Analysis And Application Research Of Gaining-sharing Knowledge Based Algorithm And Prairie Dog Optimization Algorithm

Gaining-sharing knowledge based algorithm(GSK)is a meta-heuristic algorithm proposed in 2019,which is inspired by the process of human acquiring and sharing knowledge.Prairie Dog Optimization Algorithm(PDO)is a new metaheuristic algorithm proposed in 2022 inspired by the unique behavior characteristics of prairie dogs.Both GSK and PDO algorithms have shortcomings such as weak global search ability,slow convergence speed and easy to fall into local extremum.In view of this problem,this paper carries out the improvement research of "Gaining-sharing knowledge based algorithm and Prairie Dog Optimization Algorithm" based on the analysis of the shortcomings of GSK and PDO to improve the optimization performance of two algorithms further,and achieves certain research results.The research results of this paper are as follows:(1)Gaining-sharing Knowledge Based Algorithm Using Dynamic Knowledgefactor(DKGSK)is proposed: individuals adjust their search activities with adaptive weights.In the early stage of the algorithm large steps are conducted with global exploration to enhance the global exploration ability of the algorithm;In the late stage of the algorithm,local search is carried out with a small step length to improve the local development ability of the algorithm;Individuals use dynamic knowledge factors to adjust their search step size,making the search step size more flexible and random,thus improving the individual’s ability to refine the search,enhancing the local development ability of the algorithm;With the help of Levy flight,individuals can enhance their ability to jump out of the local optimum,and then enhance the ability of the algorithm to avoid falling into the local optimum.The optimization performance of DKGSK is verified through numerical simulation,and DKGSK is applied to the ELMAN neural network optimization problem.The numerical experimental results show that DKGSK has good application value.(2)Prairie Dog Optimization Algorithm based on Cooperative Search Strategy(CSSPDO)is proposed.Division weight strategy is introduced to balance the exploration and development capabilities;Collaborative search strategy is used to improve the exploration ability of the algorithm;Evasion strategy is added to avoid the algorithm falling into local optimization.The optimization performance of CSSPDO is tested through numerical experiments.(3)GSK and PDO are combined to solve engineering optimization problems,and verify optimization performance through numerical experiments.