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 1181

Special Issue Editor


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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
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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

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Keywords

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

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Published Papers (1 paper)

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Research

31 pages, 429 KiB  
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
Viewed by 723
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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