An Improved Adaptive Clone Genetic Algorithm for Task Allocation Optimization in ITWSNs

Abstract

Research on intelligent transportation wireless sensor networks (ITWSNs) plays a very important role in an intelligent transportation system. ITWSNs deploy high-yield and low-energy-consumption traffic remote sensing sensor nodes with complex traffic parameter coordination on both sides of the road and use the self-organizing capabilities of each node to automatically establish the entire network. In the large-scale self-organization process, the importance of tasks undertaken by each node is different. It is not difficult to prove that the task allocation of traffic remote sensing sensors is an NP-hard problem, and an efficient task allocation strategy is necessary for the ITWSNs. This paper proposes an improved adaptive clone genetic algorithm (IACGA) to solve the problem of task allocation in ITWSNs. The algorithm uses a clonal expansion operator to speed up the convergence rate and uses an adaptive operator to improve the global search capability. To verify the performance of the IACGA for task allocation optimization in ITWSNs, the algorithm is compared with the elite genetic algorithm (EGA), the simulated annealing (SA), and the shuffled frog leaping algorithm (SFLA). The simulation results show that the execution performance of the IACGA is higher than EGA, SA, and SFLA. Moreover, the convergence speed of the IACGA is faster. In addition, the revenue of ITWSNs using IACGA is higher than those of EGA, SA, and SFLA. Therefore, the proposed algorithm can effectively improve the revenue of the entire ITWSN system.

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

“…For ease of understanding, the main findings and limitations of the relevant work in this article should be summarized in the following Table 1. This paper introduces a task allocation model [27,28] aimed at maximizing network revenue within Intelligent Unmanned Wireless Sensor Networks (IUWSNs). This model factors in the impact of IUWSNs on routing energy consumption.…”

Sensors, Signal, and Artificial Intelligent Processing

“…This method deploys remote sensing sensor nodes within high yield and low energy consumption for complex traffic parameter coordination. Z. Zha et al [4] have proposed a modified clone genetic algorithm with adaptive ability to solve task allocation in ITWSNs. It employs operator of clonal expansion to speed up the convergence rat, while adaptive operator is updated to improve the global search capability.…”

UAV Task Allocation Based on Clone Selection Algorithm

“…At the same time, CSA can be used to solve multiobjective problems. Comparing CSA with the GA [19][20][21], the main difference is the way the population evolves. In the GA, the population evolves through crossover and mutation, and in the CSA, cell reproduction is asexual, with each offspring produced by one cell being an exact copy of its parents, and mutation and selection are made through these offspring.…”