Multi-sensor Task Assignment Based on Simulated Annealing Genetic Algorithm

Wu, Yong; Zhang, Yijie; Tian, Haibao; Shi, Guangming; Yue, Longfei

doi:10.1109/icca.2019.8900027

Cited by 2 publications

(2 citation statements)

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“…If these parameters are not properly set, there is a possibility of deviating from achieving optimal solutions. In paper [25], an optimization algorithm is introduced, utilizing the Simulated Annealing Genetic Algorithm (SAGA), designed to address the complexities of multi-objective and multi-sensor configurations. The algorithm proceeds in several steps: firstly, it quantifies the spatial information perception capability of sensors using the Analytic Hierarchy Process (AHP); secondly, it calculates the priority of the target; and lastly, the SAGA is employed for task assignment.…”

Section: Related Workmentioning

confidence: 99%

“…In Equations ( 24) and ( 25), in combination with the task allocation problem in IUWSNs, Pc min and Pc max are set to 0.6 and 0.8, respectively, and Pm min and Pm max are set to 0.01 and 0.08, respectively. Therefore, the range of crossover probability and variation probability can be calculated as 0.6 to 0.8 and 0.01 to 0.08, respectively, according to Equations ( 24) and (25). Its parameters are set in Table 3.…”

Section: Experimental Environment and Parametersmentioning

confidence: 99%

See 1 more Smart Citation

A Novel Chaotic Elite Adaptive Genetic Algorithm for Task Allocation of Intelligent Unmanned Wireless Sensor Networks

Fei,

Zhang,

Liu

et al. 2023

Applied Sciences

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In recent times, the progress of Intelligent Unmanned Wireless Sensor Networks (IUWSNs) has inspired scientists to develop inventive task allocation algorithms. These efficient techniques serve as robust stochastic optimization methods, aimed at maximizing revenue for the network’s objectives. However, with the increase in sensor numbers, the computation time for addressing the challenge grows exponentially. To tackle the task allocation issue in IUWSNs, this paper introduces a novel approach: the Chaotic Elite Adaptive Genetic Algorithm (CEAGA). The optimization problem is formulated as an NP-complete integer programming challenge. Innovative elite and chaotic operators have been devised to expedite convergence and unveil the overall optimal solution. By merging the strengths of genetic algorithms with these new elite and chaotic operators, the CEAGA optimizes task allocation in IUWSNs. Through simulation experiments, we compare the CEAGA with other methods—Hybrid Genetic Algorithm (HGA), Multi-objective Binary Particle Swarm Optimization (MBPSO), and Improved Simulated Annealing (ISA)—in terms of task allocation performance. The results compellingly demonstrate that the CEAGA outperforms the other approaches in network revenue terms.

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Section: Related Workmentioning

confidence: 99%

Section: Experimental Environment and Parametersmentioning

confidence: 99%