Improved honey badger algorithm based on elite differential variation
Aiming at the problems that the standard honey badger algorithm (HBA) is easy to fall into local optimum,low search accuracy and slow convergence speed,a honey badger algorithm based on elite differential mutation (EDVHBA) is proposed. The elite solution searched by the two optimization strategies in the standard HBA is combined with differential mutation to generate a new elite solution. The use of three elite solutions to guide the next iteration of the population can increase the diversity of the algorithm solution and prevent the algorithm from falling into premature convergence. At the same time,the nonlinear density factor is improved and a new position update strategy is introduced to improve the convergence speed and optimization accuracy of the algorithm. In order to verify the performance of the algorithm,simulation experiments are carried out on eight classical test functions. The results show that compared with other swarm intelligence algorithms and improved HBA,EDVHBA can find the optimal value 0 in the unimodal function,and converge to the ideal optimal value in the multimodal function after about 50 iterations,which verifies that EDVHBA has better optimization performance.
honey badger algorithmnonlinear density factorelite differential variationescape predator strategy
万方数据
维普
