混沌混合蛙跳算法

Chaos Shuffled Frog Leaping Algorithm

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 problems of the shuffled frog leaping algorithm （SFLA）, such as slow convergence speed and low optimization precision, the chaos shuffled frog leaping algorithm （CSFLA） is proposed, CSFLA was proposed through importing chaos optimization thought. By exploiting the ergodicity and randomness of chaos dealing, the global optimal individual Xs or the worst individual Xw in the randomly updating method was optimized with chaos, and any individual in the present population or the Xw was replaced by optimized result. This dealing advanced the capability of frog to get rid of local extremum, and improved the algorithm＇ s speed and accuracy of convergence. Through testing six benchmark functions and Knapsack Problem, the simulated results showed that CSFLA has better opti- mization performance than basic SFLA and improved SFLA in related references.