Journal
COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE
Volume 2018, Issue -, Pages -Publisher
HINDAWI LTD
DOI: 10.1155/2018/4231647
Keywords
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Funding
- Natural Science Foundation of Guangxi Province [2014GXNSFBA118283]
- Ability Enhancement Project of Young Teachers in Guangxi Universities [2018KY0579]
- Philosophy and Social Science Planning Project of Guangxi Province [17FJY008]
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For the deficiency of the basic sine-cosine algorithm in dealing with global optimization problems such as the low solution precision and the slow convergence speed, a new unproved sine-cosine algorithm is proposed in this paper. The improvement involves three optimization strategies. Firstly, the method of exponential decreasing conversion parameter and linear decreasing inertia weight is adopted to balance the global exploration and local development ability of the algorithm. Secondly, it uses the random individuals near the optimal individuals to replace the optimal individuals in the primary algorithm, which allows the algorithm to easily jump out of the local optimum and increases the search range effectively. Finally, the greedy Levy mutation strategy is used for the optimal individuals to enhance the local development ability of the algorithm. The experimental results show that the proposed algorithm can effectively avoid falling into the local optimum, and it has faster convergence speed and higher optimization accuracy.
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