Hybrid of Ant Colony and Artificial Bee Colony Optimization for Resource Allocation in Cognitive Radio Networks

The resource allocation in heterogeneous cognitive radio networks (CRN) system has been considered a key technology in wireless networks. However, the main challenge of the CRN system is resource allocation with minimized and maximum transmission in the spectrum band, enhancing optimum throughput increase, load balance and global optimization. The proposed method is hybrid optimization, comprising Ant Colony Optimization (ACO) and Artificial Bee Colony (ABC), allocating resources while considering power, throughput and equal to distributed the allocation. The load balancing technique should ensure virtual independent task scheduling approaches of the hybrid optimization and optimize scheduling and resource utilization to maximize throughput and create load balancing. Performance analysis of the proposed method was compared using resource allocation with existing load balancing and scheduling algorithms. Although the proposed ACO-ABC method exhibits high performance compared to existing methods such as Dynamic Fuzzy-based Primary User Aware Clustering (DFPC), non-orthogonal multiple access (NOMA) and Hybrid Tabu Search-based Stimulated Algorithm (HTSA), experimental results demonstrate high performance achieving a Packet Delivery Ratio (PDR) of 97%, Delay of 0.009s, Energy consumed of 0.01 (nJ), and Throughput of 2.0 (Mpbs).