Fuzzy rule selection using artificial bee colony optimisation algorithm

Abstract

Wireless sensor network (WSN) brings an innovative model with embedded system with restrictions of computing ability, intercommunication, storage capacity, and energy resource which is applied for a high range of applications in the situations when constructing the network based on conventional infrastructure is not feasible. Clustering with WSN is a successful technique to reduce the rate of energy use of sensor node. The fuzzy logic calculates the cluster head (CH) selection probability depending on the node's earlier communication history to choose the CH. The set of rules applied to the fuzzified input is the fuzzy rule base. The output of the inference engine is changed to crisp output by defuzzification. Artificial bee colony (ABC) is an optimisation protocol owes its inspiration to the exploration behaviour of honey bees. It is a comparatively innovative optimisation algorithm which has proven to be on par with classical bio-inspired protocols. This work on ABC optimisation algorithm is suggested for selecting fuzzy rules. Rule selection methods combine different rules from fuzzy rule set to decrease the rules while maintaining the performance of the system. The rules that decrease the performance of the system are removed, to get a fuzzy rule set with improved performance.