Application of Diversity-Driven Multi-Parent Evolutionary Algorithm in WSN

This chapter presents a Diversity-Driven Multi-Parent Evolutionary Algorithm with Adaptive Non-Uniform Mutation (DDMPEA-ANUM) for efficient cluster head (CH) selection in heterogeneous wireless sensor networks (HWSNs). Addressing energy limitations and varying node capabilities in wireless sensor networks (WSNs), the protocol optimizes network lifetime and data reliability. Evaluated and compared with existing approaches like GAOC, GADA-LEACH, TEDRP, and DCH-GA, DDMPEA-ANUM shows superior performance across network lifetime, stability period, half-node dead (HND) count, and residual energy. DDMPEA-ANUM achieved 6.88%, 22.71%, 13.52%, and 42.57% improvement in network lifetime compared to GAOC, GADA-LEACH, TEDRP, and DCH-GA, respectively. The findings showcase DDMPEA-ANUM's effectiveness in balancing energy consumption, promoting network longevity, and ensuring reliable transmission in HWSNs. The adaptive features and multi-parent crossover facilitate efficient solution exploration. The protocol offers a practical solution for deploying robust and energy-efficient WSNs in real-world applications, offering a promising alternative to existing CH selection strategies.