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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Summary: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 existing approaches for dynamic multiobjective optimization, the proposed algorithm detects environmental changes and responds to them in a steady-state manner. If a change is detected, it reuses a portion of outdated solutions with good distribution and relocates a number of solutions close to the new Pareto front based on the information collected from previous environments and the new environment. This way, the algorithm can quickly adapt to changing environments and thus is expected to provide a good tracking ability. The proposed algorithm is tested on a number of bi- and three-objective benchmark problems with different dynamic characteristics and difficulties. Experimental results show that the proposed algorithm is very competitive for dynamic multiobjective optimization in comparison with state-of-the-art methods.
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ISSN:1089-778X
1941-0026
DOI:10.1109/TEVC.2016.2574621