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Algorithms
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Hybrid Intelligent Algorithms (2nd Edition)
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Hybrid Intelligent Algorithms (2nd Edition)

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Evolutionary Algorithms and Machine Learning".

Deadline for manuscript submissions: 31 October 2025 | Viewed by 3262

Special Issue Editor

Prof. Dr. Grigorios Beligiannis

E-Mail Website
Guest Editor
Department of Food Science and Technology, University of Patras, 30100 Agrinio, Greece
Interests: artificial intelligence; computational intelligence; machine learning; genetic/evolutionary algorithms; decision support theory; intelligent information systems; applications of hybrid intelligent information systems for modeling real world time series belonging to linear and non-linear systems; design and development of hybrid intelligent algorithms for solving timetabling and scheduling problems; multi-objective optimization
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Combining and hybridizing intelligent algorithms originating from different areas of computational intelligence to solve difficult real world problems has become very popular in recent decades. This is mainly due to the growing awareness that the application of hybrid intelligent algorithms most often results in better performance than applying individual computational intelligence algorithms, such as neural networks, evolutionary algorithms, fuzzy systems, particle swarm optimization, etc. The application of such hybrid intelligent schemes has indicated that hybrid intelligence algorithms succeed in solving some very difficult real world problems in which the application of deterministic or individual computational intelligence algorithms is either not possible or extremely time-consuming. In a hybrid intelligence system, a synergistic combination of multiple intelligent techniques is used to build an efficient solution to deal effectively with a particular problem. This Special Issue will comprise papers focused on hybrid intelligent algorithms following different approaches and their real-world applications.

Prof. Dr. Grigorios Beligiannis
Guest Editor

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. Algorithms 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 1800 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

  • neural networks
  • evolutionary algorithms
  • fuzzy systems
  • hybrid intelligence system
  • computational intelligence
  • soft computing
  • heuristics
  • metaheuristics

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Related Special Issue

Published Papers (4 papers)

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Research

22 pages, 2209 KB  
Article
Hybrid BiLSTM-ARIMA Architecture with Whale-Driven Optimization for Financial Time Series Forecasting
by Panke Qin, Bo Ye, Ya Li, Zhongqi Cai, Zhenlun Gao, Haoran Qi and Yongjie Ding
Algorithms 2025, 18(8), 517; https://doi.org/10.3390/a18080517 - 15 Aug 2025
Viewed by 237
Abstract
Financial time series display inherent nonlinearity and high volatility, creating substantial challenges for accurate forecasting. Advancements in artificial intelligence have positioned deep learning as a critical tool for financial time series forecasting. However, conventional deep learning models often fail to accurately predict future [...] Read more.
Financial time series display inherent nonlinearity and high volatility, creating substantial challenges for accurate forecasting. Advancements in artificial intelligence have positioned deep learning as a critical tool for financial time series forecasting. However, conventional deep learning models often fail to accurately predict future trends in complex financial data due to inherent limitations. To address these challenges, this study introduces a WOA-BiLSTM-ARIMA hybrid forecasting model leveraging parameter optimization. Specifically, the whale optimization algorithm (WOA) optimizes hyperparameters for the Bidirectional Long Short-Term Memory (BiLSTM) network, overcoming parameter tuning challenges in conventional approaches. Due to its strong capacity for nonlinear feature extraction, BiLSTM excels at modeling nonlinear patterns in financial time series. To mitigate the shortcomings of BiLSTM in capturing linear patterns, the Autoregressive Integrated Moving Average (ARIMA) methodology is integrated. By exploiting ARIMA’s strengths in modeling linear features, the model refines BiLSTM’s prediction residuals, achieving more accurate and comprehensive financial time series forecasting. To validate the model’s effectiveness, this paper applies it to the prediction experiment of future spread data. Compared to classical models, WOA-BiLSTM-ARIMA achieves significant improvements across multiple evaluation metrics. The mean squared error (MSE) is reduced by an average of 30.5%, the mean absolute error (MAE) by 20.8%, and the mean absolute percentage error (MAPE) by 29.7%. Full article
(This article belongs to the Special Issue Hybrid Intelligent Algorithms (2nd Edition))
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24 pages, 623 KB  
Article
Evaluation of Competitiveness and Sustainable Development Prospects of French-Speaking African Countries Based on TOPSIS and Adaptive LASSO Algorithms
by Binglin Liu, Liwen Li, Hang Ren, Jianwan Qin and Weijiang Liu
Algorithms 2025, 18(8), 474; https://doi.org/10.3390/a18080474 - 30 Jul 2025
Viewed by 336
Abstract
This study evaluates the competitiveness and sustainable development prospects of French-speaking African countries by constructing a comprehensive framework integrating the TOPSIS method and adaptive LASSO algorithm. Using multivariate data from sources such as the World Bank, 30 indicators covering core, basic, and auxiliary [...] Read more.
This study evaluates the competitiveness and sustainable development prospects of French-speaking African countries by constructing a comprehensive framework integrating the TOPSIS method and adaptive LASSO algorithm. Using multivariate data from sources such as the World Bank, 30 indicators covering core, basic, and auxiliary competitiveness were selected to quantitatively analyze the competitiveness of 26 French-speaking African countries. Results show that their comprehensive competitiveness exhibits spatial patterns of “high in the north and south, low in the east and west” and “high in coastal areas, low in inland areas”. Algeria, Morocco, and six other countries demonstrate high competitiveness, while Central African countries generally show low competitiveness. The adaptive LASSO algorithm identifies three key influencing factors, including the proportion of R&D expenditure to GDP, high-tech exports, and total reserves, as well as five secondary key factors, including the number of patent applications and total number of domestic listed companies, revealing that scientific and technological investment, financial strength, and innovation transformation capabilities are core constraints. Based on these findings, sustainable development strategies are proposed, such as strengthening scientific and technological research and development and innovation transformation, optimizing financial reserves and capital markets, and promoting China–Africa collaborative cooperation, providing decision-making references for competitiveness improvement and regional cooperation of French-speaking African countries under the background of the “Belt and Road Initiative”. Full article
(This article belongs to the Special Issue Hybrid Intelligent Algorithms (2nd Edition))
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33 pages, 12286 KB  
Article
A Weight Assignment-Enhanced Convolutional Neural Network (WACNN) for Freight Volume Prediction of Sea–Rail Intermodal Container Systems
by Yuhonghao Wang, Wenxin Li, Xingmin Qi and Yinzhang Yu
Algorithms 2025, 18(6), 319; https://doi.org/10.3390/a18060319 - 27 May 2025
Cited by 1 | Viewed by 388
Abstract
In order to integrate the use of transportation resources, develop a reasonable sea–rail intermodal container transportation plan, and achieve cost reduction and efficiency improvement of the multimodal transportation system, a method for predicting the daily freight volume of sea–rail intermodal transportation based on [...] Read more.
In order to integrate the use of transportation resources, develop a reasonable sea–rail intermodal container transportation plan, and achieve cost reduction and efficiency improvement of the multimodal transportation system, a method for predicting the daily freight volume of sea–rail intermodal transportation based on a convolutional neural network (CNN) algorithm is proposed and a new feature processing method is used: weight assignment (WA). Firstly, we use qualitative methods to preliminarily select the indicators, and then use multiple interpolation to fill in the missing raw data. Next, Pearson and Spearman quantitative analysis methods are used, and the analysis results are grouped using the k-means, with the high correlation groups assigned high weights. Next, we use quadratic interpolation to obtain the daily data. Finally, a weight assignment-enhanced convolutional neural network (WACNN) model and seven other mainstream models are constructed, using the Yingkou port container throughput prediction as a case study. The research results indicate that the WACNN prediction model has the best performance and strong robustness. The research results can provide a reference basis for the planning of sea–rail intermodal container transportation and the allocation of transportation resources, and achieve the overall efficiency improvement of logistics systems. Full article
(This article belongs to the Special Issue Hybrid Intelligent Algorithms (2nd Edition))
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31 pages, 429 KB  
Article
Solution of Bin Packing Instances in Falkenauer T Class: Not So Hard
by György Dósa, András Éles, Angshuman Robin Goswami, István Szalkai and Zsolt Tuza
Algorithms 2025, 18(2), 115; https://doi.org/10.3390/a18020115 - 19 Feb 2025
Cited by 1 | Viewed by 1651
Abstract
In this work, the Bin Packing combinatorial optimization problem is studied from the practical side. The focus is on the Falkenauer T benchmark class, which is a collection of 80 problem instances that are considered hard to handle algorithmically. Contrary to this widely [...] Read more.
In this work, the Bin Packing combinatorial optimization problem is studied from the practical side. The focus is on the Falkenauer T benchmark class, which is a collection of 80 problem instances that are considered hard to handle algorithmically. Contrary to this widely accepted view, we show that the instances of this benchmark class can be solved relatively easily, without applying any sophisticated methods like metaheuristics. A new algorithm is proposed, which can operate in two modes: either using backtrack or local search to find optimal packing. In theory, both operating modes are guaranteed to find a solution. Computational results show that all instances of the Falkenauer T benchmark class can be solved in a total of 1.18 s and 2.39 s with the two operating modes alone, or 0.2 s when running in parallel. Full article
(This article belongs to the Special Issue Hybrid Intelligent Algorithms (2nd Edition))
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