求解约束优化问题的自适应免疫混合蛙跳算法

针对基本混合蛙跳算法存在收敛速度幔和优化精度低等不足,提出了免疫混合蛙跳算法.将免疫算法的信息处理机制插入到基本混合蛙跳算法中,免疫接种算子可以改善算法的搜索能力,以此引导算法的进程.同时通过自适应机制来维持种群的多样性,使得算法在保持开发与探索平衡的同时提高了收敛精度.通过对13个经典约束优化问题测试函数的仿真实验,表明了自适应免疫混合蛙跳算法在约束优化问题上具有很好的性能.

Adaptive Immune Shuffled Frog Leaping Algorithm for Solving Restraint Optimization Problem

Aiming at the deficiency of shuffled frog leaping algorithm （SFLA）, such as slow conver- gence speed and low optimization precision, a new method called adaptive immune shuffled frog leaping algo- rithm （AISFLA） is proposed in this paper. The information processing mechanism of immune algorithm is inserted into SFLA, so that the immunity operator can improve search ability of the algorithm, which is used to guide the algorithm process. Meanwhile, through introducing the adaptive learning operator to balance the optimization ability of memeplexes, keep the balance between exploration and development and improved the convergence precision. Simulation results of 13 mark test functions demonstrate the good comprehensive per- formance of ISFLA in restraint optimizations.