Abstract
The existing water evaporation optimization algorithm has some disadvantages, such as slow convergence speed, poor accuracy and so on. In order to accelerate WEO’s convergence and improve its accuracy, in this paper, we propose a water evaporation optimization algorithm based on classification and unbiased search (CUS-WEO). In this algorithm, different learning objects are selected for better and poorer individuals in monolayer evaporation phase, which can balance the algorithm’s exploration ability and exploitation ability. Meanwhile, the unbiased search information is taken as the base vector, and the number of disturbance terms and the search direction are increased in droplet evaporation phase, which can improve the convergence accuracy. To verify the performance of this algorithm, a series of experiments are carried out on 15 benchmark functions and compared with WEO algorithm. The results show that the proposed algorithm can obtain global optimal solutions with higher accuracy and speed up the convergence.
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Wang, Y., Che, X. (2019). Water Evaporation Optimization Algorithm Based on Classification and Unbiased Search. In: Huang, DS., Bevilacqua, V., Premaratne, P. (eds) Intelligent Computing Theories and Application. ICIC 2019. Lecture Notes in Computer Science(), vol 11643. Springer, Cham. https://doi.org/10.1007/978-3-030-26763-6_3
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DOI: https://doi.org/10.1007/978-3-030-26763-6_3
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