Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.5
4.5
Journals
Biomimetics
Special Issues
Biomimicry for Optimization, Control, and Automation
share announcement

Biomimicry for Optimization, Control, and Automation

A special issue of Biomimetics (ISSN 2313-7673). This special issue belongs to the section "Biological Optimisation and Management".

Deadline for manuscript submissions: closed (25 October 2023) | Viewed by 15476

Special Issue Editors

Prof. Dr. Yongquan Zhou

E-Mail Website
Guest Editor
School of Artificial Intelligence, Guangxi University for Nationalities, Nanning 530006, China
Interests: bio-inspired computing; bionic optimization; computation intelligence; intelligence optimization; neural network
Special Issues, Collections and Topics in MDPI journals
Dr. Huajuan Huang

E-Mail Website
Guest Editor
College of Artificial Intelligence, Guangxi University for Nationalities, Nanning 530006, China
Interests: bionic optimization; intelligence optimization; machine learning
Special Issues, Collections and Topics in MDPI journals
Dr. Guo Zhou

E-Mail Website
Guest Editor
Department of Science and Technology Teaching, China University of Political Science and Law, Beijing 102249, China
Interests: bionic optimization; intelligence optimization; graphical visualization
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Bionic optimization is a relatively cutting-edge research direction in the field of intelligence optimization. There are many highly effective optimization, feedback control, and automation systems embedded in living organisms and nature. Evolution persistently seeks optimal robust designs for biological feedback control systems and decision-making processes. The advantages of intelligence optimization, such as global search and efficient parallelism, provide new ideas and means for solving complex control and automation optimization problems.

This Special Issue aims to collect the latest results regarding biomimicry for optimization, control, and automation applications. To this end, we encourage submissions of meta-heuristic theoretical algorithm papers and reviews, as well as experimental studies dealing with relevant questions in bionic optimization fields.

Prof. Dr. Yongquan Zhou
Dr. Huajuan Huang
Dr. Guo Zhou
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Biomimetics is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2200 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • meta-heuristic；bio-inspired computing
  • bionic optimization
  • computation intelligence
  • intelligence control
  • intelligence design
  • automatic assembly

Published Papers (11 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

22 pages, 3714 KiB  
Article
Trajectory Tracking and Obstacle Avoidance of Robotic Fish Based on Nonlinear Model Predictive Control
by Ruilong Wang, Ming Wang, Yiyang Zhang, Qianchuan Zhao, Xuehan Zheng and He Gao
Biomimetics 2023, 8(7), 529; https://0-doi-org.brum.beds.ac.uk/10.3390/biomimetics8070529 - 6 Nov 2023
Cited by 1 | Viewed by 1147
Abstract
The attainment of accurate motion control for robotic fish inside intricate underwater environments continues to be a substantial obstacle within the realm of underwater robotics. This paper presents a proposed algorithm for trajectory tracking and obstacle avoidance planning in robotic fish, utilizing nonlinear [...] Read more.
The attainment of accurate motion control for robotic fish inside intricate underwater environments continues to be a substantial obstacle within the realm of underwater robotics. This paper presents a proposed algorithm for trajectory tracking and obstacle avoidance planning in robotic fish, utilizing nonlinear model predictive control (NMPC). This methodology facilitates the implementation of optimization-based control in real-time, utilizing the present state and environmental data to effectively regulate the movements of the robotic fish with a high degree of agility. To begin with, a dynamic model of the robotic fish, incorporating accelerations, is formulated inside the framework of the world coordinate system. The last step involves providing a detailed explanation of the NMPC algorithm and developing obstacle avoidance and objective functions for the fish in water. This will enable the design of an NMPC controller that incorporates control restrictions. In order to assess the efficacy of the proposed approach, a comparative analysis is conducted between the NMPC algorithm and the pure pursuit (PP) algorithm in terms of trajectory tracking. This comparison serves to affirm the accuracy of the NMPC algorithm in effectively tracking trajectories. Moreover, a comparative analysis between the NMPC algorithm and the dynamic window approach (DWA) method in the context of obstacle avoidance planning highlights the superior resilience of the NMPC algorithm in this domain. The proposed strategy, which utilizes NMPC, demonstrates a viable alternative for achieving precise trajectory tracking and efficient obstacle avoidance planning in the context of robotic fish motion control within intricate surroundings. This method exhibits considerable potential for practical implementation and future application. Full article
(This article belongs to the Special Issue Biomimicry for Optimization, Control, and Automation)
►▼ Show Figures

Figure 1

45 pages, 4826 KiB  
Article
Optimizing Image Classification: Automated Deep Learning Architecture Crafting with Network and Learning Hyperparameter Tuning
by Koon Meng Ang, Wei Hong Lim, Sew Sun Tiang, Abhishek Sharma, Marwa M. Eid, Sayed M. Tawfeek, Doaa Sami Khafaga, Amal H. Alharbi and Abdelaziz A. Abdelhamid
Biomimetics 2023, 8(7), 525; https://0-doi-org.brum.beds.ac.uk/10.3390/biomimetics8070525 - 4 Nov 2023
Viewed by 1790
Abstract
This study introduces ETLBOCBL-CNN, an automated approach for optimizing convolutional neural network (CNN) architectures to address classification tasks of varying complexities. ETLBOCBL-CNN employs an effective encoding scheme to optimize network and learning hyperparameters, enabling the discovery of innovative CNN structures. To enhance the [...] Read more.
This study introduces ETLBOCBL-CNN, an automated approach for optimizing convolutional neural network (CNN) architectures to address classification tasks of varying complexities. ETLBOCBL-CNN employs an effective encoding scheme to optimize network and learning hyperparameters, enabling the discovery of innovative CNN structures. To enhance the search process, it incorporates a competency-based learning concept inspired by mixed-ability classrooms during the teacher phase. This categorizes learners into competency-based groups, guiding each learner’s search process by utilizing the knowledge of the predominant peers, the teacher solution, and the population mean. This approach fosters diversity within the population and promotes the discovery of innovative network architectures. During the learner phase, ETLBOCBL-CNN integrates a stochastic peer interaction scheme that encourages collaborative learning among learners, enhancing the optimization of CNN architectures. To preserve valuable network information and promote long-term population quality improvement, ETLBOCBL-CNN introduces a tri-criterion selection scheme that considers fitness, diversity, and learners’ improvement rates. The performance of ETLBOCBL-CNN is evaluated on nine different image datasets and compared to state-of-the-art methods. Notably, ELTLBOCBL-CNN achieves outstanding accuracies on various datasets, including MNIST (99.72%), MNIST-RD (96.67%), MNIST-RB (98.28%), MNIST-BI (97.22%), MNST-RD + BI (83.45%), Rectangles (99.99%), Rectangles-I (97.41%), Convex (98.35%), and MNIST-Fashion (93.70%). These results highlight the remarkable classification accuracy of ETLBOCBL-CNN, underscoring its potential for advancing smart device infrastructure development. Full article
(This article belongs to the Special Issue Biomimicry for Optimization, Control, and Automation)
►▼ Show Figures

Figure 1

44 pages, 2459 KiB  
Article
Percentile-Based Adaptive Immune Plasma Algorithm and Its Application to Engineering Optimization
by Selcuk Aslan, Sercan Demirci, Tugrul Oktay and Erdal Yesilbas
Biomimetics 2023, 8(6), 486; https://0-doi-org.brum.beds.ac.uk/10.3390/biomimetics8060486 - 14 Oct 2023
Viewed by 1240
Abstract
The immune plasma algorithm (IP algorithm or IPA) is one of the most recent meta-heuristic techniques and models the fundamental steps of immune or convalescent plasma treatment, attracting researchers’ attention once more with the COVID-19 pandemic. The IP algorithm determines the number of [...] Read more.
The immune plasma algorithm (IP algorithm or IPA) is one of the most recent meta-heuristic techniques and models the fundamental steps of immune or convalescent plasma treatment, attracting researchers’ attention once more with the COVID-19 pandemic. The IP algorithm determines the number of donors and the number of receivers when two specific control parameters are initialized and protects their values until the end of termination. However, determining which values are appropriate for the control parameters by adjusting the number of donors and receivers and guessing how they interact with each other are difficult tasks. In this study, we attempted to determine the number of plasma donors and receivers with an improved mechanism that depended on dividing the whole population into two sub-populations using a statistical measure known as the percentile and then a novel variant of the IPA called the percentile IPA (pIPA) was introduced. To investigate the performance of the pIPA, 22 numerical benchmark problems were solved by assigning different values to the control parameters of the algorithm. Moreover, two complex engineering problems, one of which required the filtering of noise from the recorded signal and the other the path planning of an unmanned aerial vehicle, were solved by the pIPA. Experimental studies showed that the percentile-based donor–receiver selection mechanism significantly contributed to the solving capabilities of the pIPA and helped it outperform well-known and state-of-art meta-heuristic algorithms. Full article
(This article belongs to the Special Issue Biomimicry for Optimization, Control, and Automation)
►▼ Show Figures

Figure 1

15 pages, 2636 KiB  
Article
A Path-Planning Method Based on Improved Soft Actor-Critic Algorithm for Mobile Robots
by Tinglong Zhao, Ming Wang, Qianchuan Zhao, Xuehan Zheng and He Gao
Biomimetics 2023, 8(6), 481; https://0-doi-org.brum.beds.ac.uk/10.3390/biomimetics8060481 - 10 Oct 2023
Cited by 1 | Viewed by 1608
Abstract
The path planning problem has gained more attention due to the gradual popularization of mobile robots. The utilization of reinforcement learning techniques facilitates the ability of mobile robots to successfully navigate through an environment containing obstacles and effectively plan their path. This is [...] Read more.
The path planning problem has gained more attention due to the gradual popularization of mobile robots. The utilization of reinforcement learning techniques facilitates the ability of mobile robots to successfully navigate through an environment containing obstacles and effectively plan their path. This is achieved by the robots’ interaction with the environment, even in situations when the environment is unfamiliar. Consequently, we provide a refined deep reinforcement learning algorithm that builds upon the soft actor-critic (SAC) algorithm, incorporating the concept of maximum entropy for the purpose of path planning. The objective of this strategy is to mitigate the constraints inherent in conventional reinforcement learning, enhance the efficacy of the learning process, and accommodate intricate situations. In the context of reinforcement learning, two significant issues arise: inadequate incentives and inefficient sample use during the training phase. To address these challenges, the hindsight experience replay (HER) mechanism has been presented as a potential solution. The HER mechanism aims to enhance algorithm performance by effectively reusing past experiences. Through the utilization of simulation studies, it can be demonstrated that the enhanced algorithm exhibits superior performance in comparison with the pre-existing method. Full article
(This article belongs to the Special Issue Biomimicry for Optimization, Control, and Automation)
►▼ Show Figures

Figure 1

27 pages, 3272 KiB  
Article
Application of Bidirectional Long Short-Term Memory to Adaptive Streaming for Internet of Autonomous Vehicles
by Chenn-Jung Huang, Kai-Wen Hu and Hao-Wen Cheng
Biomimetics 2023, 8(6), 467; https://0-doi-org.brum.beds.ac.uk/10.3390/biomimetics8060467 - 1 Oct 2023
Viewed by 926
Abstract
It is expected that interconnected networks of autonomous vehicles, especially during peak traffic, will face congestion challenges. Moreover, the existing literature lacks discussions on integrating next-generation wireless communication technologies into connected vehicular networks. Hence, this paper introduces a tailored bandwidth management algorithm for [...] Read more.
It is expected that interconnected networks of autonomous vehicles, especially during peak traffic, will face congestion challenges. Moreover, the existing literature lacks discussions on integrating next-generation wireless communication technologies into connected vehicular networks. Hence, this paper introduces a tailored bandwidth management algorithm for streaming applications of autonomous vehicle passengers. It leverages cutting-edge 6G wireless technology to create a network with high-speed transmission and broad coverage, ensuring smooth streaming application performance. The key features of bandwidth allocation for diverse streaming applications in this work include bandwidth relay and pre-loading of video clips assisted by vehicle-to-vehicle communication. Through simulations, this research effectively showcases the algorithm’s ability to fulfill the bandwidth needs of diverse streaming applications for autonomous vehicle passengers. Specifically, during periods of peak user bandwidth demand, it notably increases the bandwidth accessible for streaming applications. On average, users experience a substantial 55% improvement in the bandwidth they can access. This validation affirms the viability and promise of the proposed approach in efficiently managing the intricate complexities of bandwidth allocation issues for streaming services within the connected autonomous vehicular networks. Full article
(This article belongs to the Special Issue Biomimicry for Optimization, Control, and Automation)
►▼ Show Figures

Figure 1

27 pages, 7979 KiB  
Article
Teaching–Learning Optimization Algorithm Based on the Cadre–Mass Relationship with Tutor Mechanism for Solving Complex Optimization Problems
by Xiao Wu, Shaobo Li, Fengbin Wu and Xinghe Jiang
Biomimetics 2023, 8(6), 462; https://0-doi-org.brum.beds.ac.uk/10.3390/biomimetics8060462 - 1 Oct 2023
Cited by 2 | Viewed by 1285
Abstract
The teaching–learning-based optimization (TLBO) algorithm, which has gained popularity among scholars for addressing practical issues, suffers from several drawbacks including slow convergence speed, susceptibility to local optima, and suboptimal performance. To overcome these limitations, this paper presents a novel algorithm called the teaching–learning [...] Read more.
The teaching–learning-based optimization (TLBO) algorithm, which has gained popularity among scholars for addressing practical issues, suffers from several drawbacks including slow convergence speed, susceptibility to local optima, and suboptimal performance. To overcome these limitations, this paper presents a novel algorithm called the teaching–learning optimization algorithm, based on the cadre–mass relationship with the tutor mechanism (TLOCTO). Building upon the original teaching foundation, this algorithm incorporates the characteristics of class cadre settings and extracurricular learning institutions. It proposes a new learner strategy, cadre–mass relationship strategy, and tutor mechanism. The experimental results on 23 test functions and CEC-2020 benchmark functions demonstrate that the enhanced algorithm exhibits strong competitiveness in terms of convergence speed, solution accuracy, and robustness. Additionally, the superiority of the proposed algorithm over other popular optimizers is confirmed through the Wilcoxon signed rank-sum test. Furthermore, the algorithm’s practical applicability is demonstrated by successfully applying it to three complex engineering design problems. Full article
(This article belongs to the Special Issue Biomimicry for Optimization, Control, and Automation)
►▼ Show Figures

Figure 1

19 pages, 4568 KiB  
Article
An Automatic-Segmentation- and Hyper-Parameter-Optimization-Based Artificial Rabbits Algorithm for Leaf Disease Classification
by Ihtiram Raza Khan, M. Siva Sangari, Piyush Kumar Shukla, Aliya Aleryani, Omar Alqahtani, Areej Alasiry and M. Turki-Hadj Alouane
Biomimetics 2023, 8(5), 438; https://0-doi-org.brum.beds.ac.uk/10.3390/biomimetics8050438 - 19 Sep 2023
Cited by 1 | Viewed by 1138
Abstract
In recent years, disease attacks have posed continuous threats to agriculture and caused substantial losses in the economy. Thus, early detection and classification could minimize the spread of disease and help to improve yield. Meanwhile, deep learning has emerged as the significant approach [...] Read more.
In recent years, disease attacks have posed continuous threats to agriculture and caused substantial losses in the economy. Thus, early detection and classification could minimize the spread of disease and help to improve yield. Meanwhile, deep learning has emerged as the significant approach to detecting and classifying images. The classification performed using the deep learning approach mainly relies on large datasets to prevent overfitting problems. The Automatic Segmentation and Hyper Parameter Optimization Artificial Rabbits Algorithm (AS-HPOARA) is developed to overcome the above-stated issues. It aims to improve plant leaf disease classification. The Plant Village dataset is used to assess the proposed AS-HPOARA approach. Z-score normalization is performed to normalize the images using the dataset’s mean and standard deviation. Three augmentation techniques are used in this work to balance the training images: rotation, scaling, and translation. Before classification, image augmentation reduces overfitting problems and improves the classification accuracy. Modified UNet employs a more significant number of fully connected layers to better represent deeply buried characteristics; it is considered for segmentation. To convert the images from one domain to another in a paired manner, the classification is performed by HPO-based ARA, where the training data get increased and the statistical bias is eliminated to improve the classification accuracy. The model complexity is minimized by tuning the hyperparameters that reduce the overfitting issue. Accuracy, precision, recall, and F1 score are utilized to analyze AS-HPOARA’s performance. Compared to the existing CGAN-DenseNet121 and RAHC_GAN, the reported results show that the accuracy of AS-HPOARA for ten classes is high at 99.7%. Full article
(This article belongs to the Special Issue Biomimicry for Optimization, Control, and Automation)
►▼ Show Figures

Figure 1

20 pages, 4463 KiB  
Article
MPPT of PEM Fuel Cell Using PI-PD Controller Based on Golden Jackal Optimization Algorithm
by Ahmed M. Agwa, Tarek I. Alanazi, Habib Kraiem, Ezzeddine Touti, Abdulaziz Alanazi and Dhari K. Alanazi
Biomimetics 2023, 8(5), 426; https://0-doi-org.brum.beds.ac.uk/10.3390/biomimetics8050426 - 14 Sep 2023
Cited by 5 | Viewed by 1285
Abstract
Subversive environmental impacts and limited amounts of conventional forms of energy necessitate the utilization of renewable energies (REs). Unfortunately, REs such as solar and wind energies are intermittent, so they should be stored in other forms to be used during their absence. One [...] Read more.
Subversive environmental impacts and limited amounts of conventional forms of energy necessitate the utilization of renewable energies (REs). Unfortunately, REs such as solar and wind energies are intermittent, so they should be stored in other forms to be used during their absence. One of the finest storage techniques for REs is based on hydrogen generation via an electrolyzer during abundance, then electricity generation by fuel cell (FC) during their absence. With reference to the advantages of the proton exchange membrane fuel cell (PEM-FC), this is preferred over other kinds of FCs. The output power of the PEM-FC is not constant, since it depends on hydrogen pressure, cell temperature, and electric load. Therefore, a maximum power point tracking (MPPT) system should be utilized with PEM-FC. The techniques previously utilized have some disadvantages, such as slowness of response and largeness of each oscillation, overshoot and undershoot, so this article addresses an innovative MPPT for PEM-FC using a consecutive controller made up of proportional-integral (PI) and proportional-derivative (PD) controllers whose gains are tuned via the golden jackal optimization algorithm (GJOA). Simulation results when applying the GJOA-PI-PD controller for MPPT of PEM-FC reveal its advantages over other approaches according to quickness of response, smallness of oscillations, and tininess of overshoot and undershoot. The overshoot resulting using the GJOA-PI-PD controller for MPPT of PEM-FC is smaller than that of perturb and observe, GJOA-PID, and GJOA-FOPID controllers by 98.26%, 86.30%, and 89.07%, respectively. Additionally, the fitness function resulting when using the GJOA-PI-PD controller for MPPT of PEM-FC is smaller than that of the aforementioned approaches by 93.95%, 87.17%, and 87.97%, respectively. Full article
(This article belongs to the Special Issue Biomimicry for Optimization, Control, and Automation)
►▼ Show Figures

Figure 1

32 pages, 9386 KiB  
Article
Hybrid Manta Ray Foraging Algorithm with Cuckoo Search for Global Optimization and Three-Dimensional Wireless Sensor Network Deployment Problem
by Meiyan Wang, Qifang Luo, Yuanfei Wei and Yongquan Zhou
Biomimetics 2023, 8(5), 411; https://0-doi-org.brum.beds.ac.uk/10.3390/biomimetics8050411 - 5 Sep 2023
Viewed by 1018
Abstract
In this paper, a new hybrid Manta Ray Foraging Optimization (MRFO) with Cuckoo Search (CS) algorithm (AMRFOCS) is proposed. Firstly, quantum bit Bloch spherical coordinate coding is used for the initialization of the population, which improves the diversity of the expansion of the [...] Read more.
In this paper, a new hybrid Manta Ray Foraging Optimization (MRFO) with Cuckoo Search (CS) algorithm (AMRFOCS) is proposed. Firstly, quantum bit Bloch spherical coordinate coding is used for the initialization of the population, which improves the diversity of the expansion of the traversal ability of the search space. Secondly, the dynamic disturbance factor is introduced to balance the exploratory and exploitative search ability of the algorithm. Finally, the unique nesting strategy of the cuckoo and Levy flight is introduced to enhance the search ability. AMRFOCS is tested on CEC2017 and CEC2020 benchmark functions, which is also compared and tested by using different dimensions and other state-of-the-art metaheuristic algorithms. Experimental results reveal that the AMRFOCS algorithm has a superior convergence rate and optimization precision. At the same time, the nonparametric Wilcoxon signed-rank test and Friedman test show that the AMRFOCS has good stability and superiority. In addition, the proposed AMRFOCS is applied to the three-dimensional WSN coverage problem. Compared with the other four 3D deployment methods optimized by metaheuristic algorithms, the AMRFOCS effectively reduces the redundancy of sensor nodes, possesses a faster convergence speed and higher coverage and then provides a more effective and practical deployment scheme. Full article
(This article belongs to the Special Issue Biomimicry for Optimization, Control, and Automation)
►▼ Show Figures

Figure 1

24 pages, 4441 KiB  
Article
Design of Intelligent Neuro-Supervised Networks for Brain Electrical Activity Rhythms of Parkinson’s Disease Model
by Roshana Mukhtar, Chuan-Yu Chang, Muhammad Asif Zahoor Raja and Naveed Ishtiaq Chaudhary
Biomimetics 2023, 8(3), 322; https://0-doi-org.brum.beds.ac.uk/10.3390/biomimetics8030322 - 21 Jul 2023
Cited by 4 | Viewed by 1092
Abstract
The objective of this paper is to present a novel design of intelligent neuro-supervised networks (INSNs) in order to study the dynamics of a mathematical model for Parkinson’s disease illness (PDI), governed with three differential classes to represent the rhythms of brain electrical [...] Read more.
The objective of this paper is to present a novel design of intelligent neuro-supervised networks (INSNs) in order to study the dynamics of a mathematical model for Parkinson’s disease illness (PDI), governed with three differential classes to represent the rhythms of brain electrical activity measurements at different locations in the cerebral cortex. The proposed INSNs are constructed by exploiting the knacks of multilayer structure neural networks back-propagated with the Levenberg–Marquardt (LM) and Bayesian regularization (BR) optimization approaches. The reference data for the grids of input and the target samples of INSNs were formulated with a reliable numerical solver via the Adams method for sundry scenarios of PDI models by way of variation of sensor locations in order to measure the impact of the rhythms of brain electrical activity. The designed INSNs for both backpropagation procedures were implemented on created datasets segmented arbitrarily into training, testing, and validation samples by optimization of mean squared error based fitness function. Comparison of outcomes on the basis of exhaustive simulations of proposed INSNs via both LM and BR methodologies was conducted with reference solutions of PDI models by means of learning curves on MSE, adaptive control parameters of algorithms, absolute error, histogram error plots, and regression index. The outcomes endorse the efficacy of both INSNs solvers for different scenarios in PDI models, but the accuracy of the BR-based method is relatively superior, albeit at the cost of slightly more computations. Full article
(This article belongs to the Special Issue Biomimicry for Optimization, Control, and Automation)
►▼ Show Figures

Figure 1

16 pages, 2054 KiB  
Article
Deep Learning Combinatorial Models for Intelligent Supply Chain Demand Forecasting
by Xiaoya Ma, Mengxiu Li, Jin Tong and Xiaying Feng
Biomimetics 2023, 8(3), 312; https://0-doi-org.brum.beds.ac.uk/10.3390/biomimetics8030312 - 15 Jul 2023
Cited by 3 | Viewed by 1732
Abstract
Low-carbon and environmentally friendly living boosted the market demand for new energy vehicles and promoted the development of the new energy vehicle industry. Accurate demand forecasting can provide an important decision-making basis for new energy vehicle enterprises, which is beneficial to the development [...] Read more.
Low-carbon and environmentally friendly living boosted the market demand for new energy vehicles and promoted the development of the new energy vehicle industry. Accurate demand forecasting can provide an important decision-making basis for new energy vehicle enterprises, which is beneficial to the development of new energy vehicles. From the perspective of an intelligent supply chain, this study explored the demand forecasting of new energy vehicles, and proposed an innovative SARIMA-LSTM-BP combination model for prediction modeling. The data showed that the RMSE, MSE, and MAE values of the SARIMA-LSTM-BP combination model were 2.757, 7.603, and, 1.912, respectively, all of which are lower values than those of the single models. This study therefore, indicated that, compared with traditional econometric forecasting models and deep learning forecasting models, such as the random forest, support vector regression (SVR), long short-term memory (LSTM), and back propagation neural network (BP) models, the SARIMA-LSTM-BP combination model performed outstandingly with higher accuracy and better forecasting performance. Full article
(This article belongs to the Special Issue Biomimicry for Optimization, Control, and Automation)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-11
Back to TopTop