An improved parallel processing-based strawberry optimization algorithm for drone placement

Farrag, Tamer Ahmed; Farag, M. A.; Rizk‐Allah, Rizk M.; Hassanien, Aboul Ella; Elhosseini, Mostafa A.

doi:10.1007/s11235-022-00970-7

Cited by 3 publications

(2 citation statements)

References 35 publications

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“…However, due to fixed algorithm parameters, this method can easily fall into local optima and fail to obtain the optimal solution. In [31], Farrag et al proposed a strawberry optimization algorithm (SBA) to solve the problem of unmanned placement and reduce the number of unmanned vehicles under full coverage. However, this method can easily fall into evolutionary stagnation.…”

Section: Related Workmentioning

confidence: 99%

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A Niche Adaptive Elite Evolutionary Algorithm for the Clustering Optimization of Intelligent Unmanned Agricultural Unmanned Aerial Vehicle Swarm Collaboration Networks

Zhong,

Zhou,

Zhang

2023

Applied Sciences

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Nowadays, the intelligent unmanned agricultural unmanned aerial vehicle (UAV) swarm collaboration network (AUSCN) has fully demonstrated its advantages in agricultural monitoring and management. By using an AUSCN, multi-machine cooperation can be realized to expand the detection range, and more complex tasks can be completed without human participation, so as to improve work efficiency and reduce the consumption of manpower and material resources. In AUSCNs, clustering is a key method to lower energy consumption. However, there is a challenge to select cluster heads in AUSCNs because of the limitation of transmission distances and the complexity of network topological structures. In addition, this problem has been confirmed as NP-hard. In this paper, a new niche adaptive elite evolutionary algorithm (NAEEA) is proposed to solve this problem. NAEEAs can search within various complicated stochastic situations at high speeds with characterized high precision and fast convergence. This algorithm integrates the merits of elite selection and adaptive adjusting to achieve high performance. In NAEEAs, a new adaptive operator is designed to speed up the convergence rate, while a novel elite operator is proposed to avoid local optima and raise the exploration ability. Furthermore, a new niche operator is also proposed to increase the population diversity. The simulation results show that, compared with an evolutionary algorithm (EA), a simulated annealing algorithm (SA) and a leapfrog algorithm (SFLA), clustering energy consumption based on an NAEEA is significantly reduced, and the network energy consumption of the AUSCN is up to 21.43%, 25.00% and 25.76% lower than the other three algorithms, respectively.

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Section: Related Workmentioning

confidence: 99%

“…Farrag et al [31] 2023 SBA Leverage SBA to solve the drone placement problem through parallelization.…”

Section: Related Workmentioning

confidence: 99%

A Niche Adaptive Elite Evolutionary Algorithm for the Clustering Optimization of Intelligent Unmanned Agricultural Unmanned Aerial Vehicle Swarm Collaboration Networks

Zhong,

Zhou,

Zhang

2023

Applied Sciences

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Intelligent Parcel Delivery Scheduling Using Truck-Drones to Cut down Time and Cost

Farrag,

Askr,

Elhosseini

et al. 2024

Drones

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In the evolving landscape of logistics, drone technology presents a solution to the challenges posed by traditional ground-based deliveries, such as traffic congestion and unforeseen road closures. This research addresses the Truck–Drone Delivery Problem (TDDP), wherein a truck collaborates with a drone, acting as a mobile charging and storage unit. Although the Traveling Salesman Problem (TSP) can represent the TDDP, it becomes computationally burdensome when nodes are dynamically altered. Motivated by this limitation, our study’s primary objective is to devise a model that ensures swift execution without compromising the solution quality. We introduce two meta-heuristics: the Strawberry Plant, which refines the initial truck schedule, and Genetic Algorithms, which optimize the combined truck–drone schedule. Using “Dataset 1” and comparing with the Multi-Start Tabu Search (MSTS) algorithm, our model targeted costs to remain within 10% of the optimum and aimed for a 73% reduction in the execution time. Of the 45 evaluations, 37 met these cost parameters, with our model surpassing MSTS in eight scenarios. In contrast, using “Dataset 2” against the CPLEX solver, our model optimally addressed all 810 experiments, while CPLEX managed only 90 within the prescribed time. For 20-customer scenarios and more, CPLEX encountered memory limitations. Notably, when both methods achieved optimal outcomes, our model’s computational efficiency exceeded CPLEX by a significant margin. As the customer count increased, so did computational challenges, indicating the importance of refining our model’s strategies. Overall, these findings underscore our model’s superiority over established solvers like CPLEX and the economic advantages of drone-assisted delivery systems.

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A Novel Meta-Heuristic Algorithm Based on Birch Succession in the Optimization of an Electric Drive with a Flexible Shaft

Malarczyk,

Katsura,

Kaminski

et al. 2024

Energies

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The paper presents the application of a new bio-inspired metaheuristic optimization algorithm. The popularity and usability of different swarm-based metaheuristic algorithms are undeniable. The majority of known algorithms mimic the hunting behavior of animals. However, the current approach does not satisfy the full bio-diversity inspiration among different organisms. Thus, the Birch-inspired Optimization Algorithm (BiOA) is proposed as a powerful and efficient tool based on the pioneering behavior of one of the most common tree species. Birch trees are known for their superiority over other species in overgrowing and spreading across unrestricted terrains. The proposed two-step algorithm reproduces both the seed transport and plant development. A detailed description and the mathematical model of the algorithm are given. The discussion and examination of the influence of the parameters on efficiency are also provided in detail. In order to demonstrate the effectiveness of the proposed algorithm, its application to selecting the parameters of the control structure of a drive system with an elastic connection is shown. A structure with a PI controller and two additional feedbacks on the torque and speed difference between the drive motor and the working machine was selected. A system with rated and variable parameters is considered. The theoretical considerations and the simulation study were verified on a laboratory stand.

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An improved parallel processing-based strawberry optimization algorithm for drone placement

Cited by 3 publications

References 35 publications

A Niche Adaptive Elite Evolutionary Algorithm for the Clustering Optimization of Intelligent Unmanned Agricultural Unmanned Aerial Vehicle Swarm Collaboration Networks

A Niche Adaptive Elite Evolutionary Algorithm for the Clustering Optimization of Intelligent Unmanned Agricultural Unmanned Aerial Vehicle Swarm Collaboration Networks

Intelligent Parcel Delivery Scheduling Using Truck-Drones to Cut down Time and Cost

A Novel Meta-Heuristic Algorithm Based on Birch Succession in the Optimization of an Electric Drive with a Flexible Shaft

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