Research On Lion Swarm Reinforcement Search Algorithm And Its Application

With the rapid development of science and technology,the theory and application research of wireless communication and information processing have been widely concerned by researchers.Faced with some complex optimization problems,when traditional methods can not get better results,some researchers are inspired by the laws of nature and biological systems,and design some heuristic intelligent computing methods-intelligent optimization algorithm.Swarm intelligence algorithm is a large class of intelligent algorithms that simulate the behavior of biological population.As the top animal in the food chain,lions are good at cooperative hunting.In recent years,swarm intelligence algorithm to simulate the behavior of lions has been studied by many scholars.In order to solve these two problems,this thesis proposes two lion swarm reinforcement search algorithms and applies them to 3D DV-hop localization and spectrum sensing.Lion swarm optimization algorithm is a new swarm intelligence algorithm which simulates lion king guarding,lioness hunting and cubs following.Compared with the classical particle swarm optimization algorithm,the mechanism of lion swarm optimization algorithm is more flexible and the communication mode is more diverse.Lion swarm optimization algorithm has fast convergence speed,but it is found that lions are easily out of bounds when the range of activity is large and the position update formulas are not universal,which affect the performance of the algorithm.Driven by the problems and application of lion swarm optimization algorithm,this thesis proposes a three-dimensional DV-hop localization method based on improved lion swarm optimization algorithm,which realizes the solution of unknown node coordinates.Firstly,the idea of sheep interaction in sheep optimization algorithm and the idea of wolf hunting in grey wolf optimizer are used to improve the lion swarm optimization algorithm,and the improved lion swarm optimization algorithm is obtained.Then the performance of the improved lion swarm optimization algorithm is simply tested.Finally,the improved lion swarm optimization algorithm is applied to the optimization of unknown node coordinates in three-dimensional DV-hop positioning.The simulation results show that,compared with the classical three-dimensional DV-hop algorithm and the three-dimensional DV-hop algorithm based on the original lion swarm optimization algorithm,the proposed algorithm has higher positioning accuracy and higher stability.Aiming at the existing problems and application requirements of lion swarm optimization algorithm,this thesis proposes a linear cooperative spectrum sensing method based on lion swarm optimization by reinforcement pattern search,and realizes the optimal solution of weight vector.Inspired by the one-dimensional normal distribution of lions,a modified lion swarm optimization algorithm is proposed to solve the existing problems.In order to improve the convergence accuracy and the convergence speed of the modified lion swarm optimization algorithm,a reinforcement pattern search algorithm with low complexity is proposed by combining Q-learning and pattern search,which is used to update the Lion King's position,and lion swarm optimization by reinforcement pattern search is proposed.The experimental results on CEC2013 test function set show that compared with the original lion swarm optimization algorithm,particle swarm optimization algorithm,Gaussian bare bones particle swarm optimization algorithm and modified lion swarm optimization algorithm,the lion swarm optimization by reinforcement pattern search has better performance,higher accuracy and faster convergence speed.The modified lion swarm optimization algorithm also has its advantages in high-dimensional optimization.Finally,the proposed lion swarm optimization by reinforcement pattern search is applied to the parameter optimization of the weight vector in the linear cooperative spectrum sensing model.The simulation results show that the lion swarm optimization by reinforcement pattern search can solve the parameter optimization problem of spectrum sensing better,and its performance is better than particle swarm optimization algorithm,Gaussian bare bones particle swarm optimization algorithm and modified lion swarm optimization algorithm.When there are many cognitive users,the relative performance of the modified lion swarm optimization algorithm is better.