A Dual Biogeography-Based Optimization Algorithm for Solving High-Dimensional Global Optimization Problems and Engineering Design Problems

Zhang, Ziyu; Gao, Yuelin; Zuo, Wenlu

doi:10.1109/access.2022.3177218

Cited by 10 publications

(6 citation statements)

References 77 publications

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“…Results show an excellent performance. [35] Evaluation on tasks with unknown optimal solution with higher dimensionality could prove superiority on this method.…”

Section: Optimisation Algorithms Applied To High Scale Tasksmentioning

confidence: 99%

Demanded Scale for Modern Numerical Optimisation

Penev

2023

Lecture Notes in Computer Science

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Modern online services facilitate many aspects of the human life such as goods and food shopping and delivery. The number of requested services is growing fast and at the same time can be seen growth of unattended constraints, unforeseen limitations and accidental events which affect the quality and feasibility of the service. Efficient management of online delivery requires optimisation at appropriate scale. This article discusses examples of online delivery services, which require optimization, points out advantages, and limitations, highlights their scale and the role of numerical methods for reducing the cost and time for delivery. Initial investigation illustrates algorithms capabilities to resolve such tasks and discusses current issues. Experimental results on high dimensional tests and consideration of further work conclude the article.

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“…Results show an excellent performance. [35] Evaluation on tasks with unknown optimal solution with higher dimensionality could prove superiority on this method.…”

Section: Optimisation Algorithms Applied To High Scale Tasksmentioning

confidence: 99%

Demanded Scale for Modern Numerical Optimisation

Penev

2023

Lecture Notes in Computer Science

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“…To optimize the architecture of the DCNN, we develop a novel hybrid algorithm that combines artificial bee colony (ABC) [20][21][22], biogeography-based optimization (BBO) [23][24][25][26][27][28], and particle swarm optimization (PSO) techniques. This work proposes a novel approach for improving the detection performance of BC.…”

Section: Paper Contributionsmentioning

confidence: 99%

“…≤ 𝑥 ≤ 2.00 , 0.25 ≤ 𝑥 ≤ 1.30 , 2 ≤ 𝑥 ≤ 15(26) where 𝑥 = wire diameter (d), 𝑥 = mean coil diameter (D), and 𝑥 = the number of active coils (N).…”

mentioning

confidence: 99%

A Novel Hybrid Artificial Bee Colony-Based Deep Convolutional Neural Network to Improve the Detection Performance of Backscatter Communication Systems

Aghakhani,

Larijani,

Sadeghi

et al. 2023

Electronics

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Backscatter communication (BC) is a promising technology for low-power and low-data-rate applications, though the signal detection performance is limited since the backscattered signal is usually much weaker than the original signal. When the detection performance is poor, the backscatter device (BD) may not be able to accurately detect and interpret the incoming signal, leading to errors and degraded communication quality. This can result in data loss, slow data transfer rates, and reduced reliability of the communication link. This paper proposes a novel approach to improve the detection performance of backscatter communication systems using evolutionary deep learning. In particular, we focus on training deep convolutional neural networks (DCNNs) to improve the detection performance of BC. We first develop a novel hybrid algorithm based on artificial bee colony (ABC), biogeography-based optimization (BBO), and particle swarm optimization (PSO) to optimize the architecture of the DCNN, followed by training using a large set of benchmark datasets. To develop the hybrid ABC, the migration operator of the BBO is used to improve the exploitation. Moving towards the global best of PSO is also proposed to improve the exploration of the ABC. Then, we take advantage of the proposed deep architecture to improve the bit-error rate (BER) performance of the studied BC system. The simulation results demonstrate that the proposed algorithm has the best performance in training the benchmark datasets. The results also show that the proposed approach significantly improves the detection performance of backscattered signals compared to existing works.

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“…The algorithm is uncomplicated in principle, easy to implement, contains fewer parameters, and has good global search capability and robustness. It is extensively applied in engineering [20,31,32], data analysis [33], and image processing [34]. The BBO algorithm mainly realizes the exchange of information through species migration and mutation between habitats.…”

Section: Bbo Algorithmmentioning

confidence: 99%

State of Charge Estimation for Power Battery Using Improved Extended Kalman Filter Method Based on Neural Network

Liu,

Zhang

2023

Applied Sciences

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In order to enhance the accuracy of the traditional extended Kalman filter (EKF) algorithm in the estimation of the state of charge (SoC) of power batteries, we first derived the state space equation and measurement equation of lithium power batteries based on the Thevenin battery model and the modified Ampere-Hour integralalgorithm. Then, the basic principles of EKF, backpropagation neural networks (BPNNs), and a biogeography-based optimization (BBO) algorithm were analyzed, and the arc curve mobility model was used to improve the global search ability of the BBO algorithm. By combining these three algorithms, this paper proposes a BP neural network method based on the BBO algorithm. This method uses the BBO algorithm to optimize the incipient weight and threshold of the BP neural network and uses this improved neural network to modify the estimated value of the extended Kalman filter algorithm (BBOBP-EKF). Finally, the BBOBP-EKF algorithm, the extended Kalman filter algorithm based on the BP neural network (BP-EKF), and the EKF algorithm are used to estimate the error value of the SOC of a power battery, and according to the experimental data, it was confirmed that the proposed BBOBP-EKF algorithm has been improved compared to other algorithms with respect to each error index term, in which the maximum error is 1% less than that of the BP-EKF algorithm and 2.4% less than that of the EKF algorithm, the minimum error is also the smallest, and the estimation accuracy is improved compared to the traditional algorithms.

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A Dual Biogeography-Based Optimization Algorithm for Solving High-Dimensional Global Optimization Problems and Engineering Design Problems

Cited by 10 publications

References 77 publications

Demanded Scale for Modern Numerical Optimisation

Demanded Scale for Modern Numerical Optimisation

A Novel Hybrid Artificial Bee Colony-Based Deep Convolutional Neural Network to Improve the Detection Performance of Backscatter Communication Systems

State of Charge Estimation for Power Battery Using Improved Extended Kalman Filter Method Based on Neural Network

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