A Fast-Convergent Hyperbolic Tangent PSO Algorithm for UAVs Path Planning
Particle Swarm Optimization (PSO) stands as a cornerstone among population-based swarm intelligence algorithms, serving as a versatile tool to tackle diverse scientific and engineering optimization challenges due to its straightforward implementation and promising optimization capabilities. Nonethel...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Open Journal of Vehicular Technology |
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| Online Access: | https://ieeexplore.ieee.org/document/10505768/ |
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| author | Muhammad Haris Dost Muhammad Saqib Bhatti Haewoon Nam |
| author_facet | Muhammad Haris Dost Muhammad Saqib Bhatti Haewoon Nam |
| author_sort | Muhammad Haris |
| collection | DOAJ |
| description | Particle Swarm Optimization (PSO) stands as a cornerstone among population-based swarm intelligence algorithms, serving as a versatile tool to tackle diverse scientific and engineering optimization challenges due to its straightforward implementation and promising optimization capabilities. Nonetheless, PSO has its limitations, notably its propensity for slow convergence. Traditionally, PSO operates by guiding swarms through positions determined by their initial velocities and acceleration components, encompassing cognitive and social information. In pursuit of expedited convergence, we introduce a novel approach: the Cognitive and Social Information-Based Hyperbolic Tangent Particle Swarm Optimization (HT-PSO) algorithm. This innovation draws inspiration from the activation functions employed in neural networks, with the singular aim of accelerating convergence. To combat the issue of slow convergence, we reengineer the cognitive and social acceleration coefficients of the PSO algorithm, leveraging the power of the hyperbolic tangent function. This strategic adjustment fosters a dynamic balance between exploration and exploitation, unleashing PSO's full potential. Our experimental trials encompass thirteen benchmark functions spanning unimodal and multimodal landscapes. Besides that, the proposed algorithm is also applied to different UAV path planning scenarios, underscoring its real-world relevance. The outcomes underscore the prowess of HT-PSO, showcasing significantly better convergence rates compared to the state-of-the-art. |
| format | Article |
| id | doaj-art-9eb3f73651bc455d98c964df99fff76b |
| institution | Kabale University |
| issn | 2644-1330 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of Vehicular Technology |
| spelling | doaj-art-9eb3f73651bc455d98c964df99fff76b2025-01-30T00:04:10ZengIEEEIEEE Open Journal of Vehicular Technology2644-13302024-01-01568169410.1109/OJVT.2024.339138010505768A Fast-Convergent Hyperbolic Tangent PSO Algorithm for UAVs Path PlanningMuhammad Haris0https://orcid.org/0009-0001-5706-0800Dost Muhammad Saqib Bhatti1https://orcid.org/0000-0002-0204-8484Haewoon Nam2https://orcid.org/0000-0001-9847-7023Department of Electrical and Electronic Engineering, Hanyang University, Ansan, South KoreaDepartment of Electrical and Electronic Engineering, Hanyang University, Ansan, South KoreaDepartment of Electrical and Electronic Engineering, Hanyang University, Ansan, South KoreaParticle Swarm Optimization (PSO) stands as a cornerstone among population-based swarm intelligence algorithms, serving as a versatile tool to tackle diverse scientific and engineering optimization challenges due to its straightforward implementation and promising optimization capabilities. Nonetheless, PSO has its limitations, notably its propensity for slow convergence. Traditionally, PSO operates by guiding swarms through positions determined by their initial velocities and acceleration components, encompassing cognitive and social information. In pursuit of expedited convergence, we introduce a novel approach: the Cognitive and Social Information-Based Hyperbolic Tangent Particle Swarm Optimization (HT-PSO) algorithm. This innovation draws inspiration from the activation functions employed in neural networks, with the singular aim of accelerating convergence. To combat the issue of slow convergence, we reengineer the cognitive and social acceleration coefficients of the PSO algorithm, leveraging the power of the hyperbolic tangent function. This strategic adjustment fosters a dynamic balance between exploration and exploitation, unleashing PSO's full potential. Our experimental trials encompass thirteen benchmark functions spanning unimodal and multimodal landscapes. Besides that, the proposed algorithm is also applied to different UAV path planning scenarios, underscoring its real-world relevance. The outcomes underscore the prowess of HT-PSO, showcasing significantly better convergence rates compared to the state-of-the-art.https://ieeexplore.ieee.org/document/10505768/Particle swarm optimization (PSO)acceleration coefficientsconvergence rateexplorationexploitationand path planning |
| spellingShingle | Muhammad Haris Dost Muhammad Saqib Bhatti Haewoon Nam A Fast-Convergent Hyperbolic Tangent PSO Algorithm for UAVs Path Planning IEEE Open Journal of Vehicular Technology Particle swarm optimization (PSO) acceleration coefficients convergence rate exploration exploitation and path planning |
| title | A Fast-Convergent Hyperbolic Tangent PSO Algorithm for UAVs Path Planning |
| title_full | A Fast-Convergent Hyperbolic Tangent PSO Algorithm for UAVs Path Planning |
| title_fullStr | A Fast-Convergent Hyperbolic Tangent PSO Algorithm for UAVs Path Planning |
| title_full_unstemmed | A Fast-Convergent Hyperbolic Tangent PSO Algorithm for UAVs Path Planning |
| title_short | A Fast-Convergent Hyperbolic Tangent PSO Algorithm for UAVs Path Planning |
| title_sort | fast convergent hyperbolic tangent pso algorithm for uavs path planning |
| topic | Particle swarm optimization (PSO) acceleration coefficients convergence rate exploration exploitation and path planning |
| url | https://ieeexplore.ieee.org/document/10505768/ |
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