Improvement Of Spider Monkey Algorithm And Its Application In Logistics Center Location Problem

As an emerging search algorithm,swarm intelligence optimization algorithm opens a new way to effectively solve large-scale complex optimization problems,which has attracted the attention of many scholars.The spider monkey optimization algorithm arises by simulating the foraging behavior of spider monkeys.Based on the characteristics of fission fusion social structure,the algorithm has become more and more popular,in particular,it has certain advantages in solving high-dimensional optimization problems.Based on the research of the basic spider monkey algorithm,two improved algorithms are proposed,and the logistics center location problem that is closely related to life is solved.The main research contents of this thesis are as follows:1.An adaptive spider monkey algorithm based on Cauchy mutation operator was proposed and used to solve the general logistics center location problem.The phase factor based on the number of iterations is used to replace the random number,so that the phase factor decreases with the increase of the number of iterations and the phase factor is larger at the initial stage of the iterations,which is conducive to global exploration,and is smaller at the later stage of the iteration,which can be fully developed locally and improve the randomness of the algorithm and the optimization performance.The learning factor of non-linear dynamic transformation is added during the global leader phase,making the spider monkey's position update adaptive,further balancing the global exploration and local searching capabilities of the algorithm.The Cauchy mutation strategy is used in the local leader decision phase to disturb the spider monkey,increasing the diversity of the population,and avoiding the algorithm falling into a local optimum in the later stage.In simulation,the benchmark test functions are selected for testing and compared with other intelligent algorithms.Simulation results show that the proposed algorithm has significantly improved solution accuracy and better performance.The improved algorithm is used to solve general logistics center location problems and the results show that the new algorithm has better optimization ability,which illustrates the effectiveness of the above algorithm.2.A quasi-opposition spider monkey algorithm based on Laplace distribution is given to solve the emergency logistics location problem.During the initialization of the algorithm,a random number generated by the Laplace distribution is used to initialize the swarm,which made the distribution of the spider monkeys more uniform and improved the quality of the particles.The step size of the exponentially decreasing and random logarithmic decreasing is used to make the step adaptive.The step size is kept larger in the early stage of iteration,which accelerates the convergence speed and balances the relationship between search speed and optimization accuracy effectively.Changed the search mechanism in the global leadership stage,speeding up the spider monkey to move closer to the global leader for foraging food.In the local leader decision phase,a quasi-opposition learning strategy is introduced,and elite selection is performed from the current population and the pseudo-reverse population to effectively find the optimal solution.Finally,through the numerical simulation of classic test functions and the solution of the emergency logistics location problem,the experimental results show that the new improved algorithm has the best solution,greater optimization ability and superiority.