摘要：Shuffled frog-leaping algorithm (SFLA) is a heuristic optimization technique based on swarm intelligence that is inspired by foraging behavior of the swarm of frogs. The traditional SFLA is easy to be premature convergence. So, we present an improved shuffled frog-leaping algorithm with single step search strategy and interactive learning rule(called ‘SI-SFLA’). Single step search strategy enhances exploring ability of algorithm for higher dimension and interactive learning rule strengthens the diversity of local memeplexe. The effectiveness of the method is tested on many benchmark problems with different characteristics and the results are compared with other algorithms including PSO,SFLA,DE and TLBO. The experimental results show that SI-SFLA has not only a promising performance of searching for accurate solutions, but also a fast convergence rate, which are evaluated using benchmark functions.
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