Improving firefly algorithm by keeping individual activity

There are some disadvantages in the basic firefly algorithm(FA), such as low solving precision, premature convergence and etc. To overcome these disadvantages, a novel improved FA(IFA) was proposed that keeps individual activity. Firstly, an adaptive control for gamma value was designed by using swarm distance. Secondly, a position calculation for fireflies was updated by using the search process information. Thirdly, a special mutation for the firefly swarm was executed to activate individuals and to make them explore the search space when losing activity. Finally, a perturbation and local search method for the best individual was proposed. Based on ten multi-model test functions, the test results show that the IFA has a better convergence speed and precision than the basic FA, PSO, ABC and other improved FA. The improved FA is a good method for complex function optimization.