Improved sparrow search algorithm based on hybrid strategy

In order to overcome the shortcomings of the sparrow optimization algorithm, which include limited population diversity, low high-latitude solution accuracy, and early convergence, this paper proposes to improve the sparrow search algorithm (FASSA) by using multiple strategies. Firstly, the improved sine chaotic map is used to initialize the population position, which results in a more uniform distribution of the population's initial solution position. Secondly, the position update of the discoverer is improved by introducing the golden sine algorithm with a curve adaptation, which effectively accelerates the speed of convergence and coordinates the capabilities of global and local search. Finally, the firefly algorithm is introduced to combine all sparrows with the optimal sparrows using the firefly perturbation method, so that the quality of the individuals after each iteration is improved. This paper selected eight benchmark functions for testing, and the simulation results of FASSA and the other four meta-heuristic algorithms are compared. According to the experimental findings, the improved algorithm has a great improvement in global search, overcoming local optimization, convergence speed, and convergence accuracy.