Abstract
In view of complex optimization problems with high nonlinearity and multiple extremums, harmony search algorithm has problems of slow convergence speed, being easy to be stalled and difficult to combine global search with local search. Therefore, a global competitive harmonic search algorithm based on stochastic attraction is proposed. Firstly, by introducing the stochastic attraction model to adjust the harmonic vector, the harmonic search algorithm was greatly improved and prone to fall into the local optimum. Secondly, the competitive search strategy was made to generate two harmony vectors for each iteration and make competitive selection. The adaptive global adjustment and local search strategy were designed to effectively balance the global and local search capabilities of the algorithm. The typical test function was used to test the algorithm. The experimental results show that the algorithm has high precision and the ability to find the global optimum compared with the existing algorithms. The overall accuracy is increased by more than 50%.
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Zhang, X., He, J., Shen, Q. (2020). Research on Global Competition and Acoustic Search Algorithm Based on Random Attraction. In: Qin, P., Wang, H., Sun, G., Lu, Z. (eds) Data Science. ICPCSEE 2020. Communications in Computer and Information Science, vol 1258. Springer, Singapore. https://doi.org/10.1007/978-981-15-7984-4_15
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DOI: https://doi.org/10.1007/978-981-15-7984-4_15
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