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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 292 (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

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 450 (13 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 492 (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

Comparison of Multiobjective Evolutionary Algorithms: Empirical Results

by Eckart Zitzler, Lothar Thiele, Kalyanmoy Deb , 2000
"... In this paper, we provide a systematic comparison of various evolutionary approaches to multiobjective optimization using six carefully chosen test functions. Each test function involves a particular feature that is known to cause difficulty in the evolutionary optimization process, mainly in conver ..."
Abstract - Cited by 628 (41 self) - Add to MetaCart
In this paper, we provide a systematic comparison of various evolutionary approaches to multiobjective optimization using six carefully chosen test functions. Each test function involves a particular feature that is known to cause difficulty in the evolutionary optimization process, mainly

A Fast Elitist Non-Dominated Sorting Genetic Algorithm for Multi-Objective Optimization: NSGA-II

by Kalyanmoy Deb, Samir Agrawal, Amrit Pratap, T Meyarivan , 2000
"... Multi-objective evolutionary algorithms which use non-dominated sorting and sharing have been mainly criticized for their (i) -4 computational complexity (where is the number of objectives and is the population size), (ii) non-elitism approach, and (iii) the need for specifying a sharing ..."
Abstract - Cited by 662 (15 self) - Add to MetaCart
sharing parameter. In this paper, we suggest a non-dominated sorting based multi-objective evolutionary algorithm (we called it the Non-dominated Sorting GA-II or NSGA-II) which alleviates all the above three difficulties. Specifically, a fast non-dominated sorting approach with computational

A Fast and Elitist Multi-Objective Genetic Algorithm: NSGA-II

by Kalyanmoy Deb, Amrit Pratap, Sameer Agarwal, T. Meyarivan , 2000
"... Multi-objective evolutionary algorithms which use non-dominated sorting and sharing have been mainly criticized for their (i) O(MN computational complexity (where M is the number of objectives and N is the population size), (ii) non-elitism approach, and (iii) the need for specifying a sharing param ..."
Abstract - Cited by 1815 (60 self) - Add to MetaCart
parameter. In this paper, we suggest a non-dominated sorting based multi-objective evolutionary algorithm (we called it the Non-dominated Sorting GA-II or NSGA-II) which alleviates all the above three difficulties. Specifically, a fast non-dominated sorting approach with O(MN ) computational complexity

Multiobjective Evolutionary Algorithms: Analyzing the State-of-the-Art

by David A. Van Veldhuizen, Gary B. Lamont , 2000
"... Solving optimization problems with multiple (often conflicting) objectives is, generally, a very difficult goal. Evolutionary algorithms (EAs) were initially extended and applied during the mid-eighties in an attempt to stochastically solve problems of this generic class. During the past decade, ..."
Abstract - Cited by 440 (7 self) - Add to MetaCart
, a variety of multiobjective EA (MOEA) techniques have been proposed and applied to many scientific and engineering applications. Our discussion's intent is to rigorously define multiobjective optimization problems and certain related concepts, present an MOEA classification scheme

SPEA2: Improving the Strength Pareto Evolutionary Algorithm

by Eckart Zitzler, Marco Laumanns, Lothar Thiele , 2001
"... The Strength Pareto Evolutionary Algorithm (SPEA) (Zitzler and Thiele 1999) is a relatively recent technique for finding or approximating the Pareto-optimal set for multiobjective optimization problems. In different studies (Zitzler and Thiele 1999; Zitzler, Deb, and Thiele 2000) SPEA has shown very ..."
Abstract - Cited by 708 (19 self) - Add to MetaCart
The Strength Pareto Evolutionary Algorithm (SPEA) (Zitzler and Thiele 1999) is a relatively recent technique for finding or approximating the Pareto-optimal set for multiobjective optimization problems. In different studies (Zitzler and Thiele 1999; Zitzler, Deb, and Thiele 2000) SPEA has shown

Multicriteria Optimization

by Matthias Ehrgott , 2005
"... n 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 multiobjectiv ..."
Abstract - Cited by 322 (4 self) - Add to MetaCart
multiobjective optimization problems using transformations to single-objective optimization problems. Finally, I address linear and combinatorial optimization problems with multiple objectives and summarize techniques for solving them. Throughout the article I

An Updated Survey of GA-Based Multiobjective Optimization Techniques

by Carlos A. Coello Coello - ACM COMPUTING SURVEYS , 1998
"... ... this paper is to summarize and organize the information on these current approaches, emphasizing the importance of analyzing the Operations Research techniques in which most of them are based, in an attempt to motivate researchers to look into these mathematical programming approaches for new wa ..."
Abstract - Cited by 129 (0 self) - Add to MetaCart
... this paper is to summarize and organize the information on these current approaches, emphasizing the importance of analyzing the Operations Research techniques in which most of them are based, in an attempt to motivate researchers to look into these mathematical programming approaches for new
Results 1 - 10 of 2,490