What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Mathematical optimization
- Algorithm
Patrick Siarry mostly deals with Mathematical optimization, Metaheuristic, Algorithm, Simulated annealing and Particle swarm optimization.
His Mathematical optimization study is mostly concerned with Tabu search, Meta-optimization, Global optimization, Genetic algorithm and Multi-swarm optimization.
His research investigates the link between Meta-optimization and topics such as Continuous optimization that cross with problems in Crossover.
His studies deal with areas such as Multi-objective optimization and Ant colony optimization algorithms as well as Metaheuristic.
As part of one scientific family, Patrick Siarry deals mainly with the area of Algorithm, narrowing it down to issues related to the Resource constrained, and often Numerical analysis and Integer.
In his work, Swarm behaviour and Data point is strongly intertwined with Combinatorial optimization, which is a subfield of Particle swarm optimization.
His most cited work include:
- A survey on optimization metaheuristics (793 citations)
- Multiobjective Optimization: Principles and Case Studies (514 citations)
- Nonlinear inertia weight variation for dynamic adaptation in particle swarm optimization (485 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of investigation include Mathematical optimization, Metaheuristic, Algorithm, Artificial intelligence and Particle swarm optimization.
His study in Mathematical optimization focuses on Multi-swarm optimization, Continuous optimization, Multi-objective optimization, Meta-optimization and Optimization problem.
The study incorporates disciplines such as Evolutionary algorithm, Tabu search and Ant colony optimization algorithms, Ant colony in addition to Metaheuristic.
His work deals with themes such as Genetic algorithm and Speech recognition, which intersect with Algorithm.
Patrick Siarry has included themes like Swarm intelligence, Machine learning, Computer vision and Pattern recognition in his Artificial intelligence study.
His study in Particle swarm optimization is interdisciplinary in nature, drawing from both Swarm behaviour, Electronic engineering and Combinatorial optimization.
He most often published in these fields:
- Mathematical optimization (46.61%)
- Metaheuristic (33.94%)
- Algorithm (28.51%)
What were the highlights of his more recent work (between 2014-2021)?
- Mathematical optimization (46.61%)
- Metaheuristic (33.94%)
- Particle swarm optimization (19.91%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Mathematical optimization, Metaheuristic, Particle swarm optimization, Algorithm and Artificial intelligence.
His research in Mathematical optimization intersects with topics in Optimal design and Benchmark.
His research brings together the fields of Software engineering and Metaheuristic.
His study in Particle swarm optimization focuses on Multi-swarm optimization in particular.
Patrick Siarry combines subjects such as Watermark, Swarm behaviour, Digital image and Digital watermarking with his study of Algorithm.
His studies in Artificial intelligence integrate themes in fields like Computer vision and Pattern recognition.
Between 2014 and 2021, his most popular works were:
- An image watermarking scheme in wavelet domain with optimized compensation of singular value decomposition via artificial bee colony (83 citations)
- Fractional Extreme Value Adaptive Training Method: Fractional Steepest Descent Approach (58 citations)
- A survey on search-based model-driven engineering (27 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Algorithm
- Mathematical optimization
His primary areas of study are Particle swarm optimization, Artificial intelligence, Optimization problem, Mathematical optimization and Metaheuristic.
His Particle swarm optimization research is multidisciplinary, incorporating perspectives in Multi-objective optimization and Image segmentation.
His research in Artificial intelligence focuses on subjects like Computer vision, which are connected to Differential evolution.
His Optimization problem study combines topics from a wide range of disciplines, such as Replicator equation and Benchmark.
His Mathematical optimization research integrates issues from Gradient descent, Fractional calculus and Adaptive learning.
His Metaheuristic research is multidisciplinary, incorporating elements of Global optimum and Software engineering.
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