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Swarm and Metaheuristic Optimization for Applied Engineering Application

Submission Deadline: 01 March 2025 (closed) View: 584 Submit to Special Issue

Guest Editors

Dr. Marwa M. Eid, Delta University for Science and Technology, Egypt
Dr. Nima Khodadadi, University of Miami, Coral Gables, FL, USA

Summary

Swarm intelligence (SI) is based on the coordinated behavior of a decentralized system that is self-organized, and the potential of SI in the AI field is very considerable. "This is how SI principles impact AI research. they are used to solve complex engineering problems using a distributed approach." Amongst various human being applications, SI optimization is one of the most effective. This field can be dubbed mathematical programming or simply optimization. Optimization techniques are always used to discover the best solution out of a whole set of possibilities, regardless of the industry sector, from engineering to finance, logistics, or telecommunications.


Swarm intelligence and metaheuristic optimization are two highly effective methods of AI optimization techniques. Different metaheuristic algorithms, namely genetic algorithms, simulated annealing and particle swarm optimization, serve as potent and versatile optimization techniques that possess a natural ability to travel through complex search areas with ease to obtain near-optimal solutions. These algorithms take inspiration from natural phenomena or problem-solving concepts, enabling them to tackle difficult problems that are not solvable using common methods.


The present special issue enters the area where smart computing finds its practical applications, covering the topics of swarm optimization and metaheuristic optimization for AI in engineering applications. Also, it can act as a stage for putting novel research conceptions and discoveries made about those approaches in the limelight. Both theoretical and practical contributions are encouraged anywhere from the foundation studies of swarm intelligence through system implementations to smart applications and other critical research areas intended to develop the fields of swarm intelligence and metaheuristics to the point that they can solve real problems. This collaborative effort serves to bring interdisciplinary dialogues to brush up on the new school of thought and find ways to find solutions in the field of AI and optimization.


Keywords

Swarm Intelligence, Deep learning, Intelligent Automation, Computer-based algorithms, Soft Computing, Time Series and Forecasting, Artificial intelligence applications, Metaheuristic Optimization

Published Papers


  • Open Access

    REVIEW

    Stochastic Fractal Search: A Decade Comprehensive Review on Its Theory, Variants, and Applications

    Mohammed A. El-Shorbagy, Anas Bouaouda, Laith Abualigah, Fatma A. Hashim
    CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.3, pp. 2339-2404, 2025, DOI:10.32604/cmes.2025.061028
    (This article belongs to the Special Issue: Swarm and Metaheuristic Optimization for Applied Engineering Application)
    Abstract With the rapid advancements in technology and science, optimization theory and algorithms have become increasingly important. A wide range of real-world problems is classified as optimization challenges, and meta-heuristic algorithms have shown remarkable effectiveness in solving these challenges across diverse domains, such as machine learning, process control, and engineering design, showcasing their capability to address complex optimization problems. The Stochastic Fractal Search (SFS) algorithm is one of the most popular meta-heuristic optimization methods inspired by the fractal growth patterns of natural materials. Since its introduction by Hamid Salimi in 2015, SFS has garnered significant attention… More >

  • Open Access

    ARTICLE

    Enhanced Particle Swarm Optimization Algorithm Based on SVM Classifier for Feature Selection

    Xing Wang, Huazhen Liu, Abdelazim G. Hussien, Gang Hu, Li Zhang
    CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.3, pp. 2791-2839, 2025, DOI:10.32604/cmes.2025.058473
    (This article belongs to the Special Issue: Swarm and Metaheuristic Optimization for Applied Engineering Application)
    Abstract Feature selection (FS) is essential in machine learning (ML) and data mapping by its ability to preprocess high-dimensional data. By selecting a subset of relevant features, feature selection cuts down on the dimension of the data. It excludes irrelevant or surplus features, thus boosting the performance and efficiency of the model. Particle Swarm Optimization (PSO) boasts a streamlined algorithmic framework and exhibits rapid convergence traits. Compared with other algorithms, it incurs reduced computational expenses when tackling high-dimensional datasets. However, PSO faces challenges like inadequate convergence precision. Therefore, regarding FS problems, this paper presents a binary… More >

  • Open Access

    ARTICLE

    Robust Particle Swarm Optimization Algorithm for Modeling the Effect of Oxides Thermal Properties on AMIG 304L Stainless Steel Welds

    Rachid Djoudjou, Abdeljlil Chihaoui Hedhibi, Kamel Touileb, Abousoufiane Ouis, Sahbi Boubaker, Hani Said Abdo
    CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.2, pp. 1809-1825, 2024, DOI:10.32604/cmes.2024.053621
    (This article belongs to the Special Issue: Swarm and Metaheuristic Optimization for Applied Engineering Application)
    Abstract There are several advantages to the MIG (Metal Inert Gas) process, which explains its increased use in various welding sectors, such as automotive, marine, and construction. A variant of the MIG process, where the same equipment is employed except for the deposition of a thin layer of flux before the welding operation, is the AMIG (Activated Metal Inert Gas) technique. This study focuses on investigating the impact of physical properties of individual metallic oxide fluxes for 304L stainless steel welding joint morphology and to what extent it can help determine a relationship among weld depth… More >

  • Open Access

    ARTICLE

    Far and Near Optimization: A New Simple and Effective Metaphor-Less Optimization Algorithm for Solving Engineering Applications

    Tareq Hamadneh, Khalid Kaabneh, Omar Alssayed, Kei Eguchi, Zeinab Monrazeri, Mohammad Dehghani
    CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.2, pp. 1725-1808, 2024, DOI:10.32604/cmes.2024.053236
    (This article belongs to the Special Issue: Swarm and Metaheuristic Optimization for Applied Engineering Application)
    Abstract In this article, a novel metaheuristic technique named Far and Near Optimization (FNO) is introduced, offering versatile applications across various scientific domains for optimization tasks. The core concept behind FNO lies in integrating global and local search methodologies to update the algorithm population within the problem-solving space based on moving each member to the farthest and nearest member to itself. The paper delineates the theory of FNO, presenting a mathematical model in two phases: (i) exploration based on the simulation of the movement of a population member towards the farthest member from itself and (ii)… More >

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