Different Applications of PSO

Badar, Altaf Q. H.

doi:10.1007/978-3-030-70281-6_11

Cited by 4 publications

(4 citation statements)

References 47 publications

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“…PSO is a numerical optimization algorithm based on swarm intelligence, first introduced in 1995. It has been improved and widely researched, leading to various applications in different fields by [3,4,5,6]. PSO is a population-based stochastic method inspired by the social behavior of bird flocking or fish schooling [4].…”

Section: Introductionmentioning

confidence: 99%

Optimizing Track Contact Length for Enhanced Dynamics and Performance of Tracked Vehicles: A Particle Swarm Optimization Approach

Elsharkawy,

Salem,

Kamel

et al. 2024

J. Phys.: Conf. Ser.

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Through an in-depth investigation of tracked vehicle dynamics, this paper explores the crucial role that the track contact length (L) plays in affecting the dynamic performance of tracked vehicles. The study delves into the basic equations that control straight-line motion and turning as functions of track contact length, with an emphasis on these concepts. To improve the tracked vehicle’s overall performance, an optimization procedure is carried out using the Particle Swarm Optimization (PSO) technique that is implemented in MATLAB. The aim of this study is to clarify the complex relationship between track contact length and tracked vehicle dynamics, offering useful information for enhancing vehicles’ mobility. The study includes a pioneering validation, aligning computational results with real-world data, affirming the accuracy and practical utility of the designed code. This research provides valuable insights for designing tracked vehicles through offering a reliable guide for optimizing track contact length.

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Section: Introductionmentioning

confidence: 99%

Optimizing Track Contact Length for Enhanced Dynamics and Performance of Tracked Vehicles: A Particle Swarm Optimization Approach

Elsharkawy,

Salem,

Kamel

et al. 2024

J. Phys.: Conf. Ser.

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“…The VRP has been studied for decades with several variants and solution approaches 12,13 . But traditional vehicles suffer from the major problem of GHGs emission.…”

Section: Introductionmentioning

confidence: 99%

“…The VRP has been studied for decades with several variants and solution approaches. 12,13 But traditional vehicles suffer from the major problem of GHGs emission. So, to mitigate the hazardous environmental jolt of fossil fuel-based vehicles, constant efforts are being made to adopt the AFVs such as EV.…”

Section: Introductionmentioning

confidence: 99%

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A mat‐heuristics approach for electric vehicle route optimization under multiple recharging options and time‐of‐use energy prices

Aggarwal

Rani

Bedi

et al. 2023

Concurrency and Computation

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SummaryTraveling has contributed a lot to the evolution of mankind. Today, electric vehicles (EVs) are being preferred due to their greater efficiency, comfort, and environment‐friendly qualities. The EVs' contribution to future mobility is projected to rise exponentially in years to come. To make this innovative technology more successful, there is a dire need to install a sufficient number of charging stations (CSs). As the EVs are limited by their cruising range, they require multiple recharging to cover long distances (especially in the case of logistics delivery services). Thus, there is a great need to develop an efficient and cost‐effective EV route optimization approach considering multiple recharging options and time‐of‐use (ToU) energy prices. In this regard, a novel mat‐heuristic approach named (firefly with ant colony algorithm) has been proposed to solve the problem of EVRPTW (electric vehicle routing problem with time windows) incorporating detailed modeling of multiple charging flexibility (i.e., battery swapping, partial recharge, and different charging levels) and ToU energy prices. Our proposed approach aims to minimize the total cost of traveling, which is highly influenced by the cost of recharging. Ant colony algorithm (ACA) serves as the basic optimization framework in the proposed approach, while the firefly approach explores hitherto unexplored solution space and avoids local optima. The computation performance of the proposed approach is compared with existing state‐of‐the‐art similar domain approaches such as variable neighborhood search (VNS) and ant colony optimization using local search (ACO‐LS) which has average deviation of nearly 20%–25% from with optimal solution achieved by the proposed . The proposed approach yields a near‐optimal solution with a faster convergence rate (approximately 50%) compared to other existing approaches. Moreover, the multiple recharging options modeled in our proposed approach justify their significance in terms of cost‐effectiveness for most scenarios.

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Reducing Contact Bouncing of a Relay by Optimizing the Switch Signal During Run-Time

Winkel,

Scholz,

Wallscheid

et al. 2024

IEEE Trans. Automat. Sci. Eng.

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Different Applications of PSO

Cited by 4 publications

References 47 publications

Optimizing Track Contact Length for Enhanced Dynamics and Performance of Tracked Vehicles: A Particle Swarm Optimization Approach

Optimizing Track Contact Length for Enhanced Dynamics and Performance of Tracked Vehicles: A Particle Swarm Optimization Approach

A mat‐heuristics approach for electric vehicle route optimization under multiple recharging options and time‐of‐use energy prices

Reducing Contact Bouncing of a Relay by Optimizing the Switch Signal During Run-Time

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