Wireless sensor networks for task allocation using clone chaotic artificial bee colony algorithm

Wireless sensor network (WSN) is a promising technique adopted in many fields of geographical detecting, military use, automotive for target detection, tracking and a number of other aspects. Task allocation has always been an important aspect of the research of wireless sensor networks. In this paper, we will adopt a clone chaotic artificial bee colony algorithm (CCABCA) to solve the task allocation problem in WSNs. The CCABCA has positive aspects of both the better performance of chaotic generator and the convergence ability of the clone operator. Numerical simulations are conducted with CCABCA, grey wolf optimization (GWO), ant colony optimization (ACO) and simulated annealing (SA) and the results are compared to verify the proposed scheme. In simulations, the CCABCA technique has a better performance than GWO, ACO and SA under different conditions, especially for WSNs that have large quantity of nodes and tasks. In addition, the clone operator strategy improves the performance to prevent premature convergence.