Modeling, Analysis, and Applications in Metaheuristic Computing: Advancements and Trends

Modeling, Analysis, and Applications in Metaheuristic Computing: Advancements and Trends

Release Date: March, 2012|Copyright: © 2012 |Pages: 445
DOI: 10.4018/978-1-4666-0270-0
ISBN13: 9781466602700|ISBN10: 1466602708|EISBN13: 9781466602717
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Description & Coverage
Description:

The engineering and business problems the world faces today have become more impenetrable and unstructured, making the design of a satisfactory problem-specific algorithm nontrivial.

Modeling, Analysis, and Applications in Metaheuristic Computing: Advancements and Trends is a collection of the latest developments, models, and applications within the transdisciplinary fields related to metaheuristic computing. Providing researchers, practitioners, and academicians with insight into a wide range of topics such as genetic algorithms, differential evolution, and ant colony optimization, this book compiles the latest findings, analysis, improvements, and applications of technologies within metaheuristic computing.

Coverage:

The many academic areas covered in this publication include, but are not limited to:

  • Ant colony optimization
  • Applications of metaheuristic computing
  • Foraging algorithm
  • Genetic Algorithm
  • Hyper-heuristics
  • Local Search
  • Memetic algorithm
  • Particle Swarm Optimization
  • Strategic oscillation
  • Variable neighborhood search
Reviews & Statements

Many real-world problems are unstructured or semi-structured, making the design of a satisfactory algorithm nontrivial. Metaheuristic algorithms have emerged as viable solution methods for solving the problems to which the traditional mathematical programming and heuristics are ill-suited. The metaheuristic approach guides the course of a heuristics to search beyond the local optimality by taking full advantage of nature metaphores or problem-solving strategies.

– Peng-Yeng Yin, National Chi Nan University, Taiwan
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Editor/Author Biographies
Peng-Yeng Yin received his B.S., M.S. and Ph.D. degrees in Computer Science from National Chiao Tung University, Hsinchu, Taiwan. From 1993 to 1994, he was a visiting scholar at the Department of Electrical Engineering, University of Maryland, College Park, and the Department of Radiology, Georgetown University, Washington D.C. In 2000, he was a visiting Professor in the Visualization and Intelligent Systems Laboratory (VISLab) at the Department of Electrical Engineering, University of California, Riverside (UCR). From 2006 to 2007, he was a visiting Professor at Leeds School of Business, University of Colorado. And in 2015, he was a visiting Professor at Graduate School of Engineering, Osaka University, Japan. He is currently a Distinguished Professor of the Department of Information Management, National Chi Nan University, Taiwan, and he was the department head during 2004 and 2006, and the Dean of the office of R&D for the university from 2008 to 2012. Dr. Yin received the Overseas Research Fellowship from Ministry of Education in 1993, Overseas Research Fellowships from National Science Council in 2000 and 2015. He is a member of the Phi Tau Phi Scholastic Honor Society and listed in Who’s Who in the World, Who’s Who in Science and Engineering, and Who’s Who in Asia. Dr. Yin has published more than 140 academic articles in reputable journals and conferences including European Journal of Operational Research, Decision Support Systems, Annals of Operations Research, IEEE Trans. on Pattern Analysis and Machine Intelligence, IEEE Trans. on Knowledge and Data Engineering, IEEE Trans. on Education, etc. He is the Editor-in-Chief of the International Journal of Applied Metaheuristic Computing and has been on the Editorial Board of Journal of Computer Information Systems, Applied Mathematics & Information Sciences, Mathematical Problems in Engineering, International Journal of Advanced Robotic Systems, and served as a program committee member in many international conferences. He has also edited four books in pattern recognition and metaheuristic computing. His current research interests include artificial intelligence, evolutionary computation, educational informatics, metaheuristics, pattern recognition, image processing, machine learning, software engineering, computational intelligence, and operations research.
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