Multiple Populations for Multiple Objectives: A Coevolutionary Technique for Solving Multiobjective Optimization Problems

2013 ◽  
Vol 43 (2) ◽  
pp. 445-463 ◽  
Author(s):  
Zhi-Hui Zhan ◽  
Jingjing Li ◽  
Jiannong Cao ◽  
Jun Zhang ◽  
H. S-H Chung ◽  
...  
Keyword(s):  
Multiobjective Optimization ◽  
Optimization Problems ◽  
Multiple Objectives ◽  
Multiple Populations ◽  
Multiobjective Optimization Problems

A Modified Game Theory Approach to Multiobjective Optimization

1988 ◽  
Author(s):  
T. I. Freiheit ◽  
S. S. Rao
Keyword(s):  
Game Theory ◽  
Multiobjective Optimization ◽  
Optimization Problems ◽  
Multiple Objectives ◽  
Theory Approach ◽  
Vector Form ◽  
Multiobjective Problem ◽  
Design Variables ◽  
Multiobjective Optimization Problems ◽  
Competitive Economy

Abstract In todays competitive economy, it is no longer possible for the designer to find solutions to conflicting objective problems which are merely adequate. The designer must select the design variables such that there is a superior compromise between the objectives. To this end, a large number of methods have been developed for the solution of multiobjective optimization problems. The earliest reported in-depth work on the formulation of the multiobjective problem is that of Kuhn and Tucker [1]. Reviews of the progress in the field have been published by Hwang and Masud [2], Stadler [3], Evans [4], and Rastrigin and Eiduk [5]. The methods for optimizing multiple objectives may be categorized into two types, leaving the objectives in vector form and scalarizing the objectives into one equation.

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.

scholarly journals Duality Theorems for (ρ,ψ,d)-Quasiinvex Multiobjective Optimization Problems with Interval-Valued Components

Mathematics ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 894
Author(s):  
Savin Treanţă
Keyword(s):  
Multiobjective Optimization ◽  
Optimization Problems ◽  
Integral Functional ◽  
Multiple Integral ◽  
Control Problems ◽  
Duality Theorems ◽  
New Class ◽  
Duality Results ◽  
Multiobjective Optimization Problems ◽  
Interval Valued

The present paper deals with a duality study associated with a new class of multiobjective optimization problems that include the interval-valued components of the ratio vector. More precisely, by using the new notion of (ρ,ψ,d)-quasiinvexity associated with an interval-valued multiple-integral functional, we formulate and prove weak, strong, and converse duality results for the considered class of variational control problems.

scholarly journals Robust neutrosophic programming approach for solving intuitionistic fuzzy multiobjective optimization problems

2021 ◽  
Author(s):  
Firoz Ahmad
Keyword(s):  
Multiobjective Optimization ◽  
Optimization Problems ◽  
Practical Implication ◽  
Real Life ◽  
Future Research ◽  
Programming Approach ◽  
Solution Approach ◽  
Intuitionistic Fuzzy Numbers ◽  
Intuitionistic Fuzzy ◽  
Multiobjective Optimization Problems

AbstractThis study presents the modeling of the multiobjective optimization problem in an intuitionistic fuzzy environment. The uncertain parameters are depicted as intuitionistic fuzzy numbers, and the crisp version is obtained using the ranking function method. Also, we have developed a novel interactive neutrosophic programming approach to solve multiobjective optimization problems. The proposed method involves neutral thoughts while making decisions. Furthermore, various sorts of membership functions are also depicted for the marginal evaluation of each objective simultaneously. The different numerical examples are presented to show the performances of the proposed solution approach. A case study of the cloud computing pricing problem is also addressed to reveal the real-life applications. The practical implication of the current study is also discussed efficiently. Finally, conclusions and future research scope are suggested based on the proposed work.

scholarly journals Assessing the Performance of Interactive Multiobjective Optimization Methods

2021 ◽  
Vol 54 (4) ◽  
pp. 1-27
Author(s):  
Bekir Afsar ◽  
Kaisa Miettinen ◽  
Francisco Ruiz
Keyword(s):  
Multiobjective Optimization ◽  
Decision Maker ◽  
Optimization Problems ◽  
Optimization Methods ◽  
Interactive Methods ◽  
Value Functions ◽  
Technical Aspects ◽  
Preference Information ◽  
Multiobjective Optimization Problems ◽  
Human Decision

Interactive methods are useful decision-making tools for multiobjective optimization problems, because they allow a decision-maker to provide her/his preference information iteratively in a comfortable way at the same time as (s)he learns about all different aspects of the problem. A wide variety of interactive methods is nowadays available, and they differ from each other in both technical aspects and type of preference information employed. Therefore, assessing the performance of interactive methods can help users to choose the most appropriate one for a given problem. This is a challenging task, which has been tackled from different perspectives in the published literature. We present a bibliographic survey of papers where interactive multiobjective optimization methods have been assessed (either individually or compared to other methods). Besides other features, we collect information about the type of decision-maker involved (utility or value functions, artificial or human decision-maker), the type of preference information provided, and aspects of interactive methods that were somehow measured. Based on the survey and on our own experiences, we identify a series of desirable properties of interactive methods that we believe should be assessed.

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