变步长自适应的改进人工鱼群算法

Self-adaptive Improved Artificial Fish Swarm Algorithm with Changing Step

针对人工鱼群算法在函数优化中存在陷入局部最优、后期收敛速度慢及结果精度不高等问题,通过改进鱼群算法中觅食行为及自适应调整人工鱼步长,提出了一种变步长自适应的改进人工鱼群算法。证明了该算法的全局收敛性,从而增加了其理论基础。最后,10个标准函数测试结果表明,改进后的人工鱼群算法在跳出局部最优、收敛速度、精度和稳定性方面都优于原鱼群算法和萤火虫算法,在结果精度和稳定性方面优于文献[9,23,24]的方法。

The artificial fish swarm algorithm in function optimization problems has some defectives,such as falling into local optimum value,converging slowly in the later period and acquiring solutions inaccurately.In order to overcome these shortcomings,a new self-adaptive artificial fish swarm algorithm with changing step was proposed by improving foraging behavior and adjusting self-adaptive step of artificial fish swarm algorithm.In addition,the paper strengthened the theoretical basis of the algorithm by proving the global convergence.Finally,the experimental results of 10 typical functions show that the proposed algorithm is superior to the original artificial fish swarm algorithm and artificial glowworm swarm optimization algorithm in overcoming the local optimum,convergence efficiency,computational precision and stability.Furthermore,the method is superior to the paper[23],[24]and[9]in computational precision and stability.