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

Jiang, Shouyong and Yang, S (2017) A Steady-State and Generational Evolutionary Algorithm for Dynamic Multiobjective Optimization. IEEE Transactions on Evolutionary Computation, 21 (1). pp. 65-82. ISSN 1089-778X

Full content URL: https://doi.org/10.1109/TEVC.2016.2574621

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Item Type:Article
Item Status:Live Archive


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 If a change is detected, it reuses a portion of outdated solutions with good distribution and relocates a number of solutions close to the new Pareto front based on the information collected from previous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. © 2016 IEEE.

Additional Information:cited By 22
Keywords:Multiobjective optimization, Change detection, change response, Changing environment, Dynamic characteristics, Dynamic multiobjective optimization, Environmental change, State-of-the-art methods, Steady state, Evolutionary algorithms
Subjects:G Mathematical and Computer Sciences > G400 Computer Science
Divisions:College of Science > School of Computer Science
ID Code:35666
Deposited On:01 May 2019 12:57

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