基于模拟退火的单向环蛙跳算法在优化问题中的应用

One-way-ring shuffled frog leaping algorithm based on simulated annealing and its application to optimization problems

针对混合蛙跳算法求解优化问题时,进化后期种群多样性降低,算法极易陷入局部最优的问题,借鉴模拟退火算法中的Metropolis判别准则改进混合蛙跳算法中的族群内部寻优能力.同时,在族群之间构建一个单向环迁移机制,从而增强算法后期跳出局部最优的能力.对几种典型函数以及TSP问题的测试结果表明:基于模拟退火的混合蛙跳算法的全局搜索能力有了显著提高,并能有效避免陷入局部最优问题.

When shuffled frog leaping algorithm（SFLA） was used in solving the optimization problems, diversity of species was decreased in the later evolution period, the algorithm could easily fall into local optimum. In response to these problems, using metropolis criterion of simulation annealing （SA） algorithm for reference in order to improve the ability of optimization in each ethnic group. Besides, a one-wayring migration mechanism was proposed to strengthen the ability to escape from local optimum. The experimental results showed that the ability of seeking the global excellent result was superior to original SFLA, and could void the local convergence problem effectively.