An enhanced Bacterial Foraging Optimization and its application for training kernel extreme learning machine

Chen, Huiling; Zhang, Qian; Luo, Jie; Xu, Yanmei; Zhang, Xiaoqin

doi:10.1016/j.asoc.2019.105884

Cited by 234 publications

(90 citation statements)

References 49 publications

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“…Zhou et al [39] designed a B2C e-commerce management module. In addition, for logistics distribution, many researchers have proposed a lot of optimizations method to obtain the optimal logistics distribution schemes [40][41][42][43][44][45][46][47][48][49][50][51][52][53].…”

Section: Introductionmentioning

confidence: 99%

Innovation Mode and Optimization Strategy of B2C E-Commerce Logistics Distribution under Big Data

Ying-yan

Zhou

2020

Sustainability

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Section: Introductionmentioning

confidence: 99%

Innovation Mode and Optimization Strategy of B2C E-Commerce Logistics Distribution under Big Data

Ying-yan

Zhou

2020

Sustainability

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“…The slime mould algorithm (SMA) is a novel heuristic algorithm which was presented by Li et al [13] in 2020. It is similar to the bacterial foraging optimization algorithm [14]- [16]. However, SMA involves a distinct mathematical model, using accommodative weights to imitate the generation process of positive and negative feedback of a slime mould propagation wave based on a bio-oscillator, forming the optimal path of food connection with good exploration capacity and development tendency.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Parameter Adaptive Support Vector Regression Using K-Means Clustering and Chaotic Slime Mould Algorithm

Chen

Liu

2020

IEEE Access

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Support vector regression (SVR) performs satisfactorily in prediction problems, especially for small sample prediction. The setting parameters (e.g., kernel type and penalty factor) profoundly impact the performance and efficiency of SVR. The adaptive adjustment of the parameters has always been a research hotspot. However, the substantial time cost and forecast accuracy of parameter adjustment are challenging to many scholars. The contradiction of big data prediction is especially prominent. In the paper, an SVR-based prediction approach is presented using the K-means clustering method (KMCM) and chaotic slime mould algorithm (CSMA). Eight high-and low-dimensional benchmark datasets are applied to obtain appropriate key parameters of KMCM and CSMA, and the forecast accuracy, stability performance and computation complexity are evaluated. The proposed approach obtains the optimal (joint best) forecast accuracy on 6 datasets and produces the most stable output on 3 datasets. It ranks first with a score of 0.024 in the overall evaluation. The outcomes reveal that the proposed approach is capable of tuning the parameters of SVR. KMCM, CSMA and SVR are skillfully integrated in this work and perform well. Although the performance is not outstanding in terms of stability, the proposed approach exhibits very strong performance with respect to prediction accuracy and computation complexity. This work validated the tremendous potential of the proposed approach in the prediction field. INDEX TERMS Machine learning, forecasting problem, K-means clustering method, chaotic slime mould algorithm, support vector regression.

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“…Furthermore, a rapid development of advanced swarm intelligence optimization technology in recent years enables great opportunities to solve the energy conservation problems in rail transport. The Bacterial Foraging Optimization (BFO) algorithm is a new swarm intelligence optimization technique that possesses a series of advantages including insensitivity to initial values and parameter selection, strong robustness, simplicity, ease of implementation, parallel processing and global search [31]. BFO has been applied in a wide range of optimization problems such as the energy forecasting [32], expert energy management considering the uncertainty [33], the imbalanced data classification [34], and robotic cell scheduling [35].…”

Section: Introductionmentioning

confidence: 99%

Boundary Identification for Traction Energy Conservation Capability of Urban Rail Timetables: A Case Study of the Beijing Batong Line

Liu

Cai

et al. 2020

Energies

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Energy conservation is attracting more attention to achieve a reduced lifecycle system cost level while enabling environmentally friendly characteristics. Conventional research mainly concentrates on energy-saving speed profiles, where the energy level evaluation of the timetable is usually considered separately. This paper integrates the train driving control optimization and the timetable characteristics by analyzing the achievable tractive energy conservation performance and the corresponding boundaries. A calculation method for energy efficient driving control solution is proposed based on the Bacterial Foraging Optimization (BFO) strategy, which is utilized to carry out batch processing with timetable. A boundary identification solution is proposed to detect the range of energy conservation capability by considering the relationships with average interstation speed and the passenger volume condition. A case study is presented using practical data of Beijing Metro Batong Line and two timetable schemes. The results illustrate that the proposed optimized energy efficient driving control approach is capable of saving tractive energy in comparison with the conventional traction calculation-based train operation solution. With the proposed boundary identification method, the capability space of the energy conservation profiles with respect to the energy reduction and energy saving rate is revealed. Moreover, analyses and discussions on effects from different passenger load conditions are given to both the weekday and weekend timetables. Results of this paper may assist the decision making of rail operators and engineers by enhancing the cost effectiveness and energy efficiency.

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An enhanced Bacterial Foraging Optimization and its application for training kernel extreme learning machine

Cited by 234 publications

References 49 publications

Innovation Mode and Optimization Strategy of B2C E-Commerce Logistics Distribution under Big Data

Innovation Mode and Optimization Strategy of B2C E-Commerce Logistics Distribution under Big Data

An Efficient Parameter Adaptive Support Vector Regression Using K-Means Clustering and Chaotic Slime Mould Algorithm

Boundary Identification for Traction Energy Conservation Capability of Urban Rail Timetables: A Case Study of the Beijing Batong Line

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