A Steady-State and Generational Evolutionary Algorithm for Dynamic Multiobjective Optimization

This paper presents a new algorithm, called steady-state and generational evolutionary algorithm, which combines the fast and steadily tracking ability of steady-state algorithms and good diversity preservation of generational algorithms, for handling dynamic multiobjective optimization. Unlike most...

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Bibliographic Details
Published in:	IEEE transactions on evolutionary computation Vol. 21; no. 1; pp. 65 - 82
Main Authors:	Jiang, Shouyong, Yang, Shengxiang
Format:	Journal Article
Language:	English
Published:	New York IEEE 01.02.2017 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Change detection change response Changing environments Dynamic characteristics dynamic multiobjective optimization Evolutionary algorithms Evolutionary computation Genetic algorithms Heuristic algorithms Multiple objective analysis Optical fibers Optimization Pareto optimization Sociology Statistics Steady state steady-state and generational evolutionary algorithm Tracking
ISSN:	1089-778X, 1941-0026
Online Access:	Get full text
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