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Multiple Criteria Optimization: Theory, Computation, and Application

R. S. Laundy
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TLDR
Mathematical Background Topics from Linear Algebra Single Objective Linear Programming Determining all Alternative Optima Comments about Objective Row Parametric Programming Utility Functions, Nondominated Criterion Vectors and Efficient Points Point Estimate Weighted-sums Approach.
Abstract
Mathematical Background Topics from Linear Algebra Single Objective Linear Programming Determining all Alternative Optima Comments about Objective Row Parametric Programming Utility Functions, Nondominated Criterion Vectors and Efficient Points Point Estimate Weighted-sums Approach Optimal Weighting Vectors, Scaling and Reduced Feasible Region Methods Vector-Maximum Algorithms Goal Programming Filtering and Set Discretization Multiple Objective Linear Fractional Programming Interactive Procedures Interactive Weighted Tchebycheff Procedure Tchebycheff/Weighted-Sums Implementation Applications Future Directions Index.

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Convex analysis and nonlinear optimization : theory and examples

TL;DR: In this paper, the Karush-Kuhn-Tucker Theorem and Fenchel duality were used for infinite versus finite dimensions, with a list of results and notation.
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TOPSIS for MODM

TL;DR: In this article, the authors extend TOPSIS to solve a multiple objective decision making problem, and obtain a single-objective programming problem by using the max-min operator for the second-order compromise operation.
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Control of quantum phenomena: past, present and future

TL;DR: Adaptive feedback control (AFC) as mentioned in this paper is a state-of-the-art technique for quantum control. But it is not suitable for the case of femtosecond laser sources.

An evolutionary algorithm for multiobjective optimization: the strength Pareto approach

TL;DR: A new evolutionary approach to multicriteria optimization the Strength Pareto Evolutionary Algorithm SPEA is proposed which combines various features of previous multiobjective EAs in a unique manner and is characterized as follows.
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The normalized normal constraint method for generating the Pareto frontier

TL;DR: This paper presents a new formulation of the normal constraint (NC) method that incorporates a critical linear mapping of the design objectives, which has the desirable property that the resulting performance of the method is entirely independent of theDesign objectives scales.