Simulation study of an improved firefly algorithm for solving multi-dimensional complex function optimization

Abstract

To address the shortcomings of the traditional firefly algorithm in global optimization seeking, such as low solution accuracy, unstable convergence and slow optimization speed, a new evolutionary model of firefly algorithm based on the improved Chebyshev chaos mapping is proposed. Firstly, the population distribution is initialised with the improved Chebyshev chaos mapping to improve the population diversity. Secondly, the non-linear dynamic adaptive inertia weights are introduced to regulate the balance between convergence speed and local optimality seeking ability. Then, the boundary variation strategy is introduced to solve the boundary crossing problem to avoid falling into local optimum and continue to improve the population diversity. Finally, simulation experiments are conducted under six benchmark test functions to compare with the traditional firefly algorithm. The experimental results show that the improved algorithm has higher solution accuracy and faster convergence speed.