Whereas the imperialist competitive algorithm (ICA) shows limited global search ability and be liable to be trapped into local optimum, a double-assimilation of prosperity and destruction oriented improved imperialist...
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ISBN:
(数字)9781665467087
ISBN:
(纸本)9781665467087
Whereas the imperialist competitive algorithm (ICA) shows limited global search ability and be liable to be trapped into local optimum, a double-assimilation of prosperity and destruction oriented improved imperialist competitive algorithm (DPDO-IICA) is proposed tentatively to overcome inherent defects. The imperialist assimilation and colonial reform strategy are customized purposefully, and a novel population redistribution mechanism is introduced as well. The three improvement measures are supposed to further promote population diversity and searching accuracy. The CEC2017 test set is selected to verify the performance of the DPDO-IICA by the different types of numerical function problems with the different dimensions. Moreover, the DPDO-IICA is compared with the three first-class intelligent optimization algorithms, which have achieved significant rankings in the CEC2017 competition. The comparison shows that the DPDO-IICA has good performances, which is demonstrated by the accuracy and stability. In addition, the proportion of imperialists and colonies is investigated, and it is through the community partitioning and clustering dynamically to enhance the population diversity. In conclusion, the DPDO-IICA can effectively improve the ability of global exploration and avoid premature convergence in comparison with the original ICA.
The optimal quantization of output binary-input discrete memoryless channels is considered, whereby the optimal quantizer preserves at least a constant alpha-fraction of the original mutual information, with the small...
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The optimal quantization of output binary-input discrete memoryless channels is considered, whereby the optimal quantizer preserves at least a constant alpha-fraction of the original mutual information, with the smallest output cardinality. Two recursive methods with top-down and bottom-up approaches are developed;these methods lead to a new necessary condition for the recursive quantizer design. An efficient algorithm with linear complexity, based on dynamic programming and the new necessary optimality condition, is proposed.
Recently, evolving technologies such as 5G and cloud computing have offered new prospects in mobile ad hoc networks (MANETs). However, achieving a high quality of service (QoS) in multimedia routing over MANET-cloud u...
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Recently, evolving technologies such as 5G and cloud computing have offered new prospects in mobile ad hoc networks (MANETs). However, achieving a high quality of service (QoS) in multimedia routing over MANET-cloud using 5G networks remains challenging owing to the dynamic nature of mobile devices. The present study addresses this problem by proposing a three-tier architecture in cloud-assisted MANETs with 5G (TCM5G) communication. The network comprises the MANET, cloudlet, and cloud tiers. In the proposed scheme, partitioning and clustering are performed to optimize the cluster size. Specifically, partitioning is first performed by the improved monarch butterfly optimization algorithm. Here, the cluster head (CH) is first selected by computing the importance rate. The selected CH then forms a cluster around itself by broadcasting its selection message. Device-to-device (D2D) communication is established using the Kuhn-Munkres algorithm, which determines the optimal device for each D2D communication in the network in order to increase data transmission efficiency. Network performance depends on effective routing;hence, we considered two routing types: inter-cluster and cloudlet. The former is performed through chaotic flower pollination, and the latter is achieved by using the improved Type-2 Takagi Sugeno fuzzy algorithm. To improve the QoS in multimedia routing, we employ a full-interpolation, scalable video-coding algorithm for effective multimedia data encoding. Task offloading among cloudlets is performed based on a load criterion to balance the cloudlet load. The performance of the proposed system is evaluated using five metrics: throughput, packet delivery ratio (PDR), end-to-end delay, task-completion time, and bandwidth consumption. The results demonstrate that in comparison with the existing DCRM, PCA, and HRM methods, the proposed TCM5G scheme enhances throughput and PDR by 30%, and reduces end-to-end delay, task-completion time, and bandwidth consumptio
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