A novel improved particle swarm optimization algorithm based on individual difference evolution

Gou, Jianping; Lei, Yu-Xiang; Guo, Wang-Ping; Wang, Cheng; Cai, Yiqiao; Luo, Wei

doi:10.1016/j.asoc.2017.04.025

Cited by 72 publications

(33 citation statements)

References 53 publications

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“…Generally speaking, BPSO is a useful optimization tool and it has been successfully applied for many feature selection tasks. However, BPSO suffers from the premature convergence and slow convergence rate [15][16][17]. Additionally, one of the major drawbacks of BPSO is the setting of the inertia weight [18].…”

Section: Co-evolution Binary Particle Swarm Optimization With Multiplmentioning

confidence: 99%

A New Co-Evolution Binary Particle Swarm Optimization with Multiple Inertia Weight Strategy for Feature Selection

2019

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Feature selection is a task of choosing the best combination of potential features that best describes the target concept during a classification process. However, selecting such relevant features becomes a difficult matter when large number of features are involved. Therefore, this study aims to solve the feature selection problem using binary particle swarm optimization (BPSO). Nevertheless, BPSO has limitations of premature convergence and the setting of inertia weight. Hence, a new co-evolution binary particle swarm optimization with a multiple inertia weight strategy (CBPSO-MIWS) is proposed in this work. The proposed method is validated with ten benchmark datasets from UCI machine learning repository. To examine the effectiveness of proposed method, four recent and popular feature selection methods namely BPSO, genetic algorithm (GA), binary gravitational search algorithm (BGSA) and competitive binary grey wolf optimizer (CBGWO) are used in a performance comparison. Our results show that CBPSO-MIWS can achieve competitive performance in feature selection, which is appropriate for application in engineering, rehabilitation and clinical areas.

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Section: Co-evolution Binary Particle Swarm Optimization With Multiplmentioning

confidence: 99%

A New Co-Evolution Binary Particle Swarm Optimization with Multiple Inertia Weight Strategy for Feature Selection

2019

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“…For each problem, the best solution is a bird in the search space, namely a "particle", and the optimal solution is the "corn field" that the birds are looking for [29]. Each particle has a position vector and a velocity vector, and the adaptive value of the current position can be calculated according to the objective function, which can be understood as the distance from the "corn field".…”

Section: Particle Swarm Optimization Principlementioning

confidence: 99%

A Hybrid Algorithm for Forecasting Financial Time Series Data Based on DBSCAN and SVR

et al. 2019

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Financial prediction is an important research field in financial data time series mining. There has always been a problem of clustering massive financial time series data. Conventional clustering algorithms are not practical for time series data because they are essentially designed for static data. This impracticality results in poor clustering accuracy in several financial forecasting models. In this paper, a new hybrid algorithm is proposed based on Optimization of Initial Points and Variable-Parameter Density-Based Spatial Clustering of Applications with Noise (OVDBCSAN) and support vector regression (SVR). At the initial point of optimization, ε and MinPts, which are global parameters in DBSCAN, mainly deal with datasets of different densities. According to different densities, appropriate parameters are selected for clustering through optimization. This algorithm can find a large number of similar classes and then establish regression prediction models. It was tested extensively using real-world time series datasets from Ping An Bank, the Shanghai Stock Exchange, and the Shenzhen Stock Exchange to evaluate accuracy. The evaluation showed that our approach has major potential in clustering massive financial time series data, therefore improving the accuracy of the prediction of stock prices and financial indexes.

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“…Some hardware options to decrease execution time include FPGA [49,50], GPU [51], faster processors [52] or computer clusters [53,54]. Algorithm optimization examples are found in studies by Gou et al [55], Naderi et al [56] and Sánchez-Oro et al [57].…”

Section: Computer Clustermentioning

confidence: 99%

Parallelism Strategies for Big Data Delayed Transfer Entropy Evaluation

2019

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Generated and collected data have been rising with the popularization of technologies such as Internet of Things, social media, and smartphone, leading big data term creation. One class of big data hidden information is causality. Among the tools to infer causal relationships, there is Delay Transfer Entropy (DTE); however, it has a high demanding processing power. Many approaches were proposed to overcome DTE performance issues such as GPU and FPGA implementations. Our study compared different parallel strategies to calculate DTE from big data series using a heterogeneous Beowulf cluster. Task Parallelism was significantly faster in comparison to Data Parallelism. With big data trend in sight, these results may enable bigger datasets analysis or better statistical evidence.

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A novel improved particle swarm optimization algorithm based on individual difference evolution

Cited by 72 publications

References 53 publications

A New Co-Evolution Binary Particle Swarm Optimization with Multiple Inertia Weight Strategy for Feature Selection

A New Co-Evolution Binary Particle Swarm Optimization with Multiple Inertia Weight Strategy for Feature Selection

A Hybrid Algorithm for Forecasting Financial Time Series Data Based on DBSCAN and SVR

Parallelism Strategies for Big Data Delayed Transfer Entropy Evaluation

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