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Authors: Zhang, Zhaojun | Li, Xuanyu | Luan, Shengyang | Xu, Zhaoxiong

Article Type: Research Article

Abstract: Particle swarm optimization (PSO) as a successful optimization algorithm is widely used in many practical applications due to its advantages in fast convergence speed and convenient implementation. As a population optimization algorithm, the quality of initial population plays an important role in the performance of PSO. However, random initialization is used in population initialization for PSO. Using the solution of the solved problem as prior knowledge will help to improve the quality of the initial population solution. In this paper, we use homotopy analysis method (HAM) to build a bridge between the solved problems and the problems to be solved. …Therefore, an improved PSO framework based on HAM, called HAM-PSO, is proposed. The framework of HAM-PSO includes four main processes. It contains obtaining the prior knowledge, constructing homotopy function, generating initial solution and solving the to be solved by PSO. In fact, the framework does not change the PSO, but replaces the random population initialization. The basic PSO algorithm and three others typical PSO algorithms are used to verify the feasibility and effectiveness of this framework. The experimental results show that the four PSO using this framework are better than those without this framework. Show more

Keywords: Particle swarm optimization, homotopy analysis method, initial population, t-test

DOI: 10.3233/JIFS-200979

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 4301-4315, 2021

Authors: Hao, Lixiao | Wang, Xiaoni | Zhang, Ling | Xing, Yue | Guo, Qihao | Hu, Xiaochen | Mu, Bin | Chen, Yili | Chen, Guanqun | Cao, Jing | Zhi, Xiaodong | Liu, Jiaojiao | Li, Xuanyu | Yang, Liu | Li, Jiachen | Du, Wenying | Sun, Yu | Wang, Ting | Liu, Zhen | Liu, Zheng | Zhao, Xuexue | Li, Hongyan | Yu, Yang | Wang, Xue | Jia, Jianguo | Han, Ying

Article Type: Research Article

Abstract: Background: Substantial studies have reported the prevalence and the affecting factors of subjective cognitive decline (SCD). The complaints screening scale has also been used for probing. However, little is known in China. Objective: To investigate the prevalence and risk factors of SCD, and explore an SCD complaints screening scale in China. Methods: Stratified cluster random sampling was conducted. 2,689 residents aged 60–80 years completed questionnaire 1. 814 residents were included for clinical and neuropsychological evaluations. Two standards were used to make the diagnosis of mild cognitive impairment (MCI) and SCD, and a preliminary screening rate comparison was carried out. Finally, …we assessed the risk factors of SCD and the correlation between the SCD-questionnaire 9 (SCD-Q9) and the Auditory Verbal Learning Test-Long Delay Free Recall (AVLT-LR). Results: 1) Standard 1 (ADNI2): the prevalence of SCD was 18.8% (95% CI = 14.7–22.9%) and zero conformed to six criteria (SCD plus). 2) Standard 2 (Jak/Bondi): the prevalence of SCD was 14.4% (95% CI = 10.7–18.1%). 3) Standard 1 had a relatively higher “false” positive rate, whereas Standard 2 had higher “false” negative rate. 4) Age, low education, fewer close friends, and daily drinking were independent risk factors for SCD progressing to MCI. 5) Total points of SCD-Q9 were negatively correlated to the value of AVLT-LR. Conclusions: The prevalence of SCD is high in the ShunYi District in Beijing, China. Age, low education, less social support, and daily drinking are independent risk factors. The brief SCD-Q9 can be used as a reference. Show more

Keywords: Alzheimer’s disease, prevalence, risk factors, SCD-Q9, subjective cognitive decline

DOI: 10.3233/JAD-170347

Citation: Journal of Alzheimer's Disease, vol. 60, no. 2, pp. 371-388, 2017