research-article

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

Authors:

Shouyong Jiang,

Shengxiang YangAuthors Info & Claims

IEEE Transactions on Evolutionary Computation, Volume 21, Issue 1

Pages 65 - 82

https://doi.org/10.1109/TEVC.2016.2574621

Published: 01 February 2017 Publication History

Abstract

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 If a change is detected, it reuses a portion of outdated solutions with good distribution and relocates a number of solutions close to the new Pareto front based on the information collected from previous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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cover image IEEE Transactions on Evolutionary Computation

IEEE Transactions on Evolutionary Computation Volume 21, Issue 1

February 2017

166 pages

ISSN:1089-778X

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https://dl.acm.org/doi/10.1007/s11227-024-06832-0
Luo ZXiong JPeng HZhan GZhang QWang HZhou XLi WHuang YLi XHandl J(2024)A Micro Dynamic Multi-objective Evolutionary Algorithm for Small-scale Smart Greenhouse with Low-power MicroprocessorProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3638530.3654276(687-690)Online publication date: 14-Jul-2024
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