An Analysis of Binary Particle Swarm Optimizers for Task Assigning Problem in Wireless Sensor Networks

The tightly restricted resource in wireless sensors networks (WSN) makes it challenging to schedule the task assignment for better performance. Binary particle swarm optimizers (BPSO) along with its modified version (MBPSO) have shown promising performance to this problem, but premature convergence remains a key issue. To improve performance of BPSO for task assigning in WSN, this paper first develops various extended BPSOs by using different topologies and the comprehensive learning strategy. An integrated comparison among these candidate approaches and the MBPSO is carried out. In addition, the choice of transfer function highly affects the global optimizing ability of BPSO. Thus the significance of transfer functions with different shapes adopted in BPSO is discussed. Through sufficient simulations and analysis, it is found that the BPSO with the comprehensive learning strategy and a V-shaped transfer function is very promising, especially toward large-scale problems.