Multiobjective cooperative swarm intelligence algorithm for uplink resource allocation in LTE‐A networks

Nowadays, Long‐Term Evolution Advanced (LTE‐A) network is the primary innovation in 4G networks. The LTE‐A networks convey exceptional data rates and low latency for few sorts of application. Sometimes, in LTE‐A, the multiobjective uplink resource allocation is the perplexing optimization problem, and this problem is considered as 0‐1 multiobjective knapsack trouble. In order to overcome this trouble, a multiobjective cooperative swarm intelligence algorithm for resource allocation is proposed in this paper. In our proposed work, hybrid firefly algorithm (FFA) and particle swarm optimization (PSO) algorithm are utilized to solve 0‐1 multiobjective knapsack problem. Initially, a priority and urgency factor (urgency of packets)–based user ranking and quantifying scheme is designed for scheduling process. After the scheduling process, the resource allocations employ three objective functions, such as maximization of resource utilization, maximization of quality of service (QoS), and interference minimization. The optimization problem is overcome by using the hybrid FFA and PSO algorithm. The experimental outcomes demonstrate that it has the best QoS and less interference in the resource allocation in LTE‐A network than state‐of‐art methods in the proposed strategy. In this context, the proposed method adopted a cooperative swarm intelligence algorithm for the uplink resource allocation in the LTE‐A network. At the time of allocating the resources in the LTE‐A network, some optimization problems arise; this optimization problem is considered as the multiobjective 0‐1 knapsack problem. To solve this problem, the remarkable ability of metaheuristic technique is used. In our work, problem is avoided by adopting the FFA‐PSO algorithm in the proposed method.