Multiobjective Heuristic Search: An Introduction to intelligent Search Methods for Multicriteria Optimization

Dasgupta, Pallab (Author)/ Chakrabarti, P. P. (Author)/ DeSarkar, S. C. (Author)/ Bibel, Wolfgang (Series edited by)/ Kruse, Rudolf (Series edited by)

ISBN 10: 3528057084 / ISBN 13: 9783528057084

Published by Vieweg+Teubner Verlag, 2004

About this Item

1999 edition. 134 pages. 9.75x6.75x0.50 inches. In Stock. This item is printed on demand.

Seller Inventory # __3528057084

Bibliographic Details

• Title: Multiobjective Heuristic Search: An Introduction to intelligent Search Methods for Multicriteria Optimization
• Publisher: Vieweg+Teubner Verlag
• Publication Date: 2004
• Language: English
• ISBN 10: 3528057084
• ISBN 13: 9783528057084
• Binding: Paperback
• Condition: Brand New
• Weight: 0.57 kilograms

About this title

Synopsis

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 Author

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