Weight Induced Norm Approach to Group Decision Making for Multiobjective Optimization Problems in Systems Engineering

2020 ◽  
Vol 14 (2) ◽  
pp. 1580-1591
Author(s):  
Wei-Yu Chiu ◽  
Shri Harish Manoharan ◽  
Tai-Ying Huang
Keyword(s):  
Decision Making ◽  
Multiobjective Optimization ◽  
Group Decision Making ◽  
Systems Engineering ◽  
Group Decision ◽  
Optimization Problems ◽  
Multiobjective Optimization Problems

A Novel Multiobjective Memetic Algorithm Based on IWO-DE and its Application in Nutrition Decision Making Problem

2014 ◽  
Vol 989-994 ◽  
pp. 1849-1852
Author(s):  
Gao Ping Wang ◽  
Meng Zhang ◽  
Wei Wei Zhao
Keyword(s):  
Decision Making ◽  
Multiobjective Optimization ◽  
Memetic Algorithm ◽  
Optimization Problems ◽  
Memetic Algorithms ◽  
Pareto Optimum ◽  
Invasive Weed Optimization ◽  
Invasive Weed ◽  
Multiobjective Optimization Problems ◽  
Decision Making Problem

In this paper ,we discuss multiobjective optimization problems solved by Memetic algorithms. We present A novel multiobjective memetic algorithm based on invasive weed optimization and differential evolution (IWO-DE) to solve this class of problems .We present the Nutrition Prescription Model for Meals.the IWO-DE is applied to solve the nutrition decision making problem to map the Pareto-optimum front. The results in the problem show its effectiveness.

Multiobjective Optimization

AI Magazine ◽  
2008 ◽  
Vol 29 (4) ◽  
pp. 47 ◽  
Author(s):  
Matthias Ehrgott
Keyword(s):  
Decision Making ◽  
Combinatorial Optimization ◽  
Multiobjective Optimization ◽  
Real World ◽  
Optimization Problems ◽  
Multiple Objectives ◽  
Combinatorial Optimization Problems ◽  
Multiobjective Optimization Problems ◽  
Single Objective

Using some real world examples I illustrate the important role of multiobjective optimization in decision making and its interface with preference handling. I explain what optimization in the presence of multiple objectives means and discuss some of the most common methods of solving multiobjective optimization problems using transformations to single objective optimisation problems. Finally, I address linear and combinatorial optimization problems with multiple objectives and summarize techniques for solving them. Throughout the article, I refer to the real world examples introduced at the beginning.

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