Improvement And Application Of Invading Weeds Algorithm

With the development of the era,intelligent algorithm has been applied to more and more fields in recent years.Because it is different from the traditional optimization algorithm,it has many advantages,such as strong robustness,fast convergence and high solution quality.Therefore,the further research and development of intelligent optimization algorithm is very important.In this paper,we mainly study the improvement and application of the optimization algorithm for invading weeds.First,the intrusion weed optimization algorithm is analyzed and improved to improve the algorithm's shortcomings and improve the algorithm's performance.Then it is applied to the function optimization problem and TSP problem to solve and determine the parameters of the aquifer system.The main contents are as follows:1.An improved intrusion weeds algorithm(IIWO)is proposed to solve the problem of low optimization precision in the intrusion weed algorithm.The new algorithm takes every seed of every weed as a niche,and performs the operation of the learning operator guided by the compound method for every niche,so as to improve the optimal solution of the algorithm.In order to verify the search efficiency of the algorithm,the algorithm is applied to the problem of multi-dimensional function optimization.The experimental results show that the new algorithm is better in the optimization efficiency,the stability and the robustness of the solution.2.Weeds are divided into 4 subgroups,and a multi subgroup invading weeds algorithm(NIWO)is proposed.The new algorithm overcomes the problem that the convergence speed of the intrusion weed algorithm is slow and the local optimum is easy to be trapped.The new algorithm is applied to solve the TSP problem and the experimental comparison is made.The improved invasive weed algorithm is superior to the original algorithm in terms of optimization efficiency,quality and robustness of the solution.3.A local search algorithm is added to the invasive weed optimization algorithm,and the hybrid intrusion weed optimization algorithm--IWO-SQA algorithm is proposed.The algorithm is used to determine the parameters of the first kind of cross flow system model.In this algorithm,the IWO algorithm can play a global algorithm for global search,two simplified approximation algorithm(SQA)as the local search operator to enhance the local search ability of the algorithm,the algorithm to balance the global and local search ability,the hybrid algorithm to the optimal solution of the problem of convergence.Through the calculation and analysis of the parameters of the first type of flow system model,the feasibility and accuracy of the algorithm are proved.In order to verify the evolution speed and accuracy of the IWO-SQA algorithm,the results are compared with the experimental results,and the results are better than the original IWO algorithm.