Abstract
Differential evolution algorithm is a better algorithm for global numeric optimization in the evolution algorithm. The excellent individual in the evolution algorithm contains more abundant information. We propose a mutation method in order of cosine function in order to ensure that the information of such excellent individual will be better inherited and avoid prematurity of the algorithm. Firstly, vector selection probability is calculated according to the cosine computational model in the paper. Secondly, the individual is selected in the sorting order of probability value. Higher sorting position will lead to higher probability of selection. In addition, existing differential evolution algorithms rarely deal with population distribution information. Instead, they only allow for the distribution information of a generation in population evolution. The result is that the performance of the differential evolution algorithm will be affected due to the insufficiency of population information. For this problem, we allow for multi-generation cumulative distribution information of the population in the algorithm and perform eigen decomposition for the covariance matrix. The eigenvector so generated is used to establish the characteristic coordinate system. Crossover operation of the algorithm is performed in the new coordinate system. For such improvement operation, we use IEEECEC2014 as the test function. The experimental results show that this improved algorithm has more improved performance than existing improved DE algorithms.
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Liu, Z., Li, Jb., Song, Q. (2017). Optimized Analysis Based on Improved Mutation and Crossover Operator for Differential Evolution Algorithm. In: Wang, G., Atiquzzaman, M., Yan, Z., Choo, KK. (eds) Security, Privacy, and Anonymity in Computation, Communication, and Storage. SpaCCS 2017. Lecture Notes in Computer Science(), vol 10656. Springer, Cham. https://doi.org/10.1007/978-3-319-72389-1_1
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DOI: https://doi.org/10.1007/978-3-319-72389-1_1
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