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Multiple Criteria Optimization: Theory, Computation, and Application
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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.read more
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A multiobjective optimization approach to smart growth in land development
TL;DR: A multiobjective optimization model of Smart Growth is applied to land development—employing linear and convex quadratic objective functions subject to polyhedral and binary constraints for the stakeholders and the resulting optimization problems are convex, quadRatic mixed integer programs that are NP-complete.
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A multi-objective approach for optimal prioritization of energy efficiency measures in buildings: Model, software and case studies
TL;DR: In this article, the authors developed a methodology and a software tool for their optimal prioritization of energy efficiency measures in the residential and small commercial sector. But the methodology used is generic and could be implemented in the case of a new building or retrofitting an existing building.
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Research and application of a hybrid model based on multi-objective optimization for electrical load forecasting
TL;DR: In this paper, a modified generalized regression neural network (GRNN) based on a multi-objective firefly algorithm (MOFA), employed to optimize the initial weights and thresholds of the GRNN, is proposed.
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Core Hunter: an algorithm for sampling genetic resources based on multiple genetic measures
Chris Thachuk,José Crossa,Jorge Franco,Jorge Franco,Susanne Dreisigacker,Marilyn L. Warburton,Marilyn L. Warburton,Guy Davenport +7 more
TL;DR: Core Hunter is an advanced stochastic local search algorithm capable of selecting core subsets that have high average genetic distance between accessions, or rich genetic diversity overall, or a combination of both.