A Novel Hybrid Prairie Dog Algorithm and Harris Hawks Algorithm for Resource Allocation of Wireless Networks

Enhancing the performance of wireless networks and communication systems requires careful resource allocation. Resource allocation optimization, however, is regarded as a mixed-integer non-linear programming (MINLP) problem, which is NP-hard and non-convex. Due to the serious limitations of conventional procedures, solving such optimization problems requires specialized approaches. For instance, no optimal performance can be guaranteed using the heuristic algorithms; besides, the global optimization systems suffer from exponential computation complexity and considerable training duration. This paper introduces an improved version of the Prairie dog optimization (PDO) algorithm by the Harris Hawks optimization (HHO) algorithm. The developed technique, namely HPDO, relies on using the HHO operators to improve the exploitation capability of PDO during the searching procedure. The significance of the presented HPDO is examined and analyzed using 23 mathematical benchmark functions and CEC-2019 with several dimension sizes to show the ability to solve different numerical problems. In addition to the resource allocation problem, the HPDO is evaluated using three engineering problems: The spring design issue, The pressure vessel design issue, and the Welded beam design issue. The experimental and simulation results demonstrated that the exploration and exploitation search method of HPDO and its convergence rate had remarkably increased. The experimental results of the resource allocation of the wireless network with different numbers of users 10, 50, and 100 achieve superior results compared to other algorithms with 0.136, 2.75, and 3.64, respectively.The results showed the supremacy of the HPDO over the traditional HHO, PDO, and several with state-of-the-art algorithms.