Abstract
Computation intelligence is becoming an essential technology during the development of human society. There are many family members in computation intelligence. Many researchers have already studied the mathematical inherent rules of these biology-inspired algorithms to found methods to improve the capacity of the algorithms. In the family of computation intelligence, differential evolution (DE) algorithm shows performance optimization ability. In order to explore the reason why DE could have stable and robust quality. We analyzed the parallelism of the evolutionary process in the iterative process of DE algorithm based on graph theory. By the knowledge of graph theory, it will directly exhibit the essential reason of differential evolution in the algorithm. The research will reveal that the superior DE algorithm have more extent parallelism ability than the elementary algorithm.
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Acknowledgments
This work is supported in part by NSFC-Guangdong Joint Found (U1501254), National Natural Science Foundation of China (61370102, 61472089, 61572143), Natural Science Foundation of Guangdong(2014A030306004, 2014A030308008), Science and Technology Planning Project of Guangdong (2013B 051000076, 2015B010108006, 2015B010131015), Guangdong Natural Science Funds for Distinguished Young Scholar (2014A030306050), the Fundamental Research Funds for the Central Universities, SCUT (2015PT022) and Guangdong High-level personnel of special support program (2014TQ01X664).
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Peng, X., Hao, Z., Huang, H., Wu, H., Liu, F. (2016). Evolutionary Process: Parallelism Analysis of Differential Evolution Algorithm Based on Graph Theory. In: Gong, M., Pan, L., Song, T., Zhang, G. (eds) Bio-inspired Computing – Theories and Applications. BIC-TA 2016. Communications in Computer and Information Science, vol 682. Springer, Singapore. https://doi.org/10.1007/978-981-10-3614-9_20
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DOI: https://doi.org/10.1007/978-981-10-3614-9_20
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