Bacterial Foraging Optimization Algorithm with Dynamically Reduced Setting of Migration Probability

Abstract

In the application of bacterial foraging optimization algorithm, the parameter setting has a very important effect on the performance of the algorithm. In order to solve the problems of low convergence accuracy and complex parameter setting of bacterial foraging optimization algorithm, the bacterial foraging optimization algorithm with dynamically reduced setting of migration probability was proposed by analyzing the function of each parameter in bacterial foraging optimization algorithm and considering the better balance between global search and local search ability. In the initial stage of the algorithm, the migration probability is relatively large, and the global search ability is relatively strong. With the progress of the algorithm, the migration probability is reduced, and the local search ability is enhanced, so that the algorithm can obtain a more accurate solution. At the same time, in order to avoid the algorithm falling into the local solution, the parameter of migration probability is processed in sections in the process of dynamic parameter setting with the idea of simulated annealing algorithm. Classical single-peak and multi-peak reference functions are selected to test the effect of the improved algorithm, and the experimental results verify that the improved algorithm has higher convergence accuracy.