Improved Bats Algorithm Optimization Based on Genetic Hybrid Genes

Bat algorithm( BA) is a stochastic optimization algorithm · Standard Code bat but there optimization algorithm accuracy is not high,late slow convergence and easy to fall into local issues such as a new class of optimal search global optimal solution. To solve these problems,we propose a genetic factor in the improved cross-bat algorithm( GHBA) based. Compiled bat algorithm to improve the diversity of the population of individuals to avoid falling into local optimal algorithm to enhance the global optimization capability. In the MATLAB environment,the use of six standard test functions simulation experiments,the results show that,compared with the BA algorithm,( GHBA) The convergence speed and accuracy were significantly improved.