¹College of Medical Information and Engineering, GuangDong Pharmaceutical University, GuangZhou, China
²Dept of Computer, College of Medical Information and Engineering, GuangDong Pharmaceutical University, GuangZhou, China
³Department of information management engineering, School of Management, Guangdong university of technology, GuangZhou, China

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Abstract—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.

Index Terms—shuffled frog-leaping algorithm, single stepsearch strategy, interactive learning rule, continuous optimization, swarm intelligence

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Cite: Deyu Tang, Yongming Cai, Jie Zhao, "An Improved Shuffled Frog Leaping Algorithm with Single Step Search Strategy and Interactive Learning Rule for Continuous Optimization," Journal of Computers vol. 9, no. 6, pp. 1300-1308, 2014.