Multiobjective Optimization: Interactive and Evolutionary Approaches
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Real-world applications of multiobjective optimization
Open Preview See a Problem? Details if other :. Thanks for telling us about the problem. Return to Book Page. Multiobjective optimization deals with solving problems having not only one, but multiple, often conflicting, criteria.
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Such problems can arise in practically every field of science, engineering and business, and the need for efficient and reliable solution methods is increasing. The task is challenging due to the fact that, instead of a single optimal solution, multiobjec Multiobjective optimization deals with solving problems having not only one, but multiple, often conflicting, criteria. The task is challenging due to the fact that, instead of a single optimal solution, multiobjective optimization results in a number of solutions with different trade-offs among criteria, also known as Pareto optimal or efficient solutions.
Coello CAC Handling preferences in evolutionary multiobjective optimization: a survey. Mach Learn 93 2—3 — CrossRef.
Interactive and Evolutionary Approaches
Deb K, Sinha A, Korhonen P, Wallenius J An interactive evolutionary multiobjective optimization method based on progressively approximated value functions. Fonseca C, Fleming P Genetic algorithms for multiobjective optimization: formulation, discussion, and generalization. In: Proceedings of the fifth international conference on genetic algorithms, pp — In: Foundations of genetic algorithms. Morgan Kaufmann, Los Altos, pp — Group Decis Negot 22 3 — CrossRef. Comput Oper Res —70 CrossRef.
Outlooks and insights on group decision and negotiation, volume of the series lecture notes in business information processing, pp — Kenney R, Raiffa H Decisions with multiple objectives: preferences and value tradeoffs. Wiley, New York CrossRef. Manage Sci — CrossRef.
Traditionally, in multiob- jective optimization also known as multicriteria optimization , mathematical programming techniques and decision making have been used in an inter- twined manner, and the ultimate aim of solving a multiobjective optimization problem has been characterized as supporting the DM in nding the solution that best ts the DMs preferences. Because of the large amount of research and publications produced in the MCDM eld during the years, we have limited our review. Due to these rea- sons, the optimal solutions found by an optimization algorithm must always be analyzed through a post-optimality analysis for their appropriateness in the context of the problem. In: EMO, volume of lecture notes in computer science, pp 62— Denition 2.
Clearly, research and application in multiobjective optimization involve expertise in optimization as well as in decision support. Get A Copy. Paperback , pages. More Details Original Title.
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- A Tradeoff-Based Interactive Multi-Objective Optimization Method Driven by Evolutionary Algorithms.
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