Optimization Research And Application Of Wolf Pack Algorithm

Optimization issues have always played an important role in areas such as engineering and scientific research,and people have never stopped studying the solutions to optimization problems.However,with the improvement of the complexity of system engineering problems and the emergence of a large number of nonlinear combinatorial optimization problems,the method of solving the optimization problem is also improved.Inspired by the biological hunting behavior in nature,bionic Intelligent algorithm began to appear slowly in front of the world,the most typical bionic intelligent algorithm is Genetic Algorithm(Genetic Algorithm,GA),Simulated Annealing algorithm(Simulated Annealing,SA),Particle Swarm Optimization algorithm(Particle Swarm Optimization,PSO),Ant Colony Optimization(Ant Colony Optimization,ACO),and so on,and some algorithms are now very mature,especially in solving the optimization problem is particularly prominent,and has been applied in the related field,the Wolf Pack algorithm(Wolf Pack Algorithm,WPA)introduced in this paper is also a kind of bionic intelligent algorithm,because it shows good performance in solving complex problems compared with other bionic intelligent algorithms.Therefore,it has also been paid close attention by many experts and scholars at home and abroad.The Wolf Colony algorithm(Wolf Colony Algorithm,WCA)was originally a swarm intelligent algorithm proposed by Liu et al.based on the study of wolves 'hunting behavior,and in 2013,Wu Husheng and others abstracted three intelligent behaviors of artificial wolves based on the characteristics of Wolves ' Division of labor and cooperative hunting and the distribution of prey(That is,the walking behavior,summoning behavior and siege behavior)and two kinds of intelligent rules(that is,"the winner is the King" of the head Wolf generation rules and Darwin's theory of biological evolution of the "survival of the Fittest" Wolf Pack update Rules),proposed a new Wolf Pack algorithm(Wolf Pack algorithm),The convergence of WPA algorithm is proved based on Markov chain theory.Although the WPA has been in existence for less than 5years,it has been used in many fields because of its good performance,such as UAVtrack planning,optimization arrangement of three-dimensional sensors and optimal dispatching of hydropower station reservoirs,and also better shows that the WPA has a good prospect.However,the algorithm also has many shortcomings,such as long time,low convergence accuracy,too many parameters and easy to fall into the local optimal and other shortcomings,so that the further development of the WPA has been hindered.This topic mainly takes the WPA as the research object,and aims at the disadvantage that the WPA is easy to fall into the local optimal,low precision and slow convergence speed,and finally fuses the genetic algorithm and the WPA,and applies it to solve the Travelling Salesman Problem(TSP).The specific research contents are as follows:Firstly,in this paper,a Wolf Pack algorithm based on Gaussian perturbation and chaotic initialization(GCWPA)is proposed.This algorithm cites the chaotic cubic mapping to initialize the wolf pack,which makes the wolf pack more uniform in the search space,and is advantageous to the Wolf Pack to the solution space coverage;Through the Gaussian perturbation of the head wolf,the algorithm has the ability to jump out of the local optimum,and the simulation results show that the improved algorithm has good optimization performance.Secondly,a hybrid Wolf Pack algorithm(GASAWPA)based on crossover mutation operator and Simulated Annealing is proposed,based on the WPA,the crossover and mutation operation in Genetic algorithm is introduced,and the population diversity is increased,and the global search ability of the algorithm is enhanced,and after the siege behavior,By adding the Metropolis discriminative criterion in the Simulated annealing algorithm,local optimization is carried out near the wolves,and the simulation results show that the improved algorithm is more obvious than the WPA,compared with DWPA,although it can find the optimal value,but its stability is relatively poor,and the GASAWPA is more stable.Then the improved algorithm is applied to solve the TSP,and two typical TSP examples are selected to simulate the experiment,which proves that the improved algorithm has good performance.