改进的蚁群算法求解函数优化
首发时间:2009-04-17
摘要:蚂蚁具有找到蚁穴与食物源之间最短路径的能力,受此启发提出的蚁群算法最初用于解决旅行商问题,具有自适应性、鲁棒性及本质上的并行性等许多特点,广泛适用于各种静态和动态的组合优化问题中,具有潜在的应用前景。 由Dorgio等提出的蚁群算法的描述可知,在通用启发式蚁群算法的起始阶段,信息素值被初始化为统一的值,对解的搜索没有指导意义,启发值此时反而能够提供有用的局部信息,有助于算法的快速收敛。随着算法的进行,根据搜索到的不同路径而更新的信息素值,存储了解空间的全局最优解信息,相对于启发值而言,所起的作用不断提高。因此设计了一种自适应的启发值因子,能够随着算法的进行调整信息素值和启发值之间的相对权重,加速算法的收敛速度。 为了求解一般的函数优化,文章在对标准蚁群算法的基础上,引入遗传算法的编码方式,并对蚁群算法的信息素更新进行改进。通过对几个经典测试函数的求解,证明了算法的有效性。
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A Improved Ant Colony Algorithm Design for Function Optimization
Abstract:A general-purpose metaheuristic named Ant Colony Optimization algorithm, which take inspiration from real ant’s behavior in finding shortest paths using as information only the trail of a chemical substance (called pheromone) deposited by other ants, boasts a number of attractive features, including adaptation, robustness and distributed, decentralized nature, and have recently been successfully applied to several discrete optimization problem under consideration through a cooperative effort. This effort is mediated by indirect communication of information on the problem structure the ants concurrently collect while building solutions by using a stochastic policy. To solve function optimization problem, based on ant colony algorithm, binary coding of genetic algorithm is added and pheromone updating strategy is developed. The imp roved algorithm has been tested forvariety of different classical test functions. And the algorithm can handle these op timization problems very well
Keywords: Ant colony algorithm Function optimization Approximation of function Robustness Neural network
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No.3148545117612399****
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