计算机工程2012，Vol.38Issue(2)：204-206,3.DOI:10.3969/j.issn.1000-3428.2012.02.067
人工鱼群算法的全局收敛性证明
Global Convergence Proof of Artificial Fish Swarm Algorithm
摘要
Abstract
This paper studies the Artificial Fish Swarm Algorithm(AFSA). The continuous search space is discretized based on the interval-value that each component of a feasible solution locates, each point in the discrete space is just a position state of an artificial fish, its energy(food density) is the objective function value at this point. The whole discrete space and the set of all artificial fishes are also divided into a series of non-empty subsets. During preying, swarming or following activities of artificial fishes, each artificial fish's transition probability from a position to another position can be simply calculated. Each position state corresponds to a state of a finite Markov chain, then the stability condition of a reducible stochastic matrix can be satisfied. In conclusion, the global convergence of AFSA is proved.关键词
先进计算/人工鱼群算法/全局收敛性/有限Markov链Key words
advanced computing/ Artificial Fish Swarm Algorithm(AFSA)/ global convergence/ finite Markov chainadvanced computing/ Artificial Fish Swarm Algorithm(AFSA)/ global convergence/ finite Markov chain分类
信息技术与安全科学引用本文复制引用
黄光球,刘嘉飞,姚玉霞..人工鱼群算法的全局收敛性证明[J].计算机工程,2012,38(2):204-206,3.基金项目
陕西省科学技术研究发展计划基金资助项目(2011K0608) （2011K0608）
