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A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Computational and Applied Mathematics".

Deadline for manuscript submissions: closed (20 April 2023) | Viewed by 24460

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Prof. Dr. Xinchao Zhao

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School of Science, Beijing University of Posts and Telecommunications, Beijing 100876, China
Interests: swarm intelligence; operations research
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Xingquan Zuo

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School of Computer Science, Beijing University of Posts and Telecommunications, Beijing 100876, China
Interests: intelligent optimization; data mining; artificial intelligence; intelligent transportation systems
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Yinan Guo

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School of Information and Control Engineering, China University of Mining and Technology, Beijing, China
Interests: wireless sensor network; dynamic optimization; evolutionary optimization
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Prof. Dr. Kunpeng Kang

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School of Information Technology, Shangqiu Normal University, Shangqiu 476000, China
Interests: intelligent algorithm; cloud computing

Special Issue Information

Dear Colleagues,

In the last few decades, the role of artificial intelligence and natural computing has aroused more and more attentations, and also been successfully applied to engineering, economics, scheduling and so on.

This Special Issue will present recent research results in Artificial Intelligence and Natural Computing: Theory, Methodology and Applications. Natural Computing is a family of biologically inspired population-based algorithms for global and combinatorial optimization. It is also one of the most important tools for providing an effective and efficient solution to many real-word engineering problems due to their flexibility and capability to solve multimodal problems.

This Special Issue is focused on the artificial intelligence and natural computing: theory, methodology and applications. Potential intelligence, optimization and application topics include but are not limited to the following:

    Foundation of artificial intelligence;
    Natural computing;
    Evolutionary computation and neural network;
    Evolutionary combinatorial optimization and metaheuristics;
    Evolving hardware and developmental systems;
    Evolutionary learning and deep learning;
    Surrogate model for optimization;

and applications on:
    Multimodal optimization;
    Constrained optimization;
    Multiple objective optimization;
    Industrial engineering;
    Portfolio optimization;
    Engineering optimization;
    Information and cyberspace security;
    Cloud computing;
    Layout and route optimization;
    Complex network;
    Communication network;
    Search-based software engineering.

Prof. Dr. Xinchao Zhao
Prof. Dr. Xingquan Zuo
Prof. Dr. Yinan Guo
Prof. Dr. Kunpeng Kang
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. Mathematics is an international peer-reviewed open access semimonthly 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 2600 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

foundation of artificial intelligence
natural computing
evolutionary computation and neural network
evolutionary combinatorial optimization and metaheuristics
evolving hardware and developmental systems
evolutionary learning and deep learning
surrogate model for optimization
multimodal optimization
constrained optimization
multiple objective optimization
industrial engineering
portfolio optimization
engineering optimization
information and cyberspace security
cloud computing
layout and route optimization
complex network
communication network
search-based software engineering

Published Papers (13 papers)

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Research

43 pages, 19537 KiB

Open AccessArticle

CTOA: Toward a Chaotic-Based Tumbleweed Optimization Algorithm

by Tsu-Yang Wu, Ankang Shao and Jeng-Shyang Pan

Mathematics 2023, 11(10), 2339; https://doi.org/10.3390/math11102339 - 17 May 2023

Cited by 29 | Viewed by 1057

Abstract

Metaheuristic algorithms are an important area of research in artificial intelligence. The tumbleweed optimization algorithm (TOA) is the newest metaheuristic optimization algorithm that mimics the growth and reproduction of tumbleweeds. In practice, chaotic maps have proven to be an improved method of optimization algorithms, allowing the algorithm to jump out of the local optimum, maintain population diversity, and improve global search ability. This paper presents a chaotic-based tumbleweed optimization algorithm (CTOA) that incorporates chaotic maps into the optimization process of the TOA. By using 12 common chaotic maps, the proposed CTOA aims to improve population diversity and global exploration and to prevent the algorithm from falling into local optima. The performance of CTOA is tested using 28 benchmark functions from CEC2013, and the results show that the circle map is the most effective in improving the accuracy and convergence speed of CTOA, especially in 50D. Full article

(This article belongs to the Special Issue Artificial Intelligence and Natural Computing: Theory, Methodology and Applications)

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24 pages, 5739 KiB

Open AccessArticle

An Efficient Metaheuristic Algorithm for Job Shop Scheduling in a Dynamic Environment

by Hankun Zhang, Borut Buchmeister, Xueyan Li and Robert Ojstersek

Mathematics 2023, 11(10), 2336; https://doi.org/10.3390/math11102336 - 17 May 2023

Cited by 1 | Viewed by 876

Abstract

This paper proposes an Improved Multi-phase Particle Swarm Optimization (IMPPSO) to solve a Dynamic Job Shop Scheduling Problem (DJSSP) known as an non-deterministic polynomial-time hard (NP-hard) problem. A cellular neighbor network, a velocity reinitialization strategy, a randomly select sub-dimension strategy, and a constraint handling function are introduced in the IMPPSO. The IMPPSO is used to solve the Kundakcı and Kulak problem set and is compared with the original Multi-phase Particle Swarm Optimization (MPPSO) and Heuristic Kalman Algorithm (HKA). The results show that the IMPPSO has better global exploration capability and convergence. The IMPPSO has improved fitness for most of the benchmark instances of the Kundakcı and Kulak problem set, with an average improvement rate of 5.16% compared to the Genetic Algorithm-Mixed (GAM) and of 0.74% compared to HKA. The performance of the IMPPSO for solving real-world problems is verified by a case study. The high level of operational efficiency is also evaluated and demonstrated by proposing a simulation model capable of using the decision-making algorithm in a real-world environment. Full article

(This article belongs to the Special Issue Artificial Intelligence and Natural Computing: Theory, Methodology and Applications)

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15 pages, 2675 KiB

Open AccessArticle

Attention-Based Residual Dilated Network for Traffic Accident Prediction

by Ke Zhang and Yaming Guo

Mathematics 2023, 11(9), 2011; https://doi.org/10.3390/math11092011 - 24 Apr 2023

Viewed by 1163

Abstract

Traffic accidents directly influence public safety and economic development; thus, the prevention of traffic accidents is of great importance in urban transportation. The accurate prediction of traffic accidents can assist traffic departments to better control and prevent accidents. Thus, this paper proposes a deep learning method named attention-based residual dilated network (ARDN), to extract essential information from multi-source datasets and enhance accident prediction accuracy. The method utilizes bidirectional long short-term memory to model sequential information and incorporates an attention mechanism to recalibrate weights. Furthermore, a dilated residual layer is adopted to capture long term information effectively. Feature encoding is also employed to incorporate natural language descriptions and point-of-interest data. Experimental evaluations of datasets collected from Austin and Houston demonstrate that ARDN outperforms a range of machine learning methods, such as logistic regression, gradient boosting, Xgboost, and deep learning methods. The ablation experiments further confirm the indispensability of each component in the proposed method. Full article

(This article belongs to the Special Issue Artificial Intelligence and Natural Computing: Theory, Methodology and Applications)

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13 pages, 3551 KiB

Open AccessArticle

A Cross-Modal Feature Fusion Model Based on ConvNeXt for RGB-D Semantic Segmentation

by Xiaojiang Tang, Baoxia Li, Junwei Guo, Wenzhuo Chen, Dan Zhang and Feng Huang

Mathematics 2023, 11(8), 1828; https://doi.org/10.3390/math11081828 - 12 Apr 2023

Cited by 1 | Viewed by 1792

Abstract

Semantic segmentation, as the pixel level classification with dividing an image into multiple blocks based on the similarities and differences of categories (i.e., assigning each pixel in the image to a class label), is an important task in computer vision. Combining RGB and Depth information can improve the performance of semantic segmentation. However, there is still a problem of the way to deeply integrate RGB and Depth. In this paper, we propose a cross-modal feature fusion RGB-D semantic segmentation model based on ConvNeXt, which uses ConvNeXt as the skeleton network and embeds a cross-modal feature fusion module (CMFFM). The CMFFM designs feature channel-wise and spectral-wise fusion, which can realize the deeply feature fusion of RGB and Depth. The in-depth multi-modal feature fusion in multiple stages improves the performance of the model. Experiments are performed on the public dataset of SUN-RGBD, showing the best segmentation by our proposed model ConvNeXt-CMFFM with the highest mIoU score of 53.5% among the nine comparative models. The outstanding performance of ConvNeXt-CMFFM is also achieved on our self-built dataset of RICE-RGBD with the highest mIoU score and pixel accuracy among the three comparative datasets. The ablation experiment on our rice dataset shows that compared with ConvNeXt (without CMFFM), the mIoU score of ConvNext-CMFFM is increased from 71.5% to 74.8% and its pixel accuracy is increased from 86.2% to 88.3%, indicating the effectiveness of the added feature fusion module in improving segmentation performance. This study shows the feasibility of the practical application of the proposed model in agriculture. Full article

(This article belongs to the Special Issue Artificial Intelligence and Natural Computing: Theory, Methodology and Applications)

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19 pages, 1232 KiB

Open AccessArticle

Analysis and Forecasting of International Airport Traffic Volume

by Cheng-Hong Yang, Borcy Lee, Pey-Huah Jou, Yu-Fang Chung and Yu-Da Lin

Mathematics 2023, 11(6), 1483; https://doi.org/10.3390/math11061483 - 17 Mar 2023

Viewed by 2322

Abstract

Globalization has resulted in increases in air transportation demand and air passenger traffic. With the increases in air traffic, airports face challenges related to infrastructure, air services, and future development. Air traffic forecasting is essential to ensuring appropriate investment in airports. In this study, we combined fuzzy theory with support vector regression (SVR) to develop a fuzzy SVR (FSVR) model for forecasting international airport traffic. This model was used to predict the air traffic volumes at the world’s 10 busiest airports in terms of air traffic in 2018. The predictions were made for the period from August 2014 to December 2019. For fuzzy time series, the developed FSVR model can consider historical air traffic changes. The FSVR model can suitably divide air traffic changes into appropriate fuzzy sets, generate membership function values, and establish fuzzy relations to produce fuzzy interpolated values with minimal errors. Thus, in the prediction of continuous data, the fuzzy data with the smallest errors can be subjected to SVR to find the optimal hyperplane model with the minimum distance to the appropriate support vector sample points. The performance of the proposed model was compared with those of five other models. Of the compared models, the FSVR model exhibited the lowest mean absolute percentage error (MAPE), mean absolute error, and root mean square error for all types of traffic at all of the airports analyzed; all of the MAPE values were below 2.5. The FSVR model can predict future growth trends in air traffic, air passenger flows, aircraft flows, and logistics. An airport authority can use this model to analyze the existing operational facilities and service capacity, find bottlenecks in airport operations, and create a blueprint for future development. The findings revealed that implementing a hybrid modeling approach, specifically the FSVR model, can significantly enhance the performance of the SVR model. The FSVR model allows airlines to predict traffic growth patterns, identify viable new destinations, optimize their schedules or fleet, make accurate marketing decisions, and plan traffic effectively. The FSVR model can guide the timely construction of appropriate airport facilities with accurate predictions. Rapid, cost-effective, efficient, and balanced transportation planning enables the provision of fast, cost-effective, comfortable, safe, and convenient passenger and cargo services while ensuring the proper planning of the airport’s capacity for land-side transportation connections. Full article

(This article belongs to the Special Issue Artificial Intelligence and Natural Computing: Theory, Methodology and Applications)

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19 pages, 2050 KiB

Open AccessArticle

A Multi-Mechanism Seagull Optimization Algorithm Incorporating Generalized Opposition-Based Nonlinear Boundary Processing

by Xinyu Liu, Guangquan Li and Peng Shao

Mathematics 2022, 10(18), 3295; https://doi.org/10.3390/math10183295 - 11 Sep 2022

Cited by 5 | Viewed by 1579

Abstract

The seagull optimization algorithm (SOA), a well-known illustration of intelligent algorithms, has recently drawn a lot of academic interest. However, it has a variety of issues including slower convergence, poorer search accuracy, the single path for pursuing optimization, and the simple propensity to slip into local optimality. This paper suggests a multi-mechanism seagull optimization algorithm (GEN−SOA) that incorporates the generalized opposition-based, adaptive nonlinear weights, and evolutionary boundary constraints to address these demerits further. These methods are balanced and promoted the population variety and the capability to conduct global and local search. Compared with SOA, PSO, SCA, SSA, and BOA on 12 well-known test functions, the experimental results demonstrate that GEN-SOA has a higher accuracy and faster convergence than the other five algorithms, and it can find the global optimal solution beyond the local optimum. Furthermore, to verify the capability of GEN−SOA to solve practical problems, this paper applied GEN−SOA to solve two standard engineering optimization design problems including a welding optimization and a pressure vessel optimization, and the experimental results showed that it has significant advantages over SOA. Full article

(This article belongs to the Special Issue Artificial Intelligence and Natural Computing: Theory, Methodology and Applications)

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19 pages, 416 KiB

Open AccessArticle

Feed-Forward Neural Networks Training with Hybrid Taguchi Vortex Search Algorithm for Transmission Line Fault Classification

by Melih Coban and Suleyman Sungur Tezcan

Mathematics 2022, 10(18), 3263; https://doi.org/10.3390/math10183263 - 08 Sep 2022

Cited by 2 | Viewed by 1131

Abstract

In this study, the hybrid Taguchi vortex search (HTVS) algorithm, which exhibits a rapid convergence rate and avoids local optima, is employed as a new training algorithm for feed-forward neural networks (FNNs) and its performance was analyzed by comparing it with the vortex search (VS) algorithm, the particle swarm optimization (PSO) algorithm, the gravitational search algorithm (GSA) and the hybrid PSOGSA algorithm. The HTVS-based FNN (FNNHTVS) algorithm was applied to three datasets (iris classification, wine recognition and seed classification) taken from the UCI database (the machine learning repository of the University of California at Irvine) and to the 3-bit parity problem. The obtained statistical results were recorded for comparison. Then, the proposed algorithm was used for fault classification on transmission lines. A dataset was created using 735 kV, 60 Hz, 100 km transmission lines for different fault types, fault locations, fault resistance values and fault inception angles. The FNNHTVS algorithm was applied to this dataset and its performance was tested in comparison with that of other classifiers. The results indicated that the performance of the FNNHTVS algorithm was at least as successful as that of the other comparison algorithms. It has been shown that the FNN model trained with HTVS can be used as a capable alternative algorithm for the solution of classification problems. Full article

(This article belongs to the Special Issue Artificial Intelligence and Natural Computing: Theory, Methodology and Applications)

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17 pages, 558 KiB

Open AccessArticle

Deep-Learning Based Injection Attacks Detection Method for HTTP

by Chunhui Zhao, Shuaijie Si, Tengfei Tu, Yijie Shi and Sujuan Qin

Mathematics 2022, 10(16), 2914; https://doi.org/10.3390/math10162914 - 13 Aug 2022

Cited by 2 | Viewed by 2663

Abstract

In the context of the new era of high digitization and informatization, the emergence of the internet and artificial intelligence technologies has profoundly changed people’s lifestyles. The traditional cyber attack detection has become increasingly weak in the context of the increasingly complex network environment in the new era, and deep learning technology has begun to play a significant role in the field of network security. There are many kinds of attacks against web applications, which are very harmful, including SQL (Structured Query Language) injection, XSS (Cross-Site Scripting), and command injection. Based on the detection of SQL injection and XSS attacks, this paper combines the detection of command injection attacks, which are also very harmful, and proposes a multi-classification detection method for web injection attacks. We extract features in the URL (Uniform Resource Locator) and request body of HTTP (Hyper Text Transfer Protocol) requests and combine deep learning technology to build a multi-classification model for injection attacks. Firstly, aiming at the problem of imbalanced distribution of training samples and low detection accuracy of command injection attack, a sample generation method is proposed. The experimental results show that the proposed method ensures a higher detection rate of command injection attacks and lower false alarms. Secondly, we propose a more expressive feature fusion model, which effectively combines the features extracted by deep learning with the discrete features extracted manually. The experimental results show that the feature fusion model proposed in this work is more effective compared with a single deep learning model. The accuracy of the model is improved by about 1%. Full article

(This article belongs to the Special Issue Artificial Intelligence and Natural Computing: Theory, Methodology and Applications)

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15 pages, 2840 KiB

Open AccessArticle

Robustness Enhancement of Neural Networks via Architecture Search with Multi-Objective Evolutionary Optimization

by Haojie Chen, Hai Huang, Xingquan Zuo and Xinchao Zhao

Mathematics 2022, 10(15), 2724; https://doi.org/10.3390/math10152724 - 02 Aug 2022

Cited by 2 | Viewed by 1302

Abstract

Along with the wide use of deep learning technology, its security issues have drawn much attention over the years. Adversarial examples expose the inherent vulnerability of deep learning models and make it a challenging task to improve their robustness. Model robustness is related not only to its parameters but also to its architecture. This paper proposes a novel robustness enhanced approach for neural networks based on a neural architecture search. First, we randomly sample multiple neural networks to construct the initial population. Second, we utilize the individual networks in the population to fit and update the surrogate models. Third, the population of neural networks is evolved through a multi-objective evolutionary algorithm, where the surrogate models accelerate the performance evaluation of networks. Finally, the second and third steps are performed alternately until a network architecture with high accuracy and robustness is achieved. Experimental results show that the proposed method outperforms some classical artificially designed neural networks and other architecture search algorithms in terms of robustness. Full article

(This article belongs to the Special Issue Artificial Intelligence and Natural Computing: Theory, Methodology and Applications)

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16 pages, 6729 KiB

Open AccessArticle

Deep Adversarial Learning Triplet Similarity Preserving Cross-Modal Retrieval Algorithm

by Guokun Li, Zhen Wang, Shibo Xu, Chuang Feng, Xiaohan Yang, Nannan Wu and Fuzhen Sun

Mathematics 2022, 10(15), 2585; https://doi.org/10.3390/math10152585 - 25 Jul 2022

Viewed by 1474

Abstract

The cross-modal retrieval task can return different modal nearest neighbors, such as image or text. However, inconsistent distribution and diverse representation make it hard to directly measure the similarity relationship between different modal samples, which causes a heterogeneity gap. To bridge the above-mentioned gap, we propose the deep adversarial learning triplet similarity preserving cross-modal retrieval algorithm to map different modal samples into the common space, allowing their feature representation to preserve both the original inter- and intra-modal semantic similarity relationship. During the training process, we employ GANs, which has advantages in modeling data distribution and learning discriminative representation, in order to learn different modal features. As a result, it can align different modal feature distributions. Generally, many cross-modal retrieval algorithms only preserve the inter-modal similarity relationship, which makes the nearest neighbor retrieval results vulnerable to noise. In contrast, we establish the triplet similarity preserving function to simultaneously preserve the inter- and intra-modal similarity relationship in the common space and in each modal space, respectively. Thus, the proposed algorithm has a strong robustness to noise. In each modal space, to ensure that the generated features have the same semantic information as the sample labels, we establish a linear classifier and require that the generated features’ classification results be consistent with the sample labels. We conducted cross-modal retrieval comparative experiments on two widely used benchmark datasets—Pascal Sentence and Wikipedia. For the image to text task, our proposed method improved the mAP values by 1% and 0.7% on the Pascal sentence and Wikipedia datasets, respectively. Correspondingly, the proposed method separately improved the mAP values of the text to image performance by 0.6% and 0.8% on the Pascal sentence and Wikipedia datasets, respectively. The experimental results show that the proposed algorithm is better than the other state-of-the-art methods. Full article

(This article belongs to the Special Issue Artificial Intelligence and Natural Computing: Theory, Methodology and Applications)

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19 pages, 804 KiB

Open AccessArticle

The Internet Shopping Optimization Problem with Multiple Item Units (ISHOP-U): Formulation, Instances, NP-Completeness, and Evolutionary Optimization

by Fernando Ornelas, Alejandro Santiago, Salvador Ibarra Martínez, Mirna Patricia Ponce-Flores, Jesús David Terán-Villanueva, Fausto Balderas, José Antonio Castán Rocha, Alejandro H. García, Julio Laria-Menchaca and Mayra Guadalupe Treviño-Berrones

Mathematics 2022, 10(14), 2513; https://doi.org/10.3390/math10142513 - 19 Jul 2022

Viewed by 1703

Abstract

In this work, we investigate the variant of the Internet Shopping Optimization Problem (ISHOP) that considers different item units. This variant is more challenging than the original problem. The original ISHOP is already known as a combinatorial NP-hard problem. In this work, we present a formal proof that the ISHOP variant considering different item units belongs to the NP-Hard complexity class. The abovementioned variant is familiar to companies and consumers who need to purchase more than one unit of a specific product to satisfy their requirements. For example, companies buy different quantities of construction materials, medical equipment, office supplies, or chemical components. We propose two new evolutionary operators (crossover and mutation) and an unfeasible solution repair method for the studied ISHOP variant. Furthermore, we produce a new benchmark of 15 synthetic instances where item prices follow a random uniform distribution. Finally, to assess our evolutionary operators, we implemented two Evolutionary Algorithms, a Genetic Algorithm (GA) and a Cellular Genetic Algorithm (CGA), and an experimental evaluation against a Water Cycle Algorithm (WCA) from the state-of-the-art. Experimental results show that our proposed GA performs well with statistical significance. Full article

(This article belongs to the Special Issue Artificial Intelligence and Natural Computing: Theory, Methodology and Applications)

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19 pages, 1561 KiB

Open AccessArticle

Task-Offloading Strategy Based on Performance Prediction in Vehicular Edge Computing

by Feng Zeng, Jiangjunzhe Tang, Chengsheng Liu, Xiaoheng Deng and Wenjia Li

Mathematics 2022, 10(7), 1010; https://doi.org/10.3390/math10071010 - 22 Mar 2022

Cited by 13 | Viewed by 2268

Abstract

In vehicular edge computing, network performance and computing resources dynamically change, and vehicles should find the optimal strategy for offloading their tasks to servers to achieve a rapid computing service. In this paper, we address the multi-layered vehicle edge-computing framework, where each vehicle can choose one of three strategies for task offloading. For the best offloading performance, we propose a prediction-based task-offloading scheme for the vehicles, in which a deep-learning model is designed to predict the task-offloading result (success/failure) and service delay, and then the predicted strategy with successful task offloading and minimum service delay is chosen as the final offloading strategy. In the proposed model, an automatic feature-generation model based on CNN is proposed to capture the intersection of features to generate new features, avoiding the performance instability caused by manually designed features. The simulation results demonstrate that each part of the proposed model has an important impact on the prediction accuracy, and the proposed scheme has the higher Area Under Curve (AUC) than other methods. Compared with SVM- and MLP-based methods, the proposed scheme has the average failure rate decreased by

21.2 %

and

6.3 %

, respectively. It can be seen that our prediction-based scheme can effectively deal with dynamic changes in network performance and computing resources. Full article

(This article belongs to the Special Issue Artificial Intelligence and Natural Computing: Theory, Methodology and Applications)

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24 pages, 3095 KiB

Open AccessArticle

Forecasting the Hydrogen Demand in China: A System Dynamics Approach

by Jingsi Huang, Wei Li and Xiangyu Wu

Mathematics 2022, 10(2), 205; https://doi.org/10.3390/math10020205 - 10 Jan 2022

Cited by 3 | Viewed by 3466

Abstract

Many countries, including China, have implemented supporting policies to promote the commercialized application of green hydrogen and hydrogen fuel cells. In this study, a system dynamics (SD) model is proposed to study the evolution of hydrogen demand in China from the petroleum refining industry, the synthetic ammonia industry, and the vehicle market. In the model, the impact from the macro-environment, hydrogen fuel supply, and construction of hydrogen facilities is considered to combine in incentives for supporting policies. To further formulate the competitive relationship in the vehicle market, the Lotka–Volterra (LV) approach is adopted. The model is verified using published data from 2003 to 2017. The model is also used to forecast China’s hydrogen demand up to the year of 2030 under three different scenarios. Finally, some forward-looking guidance is provided to policy makers according to the forecasting results. Full article

(This article belongs to the Special Issue Artificial Intelligence and Natural Computing: Theory, Methodology and Applications)

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