As the Internet of Things (IoT) devices and applications proliferate, it becomes increasingly important to design robust networks that can continue to meet user demands at a high level. Wireless local area networks (W...
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As the Internet of Things (IoT) devices and applications proliferate, it becomes increasingly important to design robust networks that can continue to meet user demands at a high level. Wireless local area networks (WLANs) can be a good choice as IoT infrastructure when high throughput is required. On the other hand, wireless mesh networks (WMNs), which are WLANs with mesh topology following the IEEE802.11s standard, have many advantages compared to conventional WLANs. Nevertheless, there are some problems that need solutions. One of them is the node placement problem. In this work, we propose and implement a hybrid intelligent system that solves this problem by determining the position of mesh nodes by maximizing the mesh connectivity and the coverage of IoT devices. The system is based on particle swarm optimization (PSO), simulated annealing (SA), and distributed genetic algorithm (DGA). We compare the performance of three router replacement methods: constriction method (CM), random inertia weight method (RIWM), and rational decrement of Vmax method (RDVM). The simulation results show that RIWM achieves better performance compared to CM and RDVM because it achieves the highest connectivity while covering more clients than the other two methods.
In this paper, a new optimization method, which is effective for the problems that the optimum solution should be searched in several solution spaces, is proposed. The proposed method is an extension of distributed Ge...
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In this paper, a new optimization method, which is effective for the problems that the optimum solution should be searched in several solution spaces, is proposed. The proposed method is an extension of distributed genetic algorithm (DGA), in which each sub-population searches a solution in the corresponding solution space. Through the competition between the sub-populations, population sizes are adequately and gradually changed. By the change of the population size, the appropriate sub-population attracts many individuals. The changing population size yield the efficient search for the problems of searching for solutions in multiple spaces. In order to evaluate the proposed method, it is applied to a polynomial curve fitting and signal source localization, in which the number of sources is preliminarily unknown. Simulation results show the effectiveness of the proposed method.
According to the actual situation of sports events,the physical strength distribution model and fault tolerant control model are *** to the principle of multiobjective optimization,the improved distributedgenetic alg...
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According to the actual situation of sports events,the physical strength distribution model and fault tolerant control model are *** to the principle of multiobjective optimization,the improved distributed genetic algorithm was used for preliminary solving,and the corresponding fault-tolerant control law was developed to correct the strategies with errors,so as to maintain a high fault-tolerant degree while ensuring the overall score of the event as best as *** geneticalgorithm has the characteristics of strong search ability and fast search speed,and the fault-tolerant control theory can fully combine with the practice to ensure the overall fault tolerance of the ***,the reliability and effectiveness of the algorithm are verified by simulation.
Concept learning is a computationally demanding task that involves searching large hypothesis spaces containing candidate descriptions. Stochastic search combined with parallel processing provide a promising approach ...
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