首页|Studies from Guangxi University of Finance & Economics Update Curr ent Data on Artificial Intelligence (Imatsa - an Improved and Adaptive Intellige nt Optimization Algorithm Based On Tunicate Swarm Algorithm)
Studies from Guangxi University of Finance & Economics Update Curr ent Data on Artificial Intelligence (Imatsa - an Improved and Adaptive Intellige nt Optimization Algorithm Based On Tunicate Swarm Algorithm)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Current study results on Artificial Intelligence have been published. According to news reporting originating from Guangxi, Peopl e's Republic of China, by NewsRx correspondents, research stated, "Swarm intelli gence optimization algorithm has been proved to perform well in the field of par ameter optimization. In order to further improve the performance of intelligent optimization algorithm, this paper proposes an improved and adaptive tunicate sw arm algorithm (IMATSA) based on tunicate swarm algorithm (TSA)." Financial supporters for this research include National Natural Science Foundati on of China (NSFC), National Natural Science Foundation of Guangxi Province. Our news editors obtained a quote from the research from the Guangxi University of Finance & Economics, "IMATSA improves TSA in the following four aspects: population diversity, local search convergence speed, jumping out of l ocal optimal position, and balancing global and local search. Firstly, IMATSA ad opts Tent map and quadratic interpolation to initialize population and enhance t he diversity. Secondly, IMATSA uses Golden-Sine algorithm to accelerate the conv ergence of local search. Thirdly, in the process of global development, IMATSA a dopts Levy flight and the improved Gauss disturbance method to adaptively improv es and coordinates the ability of global development and local search. Then, thi s paper verifies the performance of IMATSA based on 14 benchmark functions exper iment, ablation experiment, parameter optimization experiments of Support Vector Machine (SVM) and Gradient Boosting Decision Tree (GBDT), Wilcoxon signed rank test and image multi-threshold segmentation experiment with the performance metr ics are convergence speed, convergence value, significance level P-value, Peak S ignal-to-Noise Ratio (PSNR) and Standard Deviation (STD)."
GuangxiPeople's Republic of ChinaAsi aArtificial IntelligenceAlgorithmsGuangxi University of Finance & Economics
NETL
NSTL
