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Quantum Particle Swarm Optimization Technique for Load Balancing in Cloud Computing
Published 2013“…However, these techniques have its weaknesses in terms of minimizing makespan, computation cost and communication cost. In this study, load balancing technique in Cloud Computing called Quantum Particle Swarm Optimization (QPSO) technique proposed by considering only minimization of makespan, computation cost and communication cost. …”
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RFID Network Planning of Smart Factory Based on Swarm Intelligent Optimization Algorithm: A Review
Published 2024“…The RNP problem needs to consider multiple objectives and constraints such as coverage, conflicts, economic benefit, and load balance which have been proven to be optimized by swarm intelligent optimization algorithms. …”
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On spectral efficiency maximization in a partial joint processing system using a multi-start particle swarm optimization algorithm
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Performance Analysis of Swarm Intelligence-Based Routing Protocol for Mobile Ad Hoc Network and Wireless Mesh Networks
Published 2009“…Ant colonies reside in social insect societies and maintain distributed systems that present a highly structured social organization despite of the simplicity of their individuals. Ants’ algorithm belongs to the Swarm Intelligence (SI), which is proposed to find the shortest path. …”
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Optimization of supply chain management by simulation based RFID with XBEE Network
Published 2015“…Among the problems associated with RFID technology are the multi-objective optimizations, which include tags coverage, economic efficiency, interference and load balance. In order to solve this problem, a simulation based “Multi-Colony Global Particle Swarm Optimization (MC-GPSO)” algorithm was developed. …”
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Digital Quran With Storage Optimization Through Duplication Handling And Compressed Sparse Matrix Method
Published 2024thesis::doctoral thesis -
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Lightweight ontology architecture for QoS aware service discovery and semantic CoAP data management in heterogeneous IoT environment
Published 2026“…Experimental results demonstrate that the proposed architecture outperforms existing methods in terms of response time, service delay, execution time, precision, recall, and F-score under varying CoAP request loads and communication overheads. The results confirm the effectiveness of the proposed lightweight ontology architecture for service discovery and data management in heterogeneous IoT environments.…”
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