intersects with topics in Genetic algorithm and Solver.
He most often published in these fields:
- Mathematical optimization (74.39%)
- Variable neighborhood search (61.99%)
- Metaheuristic (36.39%)
What were the highlights of his more recent work (between 2015-2021)?
- Variable neighborhood search (61.99%)
- Mathematical optimization (74.39%)
- Metaheuristic (36.39%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Variable neighborhood search, Mathematical optimization, Metaheuristic, Heuristics and Variable.
His work deals with themes such as Computational intelligence, Set, Cluster analysis, Heuristic and Optimization problem, which intersect with Variable neighborhood search.
Nenad Mladenović combines subjects such as Process and Benchmark with his study of Mathematical optimization.
His work in Metaheuristic covers topics such as Job shop scheduling which are related to areas like Optimal decision and Robustness.
His studies in Heuristics integrate themes in fields like Simple, Graph, Time horizon and Economic dispatch.
His research on Variable also deals with topics like
- Automatic programming which is related to area like Tree,
- Theoretical computer science which connect with Combinatorial optimization.
Between 2015 and 2021, his most popular works were:
- Variable neighborhood search: basics and variants (114 citations)
- Variable Neighborhood Search. (72 citations)
- Less is more: Basic variable neighborhood search for minimum differential dispersion problem (46 citations)
In his most recent research, the most cited papers focused on:
- Mathematical optimization
- Artificial intelligence
- Algorithm
The scientist’s investigation covers issues in Mathematical optimization, Variable neighborhood search, Heuristics, Local search and Metaheuristic.
His Mathematical optimization research is multidisciplinary, incorporating elements of Theory of computation, Computational intelligence and Cluster analysis.
The various areas that Nenad Mladenović examines in his Variable neighborhood search study include Variable, Preventive maintenance, Heuristic, Benchmark and Combinatorial optimization.
His Heuristics research includes elements of Graph, Solver, Set and Electric power system.
His research investigates the connection between Local search and topics such as GRASP that intersect with problems in Effective method, Parallel metaheuristic and Tabu search.
His research integrates issues of Power system simulation, Iterative method, Time horizon, Optimization problem and Economic dispatch in his study of Metaheuristic.
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