Studies And Application Of The Optimization Algorithm For Invasive Weeds

Intelligent optimization algorithm is an efficient and practical optimization method that emerged in nearly half a century.It is widely used in many fields because of its robustness,fast convergence and high quality solution.The main research content of this paper is the new member intrusion weed optimization algorithm in the intelligent optimization algorithm,which is widely concerned by many scholars because of its easy to understand biological basis,strong stability and outstanding global search ability.However,due to the short time proposed by the algorithm,the development has not yet fully developed,there are still some shortcomings,mainly in the slow convergence of the algorithm,easy to fall into the local optimum and affect the algorithm of the final optimization accuracy and so on.Especially when it solves the problem of non-convex or more constraints,its convergence rate is especially slow.In view of these problems,this paper,through collecting relevant data both at home and abroad,deeply studies the theoretical basis and practical application of standard invasion weed optimization algorithm,and carries out the following four major operations:population initialization,population multiplication,spatial diffusion and competitive preference Improve,and apply the improved algorithm to the two-dimensional rectangle to load such complex NP-Hard problems.The main research work of this paper is as follows:(1)The following three improvement methods are proposed for the four major operations of initialization,propagation,evolution,spatial diffusion and competitive selection of the standard invasion weed optimization algorithm: First,chaos theory is introduced in the initial stage,the initial Chebyshev-Logistic hybrid chaotic system is used to initialize the population to improve the initial population diversity;secondly,a hierarchical group strategy is proposed to classify the initial population and simulate the scoring mechanism in student evaluation to divide the population into four subgroups of "excellent","good","medium" and "bad";thirdly,in the stage of population breeding and evolution,the differential evolution model is introduced,and the crossover and mutation probability in the original model are exponentially adjusted nonlinearly to improve the ability of the algorithm to jump out of the local optimum.Simulation results carried out on standard test functions 8 show that compared with the standard algorithm and othercommonly IWO improved algorithm proposed haves a faster convergence speed and higher precision of optimization,while most effectively avoid local excellent.(2)The improved algorithm is applied to the practical problems: the first time we use intrusion weed optimization algorithm to solve the problem of two-dimensional rectangle loading.In the experiment of loading two-dimensional rectangular pieces,we use the improved weed optimization algorithm in this paper,which has good effect and has the follow-up research value.