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

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

Peng-Yeng Yin (National Chi Nan University, Taiwan)
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

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.

Topics Covered

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 and Testimonials

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

Table of Contents and List of Contributors

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Preface

INTRODUCTION

The engineering and business problems we face today have become more impenetrable and unstructured, making the design of a satisfactory algorithm nontrivial. Traditionally, researchers strive to formulate the problems as a mathematical model by relaxing, if necessary, hard objectives and constraints. The exact solution to this formulated version can be obtained through manipulations of mathematical programming such as integer linear programming, branch-and-bound and dynamic programming. However, due to the enumeration nature of these methods, mathematical programming techniques are limited to the applications with small problem size. As an alternative, approximation solutions are targeted by problem-specific heuristics which analyze the properties and structures of the underlying problems and greedily construct a feasible solution. The quality of the generated solution varies significantly upon problem instances, though the greedy heuristics are usually computationally fast. It has been a dilemma for the choice between the mathematical programming techniques and heuristic approaches. A notion of levering the two solution methods has created the regime of metaheuristic which was first coined by Fred Glover (1986). The metaheuristic approach guides the course of a heuristic to search beyond the local optimality by taking full advantage of strategic level problem solving using memory manipulations without a hassle to design problem-specific operations each time a new application shows up and still inheriting the computational efficiency from the embedded heuristic. Metaheuristic has built its foundations on multidiscipilary research findings ranging from phylogenetic evolution, sociocognition, gestalt psychology, social insects foraging, to strategic level problem solving. From a broader perspective, nature-inspired metaheuristics focusing on metaphors share its name with evolutionary algorithms, artificial immune systems, memetic algorithm, simulated annealing, ant colony optimization, particle swarm optimization, etc (Holland, 1975; Kirkpatrick et al., 1983; Dorigo, 1992; Kennedy & Eberhart, 1995). Strategic level metaheuristics incorporate higher level problem solving mechanisms from artificial intelligence relying on rules and memory. Typical renowned methods at least include tabu search, scatter search, GRASP, variable neighbourhood search (Glover, 1989; Laguna & Marti, 2003; Feo & Resende, 1995; Mladenovic & Hansen, 1997).

With technologies and conceptions emerging over the years, the development of metaheuristic has come to a new era. Researchers and practitioners intend to identify the primitive components contained in metaheuristics and try to develop the so-called hybrid metaheuristics towards more effective metaheuristic computing. In light of this, several innovations have been proposed under variable categories such as Matheuristic, Hyper-heuristic, and Cyber-heuristic. These methodologies do not stick to a particular metaheuristic method, instead, an abstract model is defined. A hybrid metaheuristic algorithm can be automatically constructed or evolved using the abstract model. These innovative ideas advance the research of metaheuristic computing into a new generation. Another desired result of the intensive research on fundamentals of metaheuristics is the provision of unified development frameworks for constructing various forms of metaheuristics. These frameworks are easy enough for practitioners to construct a main-stream metaheuristic program, and are also flexible for researchers to create a sophisticated metaheuristic algorithm.

The remainder of this chapter is organized as follows. Section 2 reviews principal metaheuristics according to the classification of nature-inspired computation vs. strategic level problem solving. In Section 3 we disclose the research trend in metaheuristics hybridizations. Section 4 presents the notion for establishing unified development frameworks for metaheuristics. Finally, conclusions are made in Section 5.

Author(s)/Editor(s) Biography

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.

Indices