A modified shuffled frog leaping algorithm with inertia weight

Abstract

Abstract The shuffled frog leaping algorithm (SFLA) is a promising metaheuristic bionics algorithm, which has been designed by the shuffled complex evolution (SCE) and the particle swarm optimization (PSO) framework. But it is easily trapped into local optimum and has the low optimization accuracy when it is used to optimize the complex engineering problems. To overcome the short-comings, a novel modified shuffled frog leaping algorithm (MSFLA) with inertia weight is proposed in this paper. To extend the scope of the direction and length of the updated worst frog (vector) of the original SFLA, the inertia weight α was introduced and its meaning and range of the new parameters are fully explained. Then the convergence of the MSFLA is deeply analyzed and proved theoretically by a new dynamic equation formed by Z-transform. Finally, we have compared the solution of 7 benchmark function with the original SFLA, other improved SFLAs, genetic algorithm (GA), PSO, artificial bee colony (ABC) algorithm, and the grasshopper optimization algorithm with invasive weed optimization (IWGOA). The testing results showed that the modified algorithms can effectively improve the solution accuracies and convergence properties, exhibited an excellent ability of global optimization in high-dimensional space and complex function problems.