Pareto
LAPPEENRANTA UNIVERSITY OF TECHNOLOGY
Laboratory of Information Processing
Jouni Lampinen
Multiobjective Nonlinear Pareto-Optimization
A Pre-Investigation Report
LAPPEENRANTA 2000
1(30)
Contents
1 Introduction 2 Major Information Sources 2.1 2.2 2.3 2.4 2.5 Literature surveys, reviews Bibliographies Thesis works Books Some significant articles 13 15 17 18 23 25 27 2 9
3. Basic Problem Statements 4. Classification for Multiobjective Optimization Approaches 5. Usage of Weighted Objective Functions 6. Pareto Optimization – Definitions 7. Evaluation of Multiobjective Evolutionary Algorithms 8. Concluding Remarks References
2(30)
1 Introduction
This report surveys briefly multiobjective nonlinear Pareto-optimization. The focus is on the usage of evolutionary algorithms for multiobjective decision making. The term evolutionary algorithms refer to a class of computationally intelligent algorithms, which are based on an artificial model of evolution in nature. When considering multiobjective optimization problems, the most frequently applied evolutionary optimization algorithms are genetic algorithms [Gol90] and evolution strategies [Schw95]. The purpose of this report is to serve as an initial investigation for establishing a future research project. The weight is on literature survey, finding the most important previous works, explaining the problem, methodology, terminology, classifications, concepts etc. In a nutshell, the objective is a brief up-to-date presentation of Status Quo, including the recent advances. Practical optimization problems, especially the engineering design optimization problems, seem to have a multiobjective nature much more frequently than a single objective one. Typically, some structural performance criteria are to be maximized, while the weight of the structure and the implementation costs should be minimized simultaneously. For example, consider designing the appearance of a wing for an advanced military
Laboratory of Information Processing
Jouni Lampinen
Multiobjective Nonlinear Pareto-Optimization
A Pre-Investigation Report
LAPPEENRANTA 2000
1(30)
Contents
1 Introduction 2 Major Information Sources 2.1 2.2 2.3 2.4 2.5 Literature surveys, reviews Bibliographies Thesis works Books Some significant articles 13 15 17 18 23 25 27 2 9
3. Basic Problem Statements 4. Classification for Multiobjective Optimization Approaches 5. Usage of Weighted Objective Functions 6. Pareto Optimization – Definitions 7. Evaluation of Multiobjective Evolutionary Algorithms 8. Concluding Remarks References
2(30)
1 Introduction
This report surveys briefly multiobjective nonlinear Pareto-optimization. The focus is on the usage of evolutionary algorithms for multiobjective decision making. The term evolutionary algorithms refer to a class of computationally intelligent algorithms, which are based on an artificial model of evolution in nature. When considering multiobjective optimization problems, the most frequently applied evolutionary optimization algorithms are genetic algorithms [Gol90] and evolution strategies [Schw95]. The purpose of this report is to serve as an initial investigation for establishing a future research project. The weight is on literature survey, finding the most important previous works, explaining the problem, methodology, terminology, classifications, concepts etc. In a nutshell, the objective is a brief up-to-date presentation of Status Quo, including the recent advances. Practical optimization problems, especially the engineering design optimization problems, seem to have a multiobjective nature much more frequently than a single objective one. Typically, some structural performance criteria are to be maximized, while the weight of the structure and the implementation costs should be minimized simultaneously. For example, consider designing the appearance of a wing for an advanced military
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