Improved shuffled frog leaping algorithm for continuous optimization problem

Shuffled frog leaping algorithm (SFLA) is mainly used for the discrete space optimization. For SFLA, the population is divided into several memeplexes, several frogs of each memeplex are selected to compose a submemeplex for local evolvement, according to the mechanism that the worst frog learns from the best frog in submemeplex or the best frog in population, and the memeplexes are shuffled for the global evolvement after some generations of each memeplex. Derived by the discrete SFLA, a new SFLA for continuous space optimization is presented, in which the population is divided based on the principle of uniform performance of memeplexes, and all the frogs participate in the evolvement by keeping the inertia learning behaviors and learning from better ones selected randomly. The simulation results of searching minima of several multi-peak continuous functions show that the improved SFLA can effectively overcome the problems of premature convergence and slow convergence speed, and achieve high optimization precision.