A levy flight-based shuffled frog-leaping algorithm and its applications for continuous optimization problems

摘要

Shuffled frog-leaping algorithm (SFLA), a novel meta-heuristic optimization algorithm inspired by the foraging behavior of frogs, has been widely applied to many areas for combination problems. But it is easy to fall into the local optimum especially for the continuous optimization problems. This paper proposed a novel variant of SFLA for the continuous optimization problems based on the expanded framework (called the levy flight-based shuffled frog-leaping algorithm, LSFLA). In this new framework, the shuffling process, local search step and global search step are combined according to the exploration and exploitation mechanism. An levy flight based attractor was adopted for the local search step, which enhance the local search ability of algorithm due to the search of short walking distance and occasionally longer walking distance. An interaction learning rule was used for the global search step, which enhances the exploration ability. In order to test the effectiveness of LSFLA, thirty benchmark functions, six real-world constrained continuous optimization problems and a real-world support vector machine (SVM) parameter optimization problem were compared to the many well-known heuristic methods. The experimental results demonstrate that the performance of our proposed algorithm is better than other algorithms for the continuous optimization problems. (C) 2016 Elsevier B.V. All rights reserved.