Multiobjective Heuristic Search: An Introduction to intelligent Search Methods for Multicriteria Optimization (Computational Intelligence)

Dasgupta, Pallab; Chakrabarti, P. P.; DeSarkar, S. C.

ISBN 10: 3528057084 / ISBN 13: 9783528057084

Edité par Vieweg+Teubner Verlag, 1999

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N° de réf. du vendeur ria9783528057084_new

Détails bibliographiques

• Titre: Multiobjective Heuristic Search: An Introduction to intelligent Search Methods for Multicriteria Optimization (Computational Intelligence)
• Éditeur: Vieweg+Teubner Verlag
• Date de parution: 1999
• Langue: anglais
• ISBN à 10 chiffres: 3528057084
• ISBN à 13 chiffres: 9783528057084
• Reliure: Couverture souple
• État de l'article: New

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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.

À propos de l?auteur

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