A novel swarm intelligence optimization method for efficient task allocation in industrial wireless sensor networks.

Industrial wireless sensor networks (IWSNs) play an increasingly important role in digital and intelligent industries; however, the communication, computing, and energy performance of their nodes are constrained by the demands and cost constraints of large-scale deployment. In this case, efficient task allocation plays a key role in improving the performance of the IWSNs. Task allocation in IWSNs is a nondeterministic polynomial (NP) hard problem whose complexity increases with an increase in node and task size. In this study, chaotic elite clone particle swarm optimization (CECPSO) was proposed. The algorithm first introduces the chaos theory to optimize the initial population. Subsequently, an elite cloning strategy was designed, which not only accelerated the exploration of the solution space and improved the accuracy of the solution but also avoided the problem of falling into the local optimal solution in the early stage through the dynamic adjustment strategy. In addition, the algorithm employs an exponential nonlinear decreasing inertia weight function that balances local and global search capabilities. By comparing the performance of CECPSO, Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Simulated Annealing (SA) in different experimental scenarios, we found that CECPSO is superior to PSO, GA, and SA in terms of the convergence rate and overall performance. Under the conditions of 40 sensors and 240 tasks, CECPSO's performance improvement of CECPSO relative to PSO, GA, and SA reached 6.6%, 21.23%, and 17.01%, respectively. Experimental results show that the proposed algorithm can effectively improve the overall performance of IWSNs.