作者:
Wu, MingmingWuhu Univ
Sch Automot Engn & Intelligent Mfg Wuhu 241000 Peoples R China
The goal of management optimization scheduling in manufacturing plants is to improve machining efficiency and reduce costs, which is one of the research hotspots in the current era. The study proposed a particle swarm...
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The goal of management optimization scheduling in manufacturing plants is to improve machining efficiency and reduce costs, which is one of the research hotspots in the current era. The study proposed a particle swarm optimization algorithm incorporating the frog-leaping algorithm, which combined the grouping mechanism with the global search to improve the search speed of the algorithm, and also incorporated the mutation and crossover ideas of the genetic algorithm. To enhance the machining efficiency while minimizing time and resource requirements, two buffering mechanisms were used for the machining process in this algorithm. The algorithm ultimately achieved the optimal solution of 1170 around the 10th generation, according to experiments, which reduced the maximum machining time by 22%, the average production cycle time by 23%, the machine utilization rate reached 69%, and the percentage of the optimal relative error was almost less than 4%. Additionally, the algorithm's average relative error fluctuation is less than that of the other algorithms, indicating that this algorithm is more stable. This result shows that the particle swarm optimization algorithm incorporating the frog hopping algorithm in this study has good practical value in optimizing the scheduling of production management in manufacturing workshops for improving the processing efficiency and reducing the cost, which is beneficial to the development of the manufacturing industry.
Routing in Unmanned Aerial Vehicle (UAV) networks is critical for effective data transfer and overall network performance. However, current UAV routing algorithms exhibit high latency, poor route selection, excessive ...
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Routing in Unmanned Aerial Vehicle (UAV) networks is critical for effective data transfer and overall network performance. However, current UAV routing algorithms exhibit high latency, poor route selection, excessive energy consumption, and limited flexibility in changing network topologies. To overcome these limitations, this paper proposes a new routing strategy that uses the Shuffled frogleapingalgorithm (SFLA) to improve UAV network routing. Using a two-phase optimization approach considering Quality of Service (QoS), our system combines global exploration with local exploitation, unlike previous techniques. This hybrid method enables UAVs to dynamically change their trajectories, helping to choose the best path even in fast-changing surroundings. Our approach's self-adaptive population-based search mechanism accelerates convergence and removes a common weakness in traditional metaheuristic algorithms-premature standstill elimination-which determines its effectiveness. By constantly adjusting UAV routing patterns depending on energy economy, latency, and throughput characteristics, SFLA guarantees that UAV networks transmit effectively and consistently. Based on experimental data, our method outperforms benchmark alternatives in terms of energy use by 3.11%, latency by 5.14%, and network lifetime by 2.25%. These developments make our approach ideal for real-time applications including aerial surveillance and disaster response that call for high data transfer speeds and great energy economy.
Prompt and reliable communication between vehicular nodes are essential as its limited coverage and dynamic mobility rate introduces frequent change of network topology. The key feature of vehicular communication that...
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Prompt and reliable communication between vehicular nodes are essential as its limited coverage and dynamic mobility rate introduces frequent change of network topology. The key feature of vehicular communication that establishes direct connectivity or Road Side Unit-based data transfer among vehicular nodes is responsible for sharing emergency information during critical situations. Multicast routing data dissemination among vehicular nodes is considered to be the potential method of parallel data transfer as they facilitate the option of determining an optimal multicast tree from feasible number of multicast trees established between the source and destinations. This estimation of optimal multicast tree using meta-heuristic techniques is confirmed to improve the throughput and reliability of the network when QoS-based constraints are imposed during multicast routing. An Improved Shuffled frog-leaping algorithm-Based QoS Constrained Multicast Routing (ISFLABMR) is proposed for estimating an optimal multicast tree that confirms effective multi-constrained applied multicast routing between vehicular nodes. ISFLABMR minimizes the cost of transmission to 22% by reducing the number of multicast clusters formed during multicasting through the utilization of local and global-based optimizations. The simulation results of ISFLABMR proveits predominant reduction rate of 24% and 21% in average packet latency and energy consumptions incurred under multicast routing.
In this article, a microhabitat frog-leaping algorithm is proposed based on original shuffled frog-leaping algorithm and effective independence method to make the algorithm more efficient to optimize the 3-axis accele...
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In this article, a microhabitat frog-leaping algorithm is proposed based on original shuffled frog-leaping algorithm and effective independence method to make the algorithm more efficient to optimize the 3-axis acceleration sensor configuration in the vibration test of structural health monitoring. Optimal sensor placement is a vital component of vibration test in structural health monitoring technique. Acceleration sensors should be placed such that all of the important information is collected. The resulting sensor configuration should be optimal such that the testing resources are saved. In addition, sensor configuration should be calculated automatically to facilitate engineers. However, most of the previous methods focus on the sensor placement of 1-axis sensors. Then, the 3-axis acceleration sensors are calculated by the method of 1-axis sensors, which results in non-optimal placement of many 3-axis acceleration sensors. Moreover, the calculation precisions and efficiencies of most of the previous methods cannot meet the requirement of practical engineering. In this work, the microhabitat frog-leaping algorithm is proposed to solve the optimal sensor placement problems of 3-axis acceleration sensors. The computation precision and efficiency are improved by microhabitat frog-leaping algorithm. Finally, microhabitat frog-leaping algorithm is applied and compared with other algorithms using Dalian South Bay Cross-sea Bridge.
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