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针对基本混合蛙跳算法(Shuffled Frog Leaping Algorithm,简称SFLA),收敛速度慢,优化精度低的问题,提出了混沌混合蛙跳算法。将混沌优化思想引入到蛙跳算法中,利用混沌运动的随机性和遍历性,对全局最优个体Xg或随机更新策略中的最差个体Xw进行混沌优化,并用优化结果随机替代当前种群中的某个体或Xw,通过这种处理增强了蛙跳算法摆脱局部极值点的能力,提高了算法的收敛速度和精度。通过对6个测试函数和背包问题进行优化实验,仿真结果表明,混沌混合蛙跳算法的优化性能明显优于基本混合蛙跳算法和相关文献中的改进算法。
Aiming at the problem of the basic mixed frog leaping algorithm (SFLA), the convergence speed is slow and the optimization precision is low, a chaotic mixed frog leaping algorithm is proposed. The idea of chaos optimization is introduced into the frog leaping algorithm. By using the randomness and ergodicity of chaos, chaos optimization is performed on the global individual Xg or the worst individual Xw in the random update strategy, and the results are randomly replaced by the results of the optimization An individual or Xw enhances the ability of frog leaping algorithms to get rid of local extremum points through this processing and improves the convergence speed and accuracy of the algorithm. Through the optimization of six test functions and knapsack problem, the simulation results show that the chaos hybrid frog leaping algorithm optimization performance is better than the basic mixed frog leaping algorithm and the related literature improved algorithm.