Abstract
Comparing with single-population optimization, co-evolution has more benefits in tackling multi-objective optimization problems, as different evolutionary algorithms can work collaboratively. This paper presents a new co-evolution algorithm which employs three populations and integrates particle swarm optimization (PSO) and differential evolution (DE) into the framework of decomposition, named MODEPSO. The main contribution is that the elite solutions got by PSO and archive evolution are considered as evolutionary candidates which will be further evolved by DE operation, so PSO and DE operators can work collaboratively. Experimental results indicate that MODEPSO has better performance than the compared algorithms.
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Acknowledgements
This work was supported by the National Natural Science Foundation of China under Grant 61402291, Seed Funding from Scientific and Technical Innovation Council of Shenzhen Government under Grant 0000012528, Foundation for Distinguished Young Talents in Higher Education of Guangdong under Grant 2014KQNCX129, and Natural Science Foundation of SZU under Grant 201531.
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Yang, S., Wang, W., Lin, Q., Chen, J. (2017). A Novel PSO-DE Co-evolutionary Algorithm Based on Decomposition Framework. In: Qiu, M. (eds) Smart Computing and Communication. SmartCom 2016. Lecture Notes in Computer Science(), vol 10135. Springer, Cham. https://doi.org/10.1007/978-3-319-52015-5_39
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DOI: https://doi.org/10.1007/978-3-319-52015-5_39
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