基于自适应高斯变异的人工鱼群算法

Artificial Fish-School Algorithm Based on Adaptive Gauss Mutation

针对基本人工鱼群算法存在的不足,根据高斯变异和历史最优鱼个体状态,提出自适应高斯变异人工鱼群算法。该算法能克服人工鱼漫无目的随机游动从而求得全局极值,提高求解质量和运行效率。典型测试函数测试、应用实例验证和理论分析表明,该算法是可行、有效的。

Aiming at the disadvantages of Artificial Fish-School Algorithm（AFSA）, this paper proposes a novel AFSA based on adaptive Gauss mutation and historical best fish. The ability of AFSA to break away from artificial fish stochastic moving without a definite purpose is improved. It can greatly improve the ability of seeking the global excellent result and convergence property and accuracy. Test of representative test function, application example and theory analysis show that it is feasible and availability.