Abstract:

Aiming at the shortcomings of shuffled frog leaping algorithm(SFLA) such as ease of trapping into local optimum, low optimization precision and slow speed when it is used to optimize some functions, a Single Population Shuffled Frog Leaping Algorithm (SPSFLA) is proposed. Without sorting the whole population, this new algorithm adopts single population. The individuals are updated by learning from the global best individual and the global middle position. If the current individual is not improved, the global best individual will be mutated and the current individual will be replaced by the new one. Those enhance the running speed, the convergence rate and the optimization precision of SPSFLA. The simulation results show that the improved algorithm has better optimization performance.

Key words: swarm intelligence;shuffled frog leaping algorithm;single population;acceleration factor;swarm behavior