Based on the optimization by artificial fish swarm, an artificial fish swarm algorithm (AFSA) suitable to transmission network planning is proposed.
以人工鱼群寻优思想为基础，提出了适用于输电网网架规划的人工鱼群算法。
Artificial fish swarm algorithm (AFSA) is a stochastic global optimization technique proposed lately.
人工鱼群算法(AFSA)是一种新型的群智能随机全局优化技术。
Then, the parameter tuning method via artificial fish school algorithm (AFSA) is proposed.
随后提出了采用人工鱼群算法进行参数整定的方法。
Artificial fish swarm algorithm (AFSA) is a novel optimization algorithm proposed lately. A new method based on this optimization is proposed to design the IIR digital filters.
本文尝试将一种新的优化算法一人工鱼群算法(AFSA)用于IIR数字滤波器设计，建立了相应的优化模型，给出了简化的人工鱼群算法及其实现步骤。
As a swarm intelligence algorithm, AFSA has its weakness, such as high complexity, low optimizing precision and low convergence speed in the later period of the optimization.
但是作为一种新的群智能算法，人工鱼群算法有自身的不足，如算法的复杂度高、算法后期的收敛速度慢和收敛精度低等。
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.
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。
Artificial fish swarm algorithm (AFSA) is a novel optimization algorithm. A new method based on this optimization is proposed to design the IIR digital filters.
将人工鱼群算法(AFSA)用于IIR数字滤波器设计，建立了相应的优化模型，给出了简化的人工鱼群算法及其实现步骤。
Artificial Fish-Swarm Algorithm (AFSA) is a novel optimizing method. It has a strong robustness and good global astringency, and it is also proved to be insensitive to initial values.
人工鱼群算法(AFSA)是一种新型的寻优策略，它具有鲁棒性强，全局收敛性好，以及对初值的不敏感性等优点。
After analyzing the low local search ability of Artificial Fish Swarm Algorithm (AFSA), an improved AFSA based on simplex method was proposed.
针对鱼群算法在局域搜索能力差的问题，提出一种基于单纯形法的改进型人工鱼群算法。
After analyzing the low local search ability of Artificial Fish Swarm Algorithm (AFSA), an improved AFSA based on simplex method was proposed.
针对鱼群算法在局域搜索能力差的问题，提出一种基于单纯形法的改进型人工鱼群算法。
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