Minimum Manhattan Distance Approach to Multiple Criteria Decision Making in Multiobjective Optimization Problems

A minimum Manhattan distance (MMD) approach to multiple criteria decision making in multiobjective optimization problems (MOPs) is proposed. The approach selects the final solution corresponding with a vector that has the MMD from a normalized ideal vector. This procedure is equivalent to the knee s...

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Veröffentlicht in:	IEEE transactions on evolutionary computation Jg. 20; H. 6; S. 972 - 985
Hauptverfasser:	Wei-Yu Chiu, Yen, Gary G., Teng-Kuei Juan
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.12.2016 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Decision making Divide and conquer (D&C) approach Electronic mail Evolutionary algorithms Evolutionary computation knee solutions Linear programming minimum Manhattan distance (MMD) approach multicriteria decision making (MCDM) multiobjective evolutionary algorithms (MOEAs) multiobjective optimization problems (MOPs) multiple attribute decision making (MADM) multiple criteria decision making (MCDM) Multiple criterion Optimization Pareto optimization Visualization
ISSN:	1089-778X, 1941-0026
Online-Zugang:	Volltext
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