What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Algorithm
Peng-Yeng Yin spends much of his time researching Algorithm, Mathematical optimization, Particle swarm optimization, Genetic algorithm and Balanced histogram thresholding.
His studies in Algorithm integrate themes in fields like Discrete mathematics and Combinatorics.
His Mathematical optimization research integrates issues from Graph and Distributed computing.
His studies deal with areas such as Entropy, Image and Optimal allocation as well as Particle swarm optimization.
His study looks at the relationship between Genetic algorithm and topics such as Multi-swarm optimization, which overlap with Linear bottleneck assignment problem, Weapon target assignment problem, Generalized assignment problem, Assignment problem and Metaheuristic.
His Balanced histogram thresholding research is multidisciplinary, incorporating perspectives in Convergence, Segmentation, Image segmentation and Thresholding.
His most cited work include:
- Multilevel minimum cross entropy threshold selection based on particle swarm optimization (188 citations)
- A fast scheme for optimal thresholding using genetic algorithms (155 citations)
- A Heuristic Algorithm for planning personalized learning paths for context-aware ubiquitous learning (149 citations)
What are the main themes of his work throughout his whole career to date?
His primary scientific interests are in Mathematical optimization, Artificial intelligence, Algorithm, Particle swarm optimization and Genetic algorithm.
Many of his research projects under Mathematical optimization are closely connected to Nonlinear system and Resource constraints with Nonlinear system and Resource constraints, tying the diverse disciplines of science together.
He combines subjects such as Machine learning, Data mining, Computer vision and Pattern recognition with his study of Artificial intelligence.
His work deals with themes such as Balanced histogram thresholding, Image segmentation, Thresholding and Image processing, which intersect with Algorithm.
His work in the fields of Particle swarm optimization, such as Multi-swarm optimization, overlaps with other areas such as Expediting.
The study incorporates disciplines such as Teaching method, Resource allocation and Heuristic in addition to Genetic algorithm.
He most often published in these fields:
- Mathematical optimization (37.37%)
- Artificial intelligence (31.31%)
- Algorithm (28.28%)
What were the highlights of his more recent work (between 2014-2021)?
- Artificial intelligence (31.31%)
- Mathematical optimization (37.37%)
- Wind power (6.06%)
In recent papers he was focusing on the following fields of study:
The scientist’s investigation covers issues in Artificial intelligence, Mathematical optimization, Wind power, Machine learning and Operations research.
The Artificial intelligence study combines topics in areas such as Particle swarm optimization, Firefly algorithm and Global optimization.
His biological study spans a wide range of topics, including Ensemble learning and Data mining.
His biological study focuses on Optimisation algorithm.
Peng-Yeng Yin interconnects Synchronous learning, Proactive learning and Tabu search in the investigation of issues within Machine learning.
His Algorithm research is multidisciplinary, relying on both Heuristics and Heuristic.
Between 2014 and 2021, his most popular works were:
- Adaptive memory artificial bee colony algorithm for green vehicle routing with cross-docking (37 citations)
- Risk management of wind farm micro-siting using an enhanced genetic algorithm with simulation optimization (29 citations)
- Cooperative coevolutionary approach for integrated vehicle routing and scheduling using cross-dock buffering (19 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Algorithm
Peng-Yeng Yin mostly deals with Operations research, Algorithm, Metaheuristic, Business value and Risk management.
His research integrates issues of Tabu search and Mathematical optimization in his study of Operations research.
His Algorithm study often links to related topics such as Heuristic.
His work carried out in the field of Metaheuristic brings together such families of science as Decision problem and Heuristics.
His Business value studies intersect with other disciplines such as Production, Swarm behaviour, Robustness and Flexibility.
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