Automatic Design of Optimization Algorithms and Their Practical Applications

A special issue of Axioms (ISSN 2075-1680).

Deadline for manuscript submissions: closed (31 August 2023) | Viewed by 4317

Special Issue Editors


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Tecnológico de Monterrey, Av. Eugenio Garza Sada 2501 Sur, Col. Tecnológico, Monterrey 64849, Mexico
Interests: applied mathematics; thermal management; computer science; electronics
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Guest Editor
Department of Computer Science, University of Pretoria, Lynnwood Rd, Hatfield, Pretoria 0083, South Africa
Interests: optimisation; hyper-heuristics; evolutionary computation; automated design of machine learning; automated design of search techniques; metaheuristics; deep learning

Special Issue Information

Dear Colleagues,

Natural phenomena have inspired almost all the technological progress of humankind. Although this has represented crucial support in proposing new methodologies, particularly in optimization, the current trend of bio-inspired techniques has become frenetic. Interestingly, many of these techniques often reuse parts implemented in different applications. For that reason, we intend to launch this Special Issue to welcome original investigations on the automatic design of optimization algorithms for general or particular applications in benchmark and real-life problems. High-quality manuscripts that address the generation of metaphor-less hyper-heuristics, metaheuristics, and hybrid algorithms from theoretical and practical perspectives are solicited. Implementations using machine learning models for generating heuristic-based or heuristic-free techniques with outstanding performances are welcome. Potential topics include, but are not limited to, continuous optimization problems, scheduling, routing, packing, partitioning, graph theory, multi-criteria optimization, multi-objective optimization, and time complexity analysis.

We hope that this initiative is of your interest, and we encourage you to submit your current research to be included in the Special Issue.

Dr. Jorge Mario Cruz-Duarte
Prof. Dr. Nelishia Pillay 
Guest Editors

Manuscript Submission Information

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Keywords

  • automatic design
  • optimisation
  • metaphor-less algorithm
  • hyper-heuristic
  • metaheuristic
  • practical problems
  • continuous optimisation problems
  • combinatorial problems
  • machine learning

Published Papers (2 papers)

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Research

27 pages, 9540 KiB  
Article
UAV Path Planning Based on an Improved Chimp Optimization Algorithm
by Qinglong Chen, Qing He and Damin Zhang
Axioms 2023, 12(7), 702; https://doi.org/10.3390/axioms12070702 - 19 Jul 2023
Cited by 5 | Viewed by 1652
Abstract
Path planning is one of the key issues in the research of unmanned aerial vehicle technology. Its purpose is to find the best path between the starting point and the destination. Although there are many research recommendations on UAV path planning in the [...] Read more.
Path planning is one of the key issues in the research of unmanned aerial vehicle technology. Its purpose is to find the best path between the starting point and the destination. Although there are many research recommendations on UAV path planning in the literature, there is a lack of path optimization methods that consider both the complex flight environment and the performance constraints of the UAV itself. We propose an enhanced version of the Chimp Optimization Algorithm (TRS-ChOA) to solve the UAV path planning problem in a 3D environment. Firstly, we combine the differential mutation operator to enhance the search capability of the algorithm and prevent premature convergence. Secondly, we use improved reverse learning to expand the search range of the algorithm, effectively preventing the algorithm from missing high-quality solutions. Finally, we propose a similarity preference weight to prevent individuals from over-assimilation and enhance the algorithm’s ability to escape local optima. Through testing on 13 benchmark functions and 29 CEC2017 complex functions, TRS-ChOA demonstrates superior optimization capability and robustness compared to other algorithms. We apply TRS-ChOA along with five well-known algorithms to solve path planning problems in three 3D environments. The experimental results reveal that TRS-ChOA reduces the average path length/fitness value by 23.4%/65.0%, 8.6%/81.0%, and 16.3%/41.7% compared to other algorithms in the three environments, respectively. This indicates that the flight paths planned by TRS-ChOA are more cost-effective, smoother, and safer. Full article
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17 pages, 6079 KiB  
Article
Developing an Intelligent Cellular Structure Design for a UAV Wireless Communication Topology
by Eman S. Alkhalifah and Faris A. Almalki
Axioms 2023, 12(2), 129; https://doi.org/10.3390/axioms12020129 - 28 Jan 2023
Cited by 4 | Viewed by 1747
Abstract
In the current digital era, where Unmanned Aerial Vehicles (UAVs), Artificial intelligence (AI), and Internet of Everything (IoE) can be well integrated, more global connectivity and automated solutions can be witnessed. This paper aims to develop an intelligent cellular structure design for a [...] Read more.
In the current digital era, where Unmanned Aerial Vehicles (UAVs), Artificial intelligence (AI), and Internet of Everything (IoE) can be well integrated, more global connectivity and automated solutions can be witnessed. This paper aims to develop an intelligent cellular structure design for a UAV wireless communication topology using an AI framework. The proposed AI framework includes Self Organizing Maps (SOMs) and an NN fitting tool that can be simulated using the Graphical User Interface (GUI) toolbox in MATLAB. The proposed framework is validated in a proof-of-concept scenario, where various parameters of link budget and cellular structure design have been tuned to achieve an efficient and optimized automatic design. The obtained results show high levels of adaptable wireless communication predictions without human intervention, which is a noticeable shift from existing work in the literature. Full article
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