Introduction to Multi-objective Optimization and Decision-Making Analysis

2019 ◽  
pp. 21-45
Author(s):  
Mohammad Kiani-Moghaddam ◽  
Mojtaba Shivaie ◽  
Philip D. Weinsier
Keyword(s):  
Decision Making ◽  
Multi Objective Optimization ◽  
Multi Objective

Multi-objective optimization for safety and reliability trade-off: Optimization and results processing

2018 ◽  
Vol 232 (6) ◽  
pp. 661-676
Author(s):  
Cristina Johansson ◽  
Johan Ölvander ◽  
Micael Derelöv
Keyword(s):  
Decision Making ◽  
Solid Base ◽  
Multi Objective Optimization ◽  
Specific System ◽  
Multi Objective ◽  
Trade Offs ◽  
Design Alternatives ◽  
Safety And Reliability ◽  
Potential Issue ◽  
Selection Of

In early design phases, it is vital to be able to screen the design space for a set of promising design alternatives for further study. This article presents a method able to balance several objectives of different mathematical natures, with high impact on the design choices. The method (MOSART) handles multi-objective optimization for safety and reliability trade-offs. The article focuses on optimization problem approach and processing of results as a base for decision-making. The output of the optimization step is the selection of specific system elements obtaining the best balance between the targets. However, what is a good base for decision can easily transform into too much information and overloading of the decision-maker. To solve this potential issue, from a set of Pareto optimal solutions, a smaller sub-set of selected solutions are visualized and filtered out using preference levels of the objectives, yielding a solid base for decision-making and valuable information on potential solutions. Trends were observed regarding each system element and discussed while processing the results of the analysis, supporting the decision of one final best solution.

A Method for Solving Multi-Objective Optimization Problems Containing an Infinite Number of Parameterized Objectives

2020 ◽  
Author(s):  
Eliot Rudnick-Cohen
Keyword(s):  
Decision Making ◽  
Infinite Number ◽  
Optimization Problems ◽  
Pareto Frontier ◽  
Brute Force ◽  
Multi Objective Optimization ◽  
New Approach ◽  
Multi Objective ◽  
Brute Force Approach ◽  
Better Than

Abstract Multi-objective decision making problems can sometimes involve an infinite number of objectives. In this paper, an approach is presented for solving multi-objective optimization problems containing an infinite number of parameterized objectives, termed “infinite objective optimization”. A formulation is given for infinite objective optimization problems and an approach for checking whether a Pareto frontier is a solution to this formulation is detailed. Using this approach, a new sampling based approach is developed for solving infinite objective optimization problems. The new approach is tested on several different example problems and is shown to be faster and perform better than a brute force approach.

Multi-Objective Optimization and Parameter Tuning for Turbine PID Controller

2013 ◽  
Vol 779-780 ◽  
pp. 971-976
Author(s):  
Yuan Sheng Lin ◽  
Yong Li ◽  
Fei Fei Song ◽  
Da Wei Teng
Keyword(s):  
Decision Making ◽  
Pid Controller ◽  
Optimization Technique ◽  
Disturbance Attenuation ◽  
Subsequent Stage ◽  
Multi Objective Optimization ◽  
Pareto Solution ◽  
Acceptable Solution ◽  
Multi Objective ◽  
Multi Attribute Decision Making

The tuning of PID controller parameters is the most important task in PID design process. A new tuning method is presented for PID parameters, based on multi-objective optimization technique and multi-attribute decision making method. Three performances of a PID controller, i.e. the accurate set point tracking, disturbance attenuation and robust stability are studied simultaneously. These specifications are usually competitive and any acceptable solution requires a tradeoff among them. A hybrid approaches is proposed. In the first stage, a Non-dominated Sorting Genetic Algorithm II (NSGA II) is employed to approximate the set of Pareto solution through an evolutionary optimization process. In the subsequent stage, a multi-attribute decision making (MADM) approach is adopted to rank these solutions from best to worst and to determine the best solution in a deterministic environment with a single decision maker. The ranking of Pareto solution is based on entropy weight and TOPSIS method. A turbine PID design example is conducted to illustrate the analysis process in present study. The effectiveness of this universal framework is supported by the simulation results.

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