A shuffled frog leaping algorithm with contraction factor and its application in mechanical optimum design

The shuffled frog leaping algorithm is easily trapped into local optimum and has the low optimization accuracy when it is used to optimize the complex functions problems. To overcome the above shortcomings, a shuffled frog leaping algorithm with contraction factor was proposed. By introducing acceleration factors c(1) and c(2), the ability of worst individual to learn from best individual within the submemeplexes or global best individual of the entire population was improved and the convergence rate of algorithm was accelerated. Under inserting the contraction factor chi, the convergence of algorithm was ensured. After performing local optimization of the self-learning operator on the worst individual, and taking full advantage of the useful information in the worst individuals, the self-learning ability of the individual and the optimization accuracy of the algorithm were improved. Simulation results illustrated that the enhanced algorithm performed better optimization performance than basic SFLA and other improved SFLAs. Finally, the proposed algorithm was used to optimize five problems of the mechanical design, and its validity and practicability were verified.