Research On Hybrid Algorithm Of Particle Swarm Optimization And Wolf Pack Algorithm And Its Application In WSN Network Coverage Optimization

The problems of optimization are widespread in all aspects of human society and can be solved by Optimization algorithm.The swarm intelligence algorithm is a biomimetic algorithm based on the social cooperative behavior of the biological group,and has been applied to the actual problem and achieved good results.Because of the different bionic objects,each algorithm has its own advantages,but also has its limitations.Hybrid algorithm is to integrate two or more optimization algorithms into a new algorithm according to certain rules.Practice has proved that the hybrid algorithm has better optimization effects in optimization problems such as traffic flow prediction,reservoir optimization scheduling,and drone cruise control.In this paper,the principles of Particle Swarm Optimization and Wolf Pack Algorithm are deeply studied,and their respective advantages and disadvantages are analyzed and summarized.Aiming at the shortcomings of single algorithm,three hybrid algorithms and an improved Particle Swarm Optimization are proposed.And one of the hybrid algorithms applied to the WSN network coverage optimization problem.The main research and innovation are included in the following several aspects.Firstly,a S-? PSO(Individual History Optimal Solution Coefficient Particle Swarm Optimization Based on Wolf Pack Update Mechanism and Siege Wolf Search)is proposed for solving single-objective numerical optimization problem.Based on the in-depth analysis of particle flight trajectory in Particle Swarm Optimization,the Individual History Optimal Solution Decrement Coefficient is designed,and a ? PSO(Individual History Optimal Solution Coefficient Particle Swarm Optimization)is proposed.And the stability of the improved algorithm is analyzed.The experimental results show that compared with the standard particle swarm optimization algorithm(PSO),? PSO has higher accuracy and less time cost in solving single peak problems.On the basis of ? PSO,the introduction of Wolf Pack Update Mechanism in Wolf Pack Algorithm enhances population diversity and algorithm expansion ability.It also embeds the Siege Wolf Search Operator in the Wolf Pack Algorithm,which enhances the global optimization ability and local optimization ability of the algorithm,enriching the optimization strategy.Experiments show that S-? PSO is better in optimizing accuracy,optimizing speed and the ability of jumping out of local optimum when facing most single-objective optimization problems.Secondly,a S-? MOPSO(Individual History Optimal Solution Coefficient Multi-objective Particle Swarm Optimization Based on Wolf Pack Update Mechanism and Siege Wolf Search)is proposed for solving multi-objective numerical optimization problem.The multi-objective optimization problem requires a set of solutions that enable each target to achieve a better solution.Therefore,the optimization algorithm for single-objective optimization problems cannot be directly used for multi-objective optimization problems.In this paper,based on the characteristics of multi-objective optimization problems,the S-? PSO operator is improved and a new operators are added to form a hybrid algorithm which is suitable for multi-objective optimization problems.Experiments show that S-? MOPSO has good optimization performance when faced with multi-objective optimization problems.Finally,based on the dual-population evolution strategy,a W-PSO(Hybrid algorithm for Particle Swarm Optimization and Wolf pack Algorithm sharing high quality individuals)is proposed and applied to the WSN network coverage problem.Simulation experiments show that W-PSO has good optimization performance in WSN network coverage problem.The experimental results show that the above three hybrid algorithms have good optimization quality and optimization efficiency when solving optimization problems.Among them,S-? PSO has good optimization precision,optimization speed and good local optimal ability when solving single-objective optimization problems.The Pareto optimal solution set obtained by S-? MOPSO in solving multi-objective optimization problems has a high degree of coverage and a more uniform distribution of true Pareto frontiers.For the optimization of the WSN network coverage,the average network coverage obtained by W-PSO experiment is 6.55% and 2.79% higher than that of PSO and WPA,and the optimal network coverage is 6.84% and 3.93% higher,respectively.Through the application of hybrid algorithm in WSN network coverage optimization problem,it expands the ideas and methods for optimization of microgrid optimal configuration and urban traffic route planning,which can bring certain economic and social benefits and help strengthen the national economy.