An Improved PSO for Multimodal Complex Problem

Liu, Yanmin; Zhang, Zhuanzhou; Luo, Yuanfeng; Wu, Xiangbiao

doi:10.1007/978-3-319-09330-7_44

Cited by 2 publications

(1 citation statement)

References 11 publications

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“…Beheshti and Shamsuddin [28] proposed a method to aggregate weights in order to solve multi-objective optimization problem. Liu et al [29] advanced a novel PSO based on adaptive rejection factor (ARFPSO).…”

Section: Related Workmentioning

confidence: 99%

Generalized Pareto Model Based on Particle Swarm Optimization for Anomaly Detection

Huang

Chen

et al. 2019

IEEE Access

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Anomaly detection of time series has been widely used in various fields. Most detection methods depend either on assumptions about data distribution or manual threshold setting. If the assumption is incorrect, the effectiveness of detection technology will be greatly reduced. To deal with this problem, we propose a maximum likelihood estimation method based on particle swarm optimization for generalized Pareto model to detect outliers of time series, which can be called Generalized Pareto Model Based on Particle Swarm Optimization (GPMPSO). Because the generalized Pareto model is multidimensional, we introduce a comprehensive learning strategy to improve search ability of particle swarm algorithm. Due to the multiple peaks of the log-likelihood function of generalized Pareto model, we apply dynamic neighbors to reduce the possibility of particle swarm optimization falling into local optimum. Moreover, we propose a new processing model Big Drift Streaming Peak Over Threshold (BDSPOT) to enhance the capability of the data stream processor. Our algorithm is tested on various real-world datasets which demonstrate its very competitive performance. INDEX TERMSAnomaly detection, generalized pareto distribution, particle swarm optimization, time series. JIAN CHEN received the Ph.D. degree from Zhejiang University, Hangzhou, China. He is currently a Research Professor with the Third Institute of Oceanography, Ministry of Natural Resources, China. He have undertaken many marine geology investigation projects, such as Multiple Dis-

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Section: Related Workmentioning

confidence: 99%