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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Multiobjective Heuristic Search: An Introduction to Intelligent Search Methods for Multicriteria Optimization

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

Dasgupta, Pallab
Chakrabarti, P. P.
Bibel, Wolfgang

Paperback

Series: Computational Intelligence

General Computers

ISBN10: 3528057084
ISBN13: 9783528057084
Publisher: Vieweg+teubner Verlag
Published: Jun 29 1999
Pages: 134
Weight: 0.54
Height: 0.32 Width: 6.69 Depth: 9.61
Language: English
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.

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