Performance Comparison between Particle Swarm Optimization and Differential Evolution Algorithms for Postman Delivery Routing Problem

Wisittipanich, Warisa; Phoungthong, Khamphe; Srisuwannapa, Chanin; Baisukhan, Adirek; Wisittipanit, Nuttachat

doi:10.3390/app11062703

Cited by 9 publications

(5 citation statements)

References 25 publications

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“…Although particle swarm optimization is a popular stochastic optimization method, it may not always provide low-complexity routing algorithms [45,46]. Authors in [47] compared the performance of the particle swarm optimization and differential evolution techniques to find delivery routings with minimum travel distances with quadratic complexity in time and space. This complexity is calculated based on a spatial distance matrix from the deployment to the landing.…”

Section: Complexity Of the Auas Swarm Routing Algorithmmentioning

confidence: 99%

Integrating Communication and Sensor Arrays to Model and Navigate Autonomous Unmanned Aerial Systems

et al. 2022

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The emerging concept of drone swarms creates new opportunities with major societal implications. However, future drone swarm applications and services pose new communications and sensing challenges, particularly for collaborative tasks. To address these challenges, in this paper, we integrate sensor arrays and communication to propose a mathematical model to route a collection of autonomous unmanned aerial systems (AUAS), a so-called drone swarm or AUAS swarm, without having a base station of communication but communicating with each other using multiple spatio-temporal data. The theories of structured matrices, concepts in multi-beam beamforming, and sensor arrays are utilized to propose a swarm routing algorithm. We address the routing algorithm’s computational and arithmetic complexities, precision, and reliability. We measure bit-error-rate (BER) based on the number of elements in sensor arrays and beamformed output of the members of the swarm to authenticate and secure the routing for the decentralized AUAS networking. The proposed model has the potential to enable future drone swarm applications and services. Finally, we discuss future work on obtaining a machine-learning-based low-cost drone swarm routing algorithm.

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Section: Complexity Of the Auas Swarm Routing Algorithmmentioning

confidence: 99%

Integrating Communication and Sensor Arrays to Model and Navigate Autonomous Unmanned Aerial Systems

et al. 2022

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“…However, it does not consistently yield routing algorithms with low complexity [1,20,62]. On the other hand, the paper [63] shows the performance comparisons of particle swarm optimization and differential evolution techniques while identifying delivery routes, with minimal travel distances having quadratic complexity in both time and space. Nevertheless, this complexity has been computed based on a spatial distance matrix derived from the deployment to landing locations [1].…”

Section: Arithmetic Complexity Of the Algorithmmentioning

confidence: 97%

“…Remark 1. Since N is fixed after the deployment of the drone swarms, i.e., the number of elements in the antenna array is fixed after the deployments, and followed by the Proposition 2, the RSwarm algorithm has just less than the quadratic complexity, i.e., O(M p ), where p < 2, while comparing with [1,20,[62][63][64][65][66]. Furthermore, the RSwarm algorithm allows one to route a drone swarm with over 100 members, which is an improvement compared to the limitations of the existing AODV-based swarm members [15,59,60].…”

Section: Arithmetic Complexity Of the Algorithmmentioning

confidence: 99%

Multi-Beam Beamforming-Based ML Algorithm to Optimize the Routing of Drone Swarms

Myers,

Perera,

McLewee

et al. 2024

Drones

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The advancement of wireless networking has significantly enhanced beamforming capabilities in Autonomous Unmanned Aerial Systems (AUAS). This paper presents a simple and efficient classical algorithm to route a collection of AUAS or drone swarms extending our previous work on AUAS. The algorithm is based on the sparse factorization of frequency Vandermonde matrices that correspond to each drone, and its entries are determined through spatiotemporal data of drones in the AUAS. The algorithm relies on multibeam beamforming, making it suitable for large-scale AUAS networking in wireless communications. We show a reduction in the arithmetic and time complexities of the algorithm through theoretical and numerical results. Finally, we also present an ML-based AUAS routing algorithm using the classical AUAS algorithm and feed-forward neural networks. We compare the beamformed signals of the ML-based AUAS routing algorithm with the ground truth signals to minimize the error between them. The numerical error results show that the ML-based AUAS routing algorithm enhances the accuracy of the routing. This error, along with the numerical and theoretical results for over 100 drones, provides the basis for the scalability of the proposed ML-based AUAS algorithms for large-scale deployments.

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“…Liu et al [11] designed a hybrid path-planning algorithm based on optimized reinforcement learning and improved particle swarm optimization to solve the pathplanning problem of intelligent driving vehicles. Halassi et al [12] presented a new multi-objective discrete particle swarm algorithm for the Capacitated vehicle routing problem, and Wisittipanich et al [13] applied two metaheuristic methods with particular solution representation, i.e., particle swarm optimization and differential evolution to find delivery routings with minimum travel distances. Early research often focused on cutting path problems using particle swarm optimization relying on inertia weight and individual and social learning factors.…”

Section: Introductionmentioning

confidence: 99%

Improved Particle Swarm Optimization for Laser Cutting Path Planning

2023

IEEE Access

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This research focuses on the long empty cutting path problem during the laser cutting process by employing an improved proximity method to establish the starting point set in complex closed graphics. Specifically, this work improves the particle swarm algorithm and proposes the Levy Flight, power function, and Singer map employed particle swarm optimization (LPSPSO) to avoid the disadvantages of the standard particle swarm optimization (PSO) algorithm. Specifically, the comprehensive prospect-regret theoretical model evaluation value is used as the fitness value to guide the algorithm's evolution and adaptively adjust the parameters in the LPSPSO algorithm, including the inertia weight power function, the learning factors, and the chaotic random number based on the Singer chaotic map. Additionally, the Levy flight is introduced to disturb the particles and prevent local optimization. This is achieved by adjusting the Levy flight threshold based on the distance between the particles to prevent the Levy flight from starting prematurely and increasing the calculation burden. To verify the performance of the LPSPSO algorithm, it was challenged against three state-of-the-art algorithms on 22 benchmark test instances and a laser cutting problem, with the results revealing that the LPSPSO algorithm has a better performance and can be used to solve the empty length of the laser cutting path problem.INDEX TERMS laser cutting path planning, improved particle swarm optimization, improved proximity method, Levy flight threshold, comprehensive prospect-regret theory, chaotic random number

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Performance Comparison between Particle Swarm Optimization and Differential Evolution Algorithms for Postman Delivery Routing Problem

Cited by 9 publications

References 25 publications

Integrating Communication and Sensor Arrays to Model and Navigate Autonomous Unmanned Aerial Systems

Integrating Communication and Sensor Arrays to Model and Navigate Autonomous Unmanned Aerial Systems

Multi-Beam Beamforming-Based ML Algorithm to Optimize the Routing of Drone Swarms

Improved Particle Swarm Optimization for Laser Cutting Path Planning

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