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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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An Orthogonal Multi-objective Evolutionary Algorithm for Multi-objective Optimization Problems with Constraints
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A multiobjective modeling approach to locating incinerators
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Book ChapterDOI
Multiple criteria mathematical programming: an updated overview and several approaches
TL;DR: MCDM refers to making decisions in the presence of multiple, usually conflicting, objectives, which pervade all that the authors do and include such public policy tasks as determining a country’s policy developing a national energy plan, as well as planning national defense expenditures.
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Interactive Multiobjective Optimization Procedure
TL;DR: In this article, an interactive multiobjective optimization procedure (IMOOP) that uses an aspiration-level approach to generate Pareto points is developed, which provides the designer or the decision maker with a formal means for efficient design exploration around a given pareto point.
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A Comparative Study of Multiple-Objective Metaheuristics on the Bi-Objective Set Covering Problem and the Pareto Memetic Algorithm
TL;DR: The results indicate that the performance of multiple-objective metaheuristics may differ radically even if the methods are based on the same single objective algorithm and use exactly the same problem-specific operators.