Abstract
The Conical Area Evolutionary Algorithm (CAEA) has exhibited significant performance for bi-objective optimization problems. In this paper, a parallel partially evolved CAEA (peCAEA) on message-passing clusters is proposed to further reduce the runtime of the sequential CAEA for bi-objective optimization. Each island maintains an entire population but is responsible for evolution of only a portion of the population. Further, the elitist migration adopted in order to share information among islands and speed up the evolutionary process. Additionally, a dynamic directional migration topology presented here to obtain a satisfactory balance between convergence speed and communication costs. Experimental results on ZDT test problems indicate that the peCAEA obtains satisfactory solution quality with good speedup on clusters.
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Acknowledgments
This work is supported by the National Natural Science Foundation of China (No. 61203310, 61503087), the Natural Science Foundation of Guangdong Province, China (No. 2015A030313204, 2015A030310446), the China Scholarship Council (CSC) (No. 201406155076, 201408440193), the Pearl River S&T Nova Program of Guangzhou (No. 2014J2200052), the Fundamental Research Funds for the Central Universities, SCUT (No. 2013ZZ0048, 2013ZM0104), and the Science and Technology Planning Project of Guangdong Province, China (No. 2013B010401003).
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Ying, W., Wu, B., Xie, Y., Feng, Y., Wu, Y. (2017). Parallelization of the Conical Area Evolutionary Algorithm on Message-Passing Clusters. In: Xhafa, F., Patnaik, S., Yu, Z. (eds) Recent Developments in Intelligent Systems and Interactive Applications. IISA 2016. Advances in Intelligent Systems and Computing, vol 541. Springer, Cham. https://doi.org/10.1007/978-3-319-49568-2_6
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DOI: https://doi.org/10.1007/978-3-319-49568-2_6
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