Meta-Heuristics Optimization Algorithms in Engineering, Business, Economics, and Finance
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Meta-Heuristics Optimization Algorithms in Engineering, Business, Economics, and Finance

Pandian M. Vasant (Petronas University of Technology, Malaysia)
Indexed In: SCOPUS View 2 More Indices
Release Date: September, 2012|Copyright: © 2013 |Pages: 734
DOI: 10.4018/978-1-4666-2086-5
ISBN13: 9781466620865|ISBN10: 1466620862|EISBN13: 9781466620872
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Description & Coverage
Description:

Optimization techniques have developed into a significant area concerning industrial, economics, business, and financial systems. With the development of engineering and financial systems, modern optimization has played an important role in service-centered operations and as such has attracted more attention to this field. Meta-heuristic hybrid optimization is a newly development mathematical framework based optimization technique. Designed by logicians, engineers, analysts, and many more, this technique aims to study the complexity of algorithms and problems.

Meta-Heuristics Optimization Algorithms in Engineering, Business, Economics, and Finance explores the emerging study of meta-heuristics optimization algorithms and methods and their role in innovated real world practical applications. This book is a collection of research on the areas of meta-heuristics optimization algorithms in engineering, business, economics, and finance and aims to be a comprehensive reference for decision makers, managers, engineers, researchers, scientists, financiers, and economists as well as industrialists.

Coverage:

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

  • Application of MHHO in Engineering
  • Application of MHHO in Finance, Economics and Business
  • Artificial immune systems
  • Bee Optimization Algorithms
  • Cultural algorithms
  • Differential Evolution
  • Evolutionary Programming (EP)
  • Gravitational Search Algorithm (GSA)
  • Harmony Search
  • Multi-Modal Optimization
  • Multi-Objective Heuristic Based Optimization (MOP)
  • Particle Swarm Optimization
Reviews and Testimonials

The meta-heuristic hybrid optimization techniques discussed in this collection address the intrinsic difficulty of algorithms and the need to model uncertainty problems troubling modern industrial, business, and financial systems. Mexican and Iranian contributors describe algorithms for inventory and supply chain management problems, compare Lagrangian relaxations of a two stage facility location problem, and propose an ANN self-tuning frequency control design for an isolated microgrid. Other topics of the 20 papers include particle swarm for optimal power flow, the relationship between stock returns and earnings, robot motion control, generator maintenance scheduling, and weighted affinity measure clustering for online data mining.

– Book News Inc. Portland, OR

The book contains 20 outstanding chapters concentrating on research and development of new and improved hybrid meta-heuristic optimization algorithms. Apparently, the chapters therein represent the state-of-the-art and recent developments in metaheuristic applications. Therefore, the book forms a platform for knowledge sharing, established by different researchers, academicians, and decision analysts from various backgrounds. The content of the research work demonstrates unique potentials and strengths. [...] Highly recommended for researchers, scientists, consultants, industrialists, decision makers, managers, engineers, financiers, and economists.

– Dr. Michael Mutingi, Namibia University of Science and Technology

I strongly recommend this book. It shows applicability and usability of meta-heuristics optimization algorithms and methods on everyday problems, along strong theoretical background. It consolidates theory and practice, where meta-heuristics optimization algorithms play a key role in solving real problems in the domains of Engineering, Business, Economics, and Finance.

– Dr. Goran Klepac, Raiffeisenbank Austria Zagreb, Croatia

Provides a complex and many-sided look at meta-heuristics techniques for different applications, which is very useful for understanding the progress in the described area. Highly Recommended.

– Professor Nikolai Voropai, Director of the Melentiev Energy Systems Institute, Irkutsk, Russia
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