World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
37
Citations
4392
World Ranking
10930
National Ranking
99

Overview

Peng-Yeng Yin is affiliated with National Chi Nan University in Taiwan and conducts research primarily in Environmental Science, Computer Science, and Engineering. Their scholarly output includes notable contributions to Environmental Engineering, Artificial Intelligence, Health, Toxicology and Mutagenesis, Industrial and Manufacturing Engineering, and Global and Planetary Change.

The primary topics addressed in Yin's work are focused on Air Quality Monitoring and Forecasting, Air Quality and Health Impacts, Atmospheric Chemistry and Aerosols, Metaheuristic Optimization Algorithms, the COVID-19 impact on air quality, Scheduling and Optimization Algorithms, and Evolutionary Algorithms and Applications.

Some of the recent publications authored by Peng-Yeng Yin include:

  • Risk-aware optimal planning for a hybrid wind-solar farm, 2020, Renewable Energy
  • Improving PM2.5 Concentration Forecast with the Identification of Temperature Inversion, 2021, Applied Sciences
  • Minimizing the Makespan in Flowshop Scheduling for Sustainable Rubber Circular Manufacturing, 2021, Sustainability
  • A Machine Learning-Based Ensemble Framework for Forecasting PM2.5 Concentrations in Puli, Taiwan, 2022, Applied Sciences

Yin's research frequently appears in journals such as Applied Sciences, Sustainability, Renewable Energy, Symmetry, and Mathematical Biosciences & Engineering.

Collaborations include frequent co-authorship with Rong-Fuh Day, Ray-I Chang, Hsin-Min Chen, Ying-Chieh Wei, and Chun-Ying Cheng. These partnerships have contributed to the multidisciplinary scope of Yin's work.

Best Publications

  • Multilevel minimum cross entropy threshold selection based on particle swarm optimization

    Peng-Yeng Yin

  • A Heuristic Algorithm for planning personalized learning paths for context-aware ubiquitous learning

    Gwo-Jen Hwang;Fan-Ray Kuo;Peng-Yeng Yin;Kuo-Hsien Chuang

  • A fast scheme for optimal thresholding using genetic algorithms

    Peng-Yeng Yin

  • A discrete particle swarm algorithm for optimal polygonal approximation of digital curves

    Peng-Yeng Yin

  • A hybrid particle swarm optimization algorithm for optimal task assignment in distributed systems

    Peng-Yeng Yin;Shiuh-Sheng Yu;Pei-Pei Wang;Yi-Te Wang

  • Application of ant colony optimization for no-wait flowshop scheduling problem to minimize the total completion time

    S. J. Shyu;B. M. T. Lin;P. Y. Yin

  • Integrating relevance feedback techniques for image retrieval using reinforcement learning

    Peng-Yeng Yin;B. Bhanu;Kuang-Cheng Chang;Anlei Dong

  • Ant colony search algorithms for optimal polygonal approximation of plane curves

    Peng-Yeng Yin

  • An ant colony optimization algorithm for the minimum weight vertex cover problem

    Shyong Jian Shyu;Peng-Yeng Yin;Bertrand M.T. Lin

  • Task allocation for maximizing reliability of a distributed system using hybrid particle swarm optimization

    Peng-Yeng Yin;Shiuh-Sheng Yu;Pei-Pei Wang;Yi-Te Wang

  • A fast iterative scheme for multilevel thresholding methods

    Peng-Yeng Yin;Ling-Hwei Chen

  • A particle swarm optimization approach to the nonlinear resource allocation problem

    Peng-Yeng Yin;Jing-Yu Wang

  • A new circle/ellipse detector using genetic algorithms

    Peng-Yeng Yin

  • Adaptive memory artificial bee colony algorithm for green vehicle routing with cross-docking

    Peng-Yeng Yin;Ya-Lan Chuang

  • Ant colony optimization for the nonlinear resource allocation problem

    Peng-Yeng Yin;Jing-Yu Wang

  • Maximum entropy-based optimal threshold selection using deterministic reinforcement learning with controlled randomization

    Peng-Yeng Yin

  • Multi-objective task allocation in distributed computing systems by hybrid particle swarm optimization

    Peng-Yeng Yin;Shiuh-Sheng Yu;Pei-Pei Wang;Yi-Te Wang

  • A new method for polygonal approximation using genetic algorithms

    Peng-Yeng Yin

  • Particle swarm optimization for point pattern matching

    Peng-Yeng Yin

  • An Enhanced Genetic Approach to Composing Cooperative Learning Groups for Multiple Grouping Criteria.

    Gwo Jen Hwang;Peng Yeng Yin;Chi Wei Hwang;Chin Chung Tsai

Frequent Co-Authors

Gwo-Jen Hwang
Gwo-Jen Hwang National Taiwan University of Science and Technology
Bir Bhanu
Bir Bhanu University of California, Riverside
Fred Glover
Fred Glover University of Colorado Boulder
Manuel Laguna
Manuel Laguna University of Colorado Boulder
Youcef Djenouri
Youcef Djenouri University of South-Eastern Norway
Yiannis Papadopoulos
Yiannis Papadopoulos University of Hull
Chin Chung Tsai
Chin Chung Tsai National Taiwan Normal University
Mohamed Haouari
Mohamed Haouari Qatar University

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