Combining convergence and diversity in evolutionary multi-objective optimization

Combining convergence and diversity in evolutionary multi-objective optimization (2002)

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by Marco Laumanns , Lothar Thiele , Kalyanmoy Deb , Eckart Zitzler

Venue:	Evolutionary Computation
Citations:	84 - 7 self

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Datum	Value	Source
TITLE	Combining convergence and diversity in evolutionary multi-objective optimization	INFERENCE
AUTHOR NAME	Marco Laumanns	SVM HeaderParse 0.2
AUTHOR AFFIL	Department of Information Technology and Electrical Engineering, Swiss Federal; Institute of Technology Zurich	SVM HeaderParse 0.2
AUTHOR ADDR	8092 Zurich, Switzerland	SVM HeaderParse 0.2
AUTHOR NAME	Lothar Thiele	SVM HeaderParse 0.2
AUTHOR AFFIL	Department of Information Technology and Electrical Engineering, Swiss Federal; Institute of Technology Zurich	SVM HeaderParse 0.2
AUTHOR ADDR	8092 Zurich, Switzerland	SVM HeaderParse 0.2
AUTHOR NAME	Kalyanmoy Deb	SVM HeaderParse 0.2
AUTHOR AFFIL	Department of Mechanical Engineering, Indian Institute of Technology Kanpur,	SVM HeaderParse 0.2
AUTHOR ADDR	Kanpur, PIN 208 016, India	SVM HeaderParse 0.2
AUTHOR NAME	Eckart Zitzler	SVM HeaderParse 0.2
AUTHOR AFFIL	Department of Information Technology and Electrical Engineering, Swiss Federal; Institute of Technology Zurich	SVM HeaderParse 0.2
AUTHOR ADDR	8092 Zurich, Switzerland	SVM HeaderParse 0.2
ABSTRACT	Over the past few years, the research on evolutionary algorithms has demonstrated their niche in solving multiobjective optimization problems, where the goal is to �nd a number of Pareto-optimal solutions in a single simulation run. Many studies have depicted different ways evolutionary algorithms can progress towards the Paretooptimal set with a widely spread distribution of solutions. However, none of the multiobjective evolutionary algorithms (MOEAs) has a proof of convergence to the true Pareto-optimal solutions with a wide diversity among the solutions. In this paper, we discuss why a number of earlier MOEAs do not have such properties. Based on the concept of-dominance, new archiving strategies are proposed that overcome this fundamental problem and provably lead to MOEAs that have both the desired convergence and distribution properties. A number of modi�cations to the baseline algorithm are also suggested. The concept of-dominance introduced in this paper is practical and should make the proposed algorithms useful to researchers and practitioners alike.	SVM HeaderParse 0.2
YEAR	2002	INFERENCE
VENUE	Evolutionary Computation	INFERENCE
VENUE TYPE	JOURNAL	INFERENCE
PAGES	2002	INFERENCE
VOLUME	10	INFERENCE
CITATIONS	28 found	ParsCit 1.0