The flower pollination algorithm is a heuristic algorithm for simulating the flowering plant process.Its characteristics are simple operation,strong robustness,fast search speed,high precision and strong applicability.It is widely used in the field of optimization and selection.However,with the large increase in the size of the processed data,there is a problem of slow convergence,weak local search capability,difficulty in jumping out of local optimum,and weak global search capability.Aiming at the above problems,an improved flower pollination algorithm based on mixed frog leaping and center random replacement is proposed.Firstly,the improved shuffled frogleaping algorithm adjusts the position of the worst individual by the average value of the better individual,and improves the local depth search ability of the algorithm.Secondly,the central random replacement strategy is used to connect the central point of the solution set with the global optimal solution.Lines and extension lines randomly take a point to replace the current solution,speed up the convergence speed of the algorithm;finally,to better meet the global search performance requirements of the algorithm,introduce a diversity control strategy,dynamically change the conversion probability according to the diversity of the population,Increase the probability of a global search.The simulation results of six test functions show that the algorithm is effective.When the convergence speed is accelerated,the local optimization is easy to jump out.The global optimization ability of the algorithm is obviously improved,and the diversity is not affected.The optimization ability is much higher than the standard.Flower pollination algorithm.There are 13 figures,6 tables and 52 references in this paper. |