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Multiobjective Heuristic Search

An Introduction to intelligent Search Methods for Multicriteria Optimization

  • Book
  • © 1999

Overview

Authors:
  1. Pallab Dasgupta
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  2. P. P. Chakrabarti
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  3. S. C. DeSarkar
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  • This is the first book on multiobjective heuristic search.

Part of the book series: Computational Intelligence (CI)

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Table of contents (7 chapters)

  1. Front Matter

    Pages i-ix
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  2. Introduction

    • Pallab Dasgupta, P. P. Chakrabarti, S. C. DeSarkar
    Pages 1-7

  3. The Multiobjective Search Model

    • Pallab Dasgupta, P. P. Chakrabarti, S. C. DeSarkar
    Pages 9-18

  4. Multiobjective State Space Search

    • Pallab Dasgupta, P. P. Chakrabarti, S. C. DeSarkar
    Pages 19-48

  5. Applications of Multiobjective Search

    • Pallab Dasgupta, P. P. Chakrabarti, S. C. DeSarkar
    Pages 49-73

  6. Multiobjective Problem Reduction Search

    • Pallab Dasgupta, P. P. Chakrabarti, S. C. DeSarkar
    Pages 75-96

  7. Multiobjective Game Tree Search

    • Pallab Dasgupta, P. P. Chakrabarti, S. C. DeSarkar
    Pages 97-118

  8. Conclusion

    • Pallab Dasgupta, P. P. Chakrabarti, S. C. DeSarkar
    Pages 119-123

  9. Back Matter

    Pages 125-134
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Keywords

About this book

A large number of problems require the optimization of multiple criteria. These crite­ ria are often non-commensurate and sometimes conflicting in nature making the task of optimization more difficult. In such problems, the task of creating a combined opti­ mization function is often not easy. Moreover, the decision procedure can be affected by the sensitivity of the solution space, and the trade-off is often non-linear. In real life we traditionally handle such problems by suggesting not one, but several non-dominated solutions. Finding a set of non-dominated solutions is also useful in multistaged opti­ mization problems, where the solution of one stage of optimization is passed on to the next stage. One classic example is that of circuit design, where high-level synthesis, logic synthesis and layout synthesis comprise important stages of optimization of the circuit. Passing a set of non-dominated partial solutions from one stage to the next typically ensures better global optimization. This book presents a new approach to multi-criteria optimization based on heuristic search techniques. Classical multicriteria optimization techniques rely on single criteria optimization algorithms, and hence we are either required to optimize one criterion at a time (under constraints on the others), or we are asked for a single scalar combined optimization function. On the other hand, the multiobjective search approach maps each optimization criterion onto a distinct dimension of a vector valued cost structure.

About the authors

Assistant Professor Dr. Pallab Dasgupta, Associate Professor Dr. P.P. Chakrabarti and Professor Dr. S. C. DeSarkar are at the Department of Computer Science & Engineering at the Indian Institute of Technology Kharagpur, INDIA 721302

Bibliographic Information

  • Book Title: Multiobjective Heuristic Search

  • Book Subtitle: An Introduction to intelligent Search Methods for Multicriteria Optimization

  • Authors: Pallab Dasgupta, P. P. Chakrabarti, S. C. DeSarkar

  • Series Title: Computational Intelligence

  • DOI: https://doi.org/10.1007/978-3-322-86853-4

  • Publisher: Vieweg+Teubner Verlag Wiesbaden

  • eBook Packages: Springer Book Archive

  • Copyright Information: Friedr. Vieweg & Sohn Verlagsgesellschaft mbH, Braunschweig/Wiesbaden 1999

  • Softcover ISBN: 978-3-528-05708-4Published: 29 June 1999

  • eBook ISBN: 978-3-322-86853-4Published: 11 November 2013

  • Series ISSN: 2522-0519

  • Series E-ISSN: 2522-0527

  • Edition Number: 1

  • Number of Pages: X, 134

  • Topics: Computer Science, general

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