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Evolutionary Algorithms for Multiobjective Optimization

by Eckart Zitzler , 2002
"... Multiple, often conflicting objectives arise naturally in most real-world optimization scenarios. As evolutionary algorithms possess several characteristics due to which they are well suited to this type of problem, evolution-based methods have been used for multiobjective optimization for more than ..."
Abstract - Cited by 436 (14 self) - Add to MetaCart
Multiple, often conflicting objectives arise naturally in most real-world optimization scenarios. As evolutionary algorithms possess several characteristics due to which they are well suited to this type of problem, evolution-based methods have been used for multiobjective optimization for more

An Overview of Evolutionary Algorithms in Multiobjective Optimization

by Carlos M. Fonseca, Peter J. Fleming - Evolutionary Computation , 1995
"... The application of evolutionary algorithms (EAs) in multiobjective optimization is currently receiving growing interest from researchers with various backgrounds. Most research in this area has understandably concentrated on the selection stage of EAs, due to the need to integrate vectorial performa ..."
Abstract - Cited by 487 (13 self) - Add to MetaCart
The application of evolutionary algorithms (EAs) in multiobjective optimization is currently receiving growing interest from researchers with various backgrounds. Most research in this area has understandably concentrated on the selection stage of EAs, due to the need to integrate vectorial

Multiobjective Optimization Using Nondominated Sorting in Genetic Algorithms

by N. Srinivas, Kalyanmoy Deb - Evolutionary Computation , 1994
"... In trying to solve multiobjective optimization problems, many traditional methods scalarize the objective vector into a single objective. In those cases, the obtained solution is highly sensitive to the weight vector used in the scalarization process and demands the user to have knowledge about t ..."
Abstract - Cited by 524 (4 self) - Add to MetaCart
In trying to solve multiobjective optimization problems, many traditional methods scalarize the objective vector into a single objective. In those cases, the obtained solution is highly sensitive to the weight vector used in the scalarization process and demands the user to have knowledge about

Genetic Algorithms for Multiobjective Optimization: Formulation, Discussion and Generalization

by Carlos M. Fonseca, Peter J. Fleming , 1993
"... The paper describes a rank-based fitness assignment method for Multiple Objective Genetic Algorithms (MOGAs). Conventional niche formation methods are extended to this class of multimodal problems and theory for setting the niche size is presented. The fitness assignment method is then modified to a ..."
Abstract - Cited by 610 (15 self) - Add to MetaCart
to allow direct intervention of an external decision maker (DM). Finally, the MOGA is generalised further: the genetic algorithm is seen as the optimizing element of a multiobjective optimization loop, which also comprises the DM. It is the interaction between the two that leads to the determination of a

A Niched Pareto Genetic Algorithm for Multiobjective Optimization

by Jeffrey Horn, Nicholas Nafpliotis, David E. Goldberg - IN PROCEEDINGS OF THE FIRST IEEE CONFERENCE ON EVOLUTIONARY COMPUTATION, IEEE WORLD CONGRESS ON COMPUTATIONAL INTELLIGENCE , 1994
"... Many, if not most, optimization problems have multiple objectives. Historically, multiple objectives have been combined ad hoc to form a scalar objective function, usually through a linear combination (weighted sum) of the multiple attributes, or by turning objectives into constraints. The genetic a ..."
Abstract - Cited by 395 (6 self) - Add to MetaCart
Many, if not most, optimization problems have multiple objectives. Historically, multiple objectives have been combined ad hoc to form a scalar objective function, usually through a linear combination (weighted sum) of the multiple attributes, or by turning objectives into constraints. The genetic

An Evolution Strategy for Multiobjective Optimization

by Lino Costa, Pedro Oliveira , 2002
"... Almost all approaches to multiobjective optimization are based on Genetic Algorithms, and implementations based on Evolution Strategies (ESs) are very rare. In this paper, a new approach to multiobjective optimization, based on ESs, is presented. The comparisons with other algorithms indicate a good ..."
Abstract - Cited by 6 (0 self) - Add to MetaCart
Almost all approaches to multiobjective optimization are based on Genetic Algorithms, and implementations based on Evolution Strategies (ESs) are very rare. In this paper, a new approach to multiobjective optimization, based on ESs, is presented. The comparisons with other algorithms indicate a

A Comprehensive Survey of Evolutionary-Based Multiobjective Optimization Techniques

by Carlos A. Coello Coello - Knowledge and Information Systems , 1998
"... . This paper presents a critical review of the most important evolutionary-based multiobjective optimization techniques developed over the years, emphasizing the importance of analyzing their Operations Research roots as a way to motivate the development of new approaches that exploit the search cap ..."
Abstract - Cited by 286 (22 self) - Add to MetaCart
. This paper presents a critical review of the most important evolutionary-based multiobjective optimization techniques developed over the years, emphasizing the importance of analyzing their Operations Research roots as a way to motivate the development of new approaches that exploit the search

MULTIOBJECTIVE OPTIMIZATION IN COMPUTATIONAL ELECTROMAGNETICS

by Stefan Jakobsson, Fredrik Edelvik
"... In this paper we show how multiobjective optimization can be applied to elec-tromagnetic problems. The optimization algorithms are combined with CAD and mesh generation software, and electromagnetic solvers. Three dierent multiobjective optimization methods are applied: one evolutionary method, one ..."
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In this paper we show how multiobjective optimization can be applied to elec-tromagnetic problems. The optimization algorithms are combined with CAD and mesh generation software, and electromagnetic solvers. Three dierent multiobjective optimization methods are applied: one evolutionary method, one

Multiobjective Optimization: Improved FPTAS . . .

by George Tsaggouris, Christos Zaroliagis - THEORY COMPUT SYST
"... We provide an improved FPTAS for multiobjective shortest paths—a fundamental (NP-hard) problem in multiobjective optimization—along with a new generic method for obtaining FPTAS to any multiobjective optimization problem with non-linear objectives. We show how these results can be used to obtain bet ..."
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We provide an improved FPTAS for multiobjective shortest paths—a fundamental (NP-hard) problem in multiobjective optimization—along with a new generic method for obtaining FPTAS to any multiobjective optimization problem with non-linear objectives. We show how these results can be used to obtain

DEMO: Differential Evolution for multiobjective optimization

by Tea Robič, Bogdan Filipič - In Proceedings of the 3rd International Conference on Evolutionary MultiCriterion Optimization (EMO 2005 , 2005
"... Abstract. Differential Evolution (DE) is a simple but powerful evolutionary optimization algorithm with many successful applications. In this paper we propose Differential Evolution for Multiobjective Optimization (DEMO) – a new approach to multiobjective optimization based on DE. DEMO combines the ..."
Abstract - Cited by 48 (2 self) - Add to MetaCart
Abstract. Differential Evolution (DE) is a simple but powerful evolutionary optimization algorithm with many successful applications. In this paper we propose Differential Evolution for Multiobjective Optimization (DEMO) – a new approach to multiobjective optimization based on DE. DEMO combines
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