It is challenging to place drones in the best possible locations to monitor all sensor targets while keeping the number of drones to a minimum. strawberryoptimization (SBA) has been demonstrated to be more effective ...
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It is challenging to place drones in the best possible locations to monitor all sensor targets while keeping the number of drones to a minimum. strawberryoptimization (SBA) has been demonstrated to be more effective and superior to current methods in evaluating engineering functions in various engineering problems. Because the SBA is a new method, it has never been used to solve problems involving optimal drone placement. SBA is preferred for optimizing drone placement in this study due to its promising results for nonlinear, mixed, and multimodal problems. Based on the references listed below, no study has investigated the need to develop a parallelized strategy version. Several studies have been conducted on the use of drones for coverage. However, no optimizationalgorithms have been evaluated regarding time complexity or execution time. Despite what has been said thus far, no study has looked into the significance of a systematic framework for assessing drone coverage techniques using test suits. An optimized drone placement algorithm based on strawberryoptimization is presented in the paper. The strawberry optimization algorithm will solve the drone placement problem through parallelization. In addition, the authors deploy test suits that vary in size from small to large. The dataset consists of four categories with three problems each. Results indicate that strawberry optimizers outperform Genetic algorithms (GA) and particle swarm optimizationalgorithms (PSO) in the number of drones, convergence, and computation time. Furthermore, the proposed approach achieves the best solution in a finite number of steps. In small-scale problems, the performance of all algorithms is convergent. As the size of the data set increases, the superiority of strawberry optimization algorithms becomes evident. Overall, strawberry comes out on top for eleven out of twelve comparisons.
To study the polarization reflection characteristics of metal surfaces, a parameter optimization method for the polarization bidirectional reflection distribution function (pBRDF) model of metal surfaces based on the ...
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To study the polarization reflection characteristics of metal surfaces, a parameter optimization method for the polarization bidirectional reflection distribution function (pBRDF) model of metal surfaces based on the improved strawberryalgorithm has been proposed. Firstly, the light scattering characteristics of metal surfaces were analyzed and a multi-parameter pBRDF model was constructed. Then, the working mechanism of the strawberry optimization algorithm was investigated and improved by introducing the chaotic mapping and Levy flight strategy to overcome the shortcomings, such as low convergence rate and easily falling into local optimum. Finally, the method proposed in this paper was validated by simulating open-source data from references and the obtained ones with a self-built experimental platform. The results show that the proposed method outperforms those by nonlinear least squares, particle swarm optimization and the original strawberryalgorithm in fitting the detected degree of polarization (DOP) data, indicating the modeling accuracy was significantly improved and better suited to characterize the polarized reflection properties of metal surfaces.
Mobile Ad hoc Network (MANET) is a popular mlobile network that facilitates more emergency services. However, achieving a high quality of service (QoS) and less energy consumption in MANETs remains a challenge owing t...
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Mobile Ad hoc Network (MANET) is a popular mlobile network that facilitates more emergency services. However, achieving a high quality of service (QoS) and less energy consumption in MANETs remains a challenge owing to their ad hoc nature. To address these shortcomings, this paper designed a stable zone-based 5G clustered MANET environment.. The integration of the MANET with 5G communication aims to yield a high data rate while reducing the latency and cost. This study comprises four major processes, namely, the stable zone-based clustering, Grey-Dematel (GD) gateway selection, adaptive buffer management, and interest-region-based routing. Primarily, mobile nodes are gathered to form a stable cluster, which is vital in frequent topology-changing environments such as MANETs by strawberry optimization algorithm. A gateway is selected to provide 5G multimedia access to MANET users, and the GD algorithm is used to select an optimum gateway (5G user) from among the nearby 5G users of the current CH. The adaptive buffer management (ABM) method is used to reduce congestion in the CH node. ABM manages the buffer through adaptive time division multiple access scheduling, where video files are prioritized over audio/text files. Interest-region-based routing is performed among the users of MANET to reduce the delay and loss. Furthermore, the discrete interval type 2 improved fuzzy function algorithm is used to discover an optimum path in the interest region, which is formed by the angle between the source and destination. The proposed method is implemented using Network Simulator 3.26 (NS3.26). The results indicate that the proposed method improves the packet delivery ratio and throughput metrics by 30% and reduces the end-to-end delay, jitter, and packet loss ratio by 35% when compared to existing methods such as AERP, MCQR, and M-AODV.
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