摘要
运输问题是一个应用非常广泛的问题,传统方法对于大规模的运输问题求解比较复杂,而一些基于随机搜索算法的方法对于其约束条件的处理又比较困难。基于运输问题约束条件的特殊性,设计了一种产生可行解的方法,将对约束条件的处理转化到了算法设计之中。在此基础上,又设计了基于遗传算法和粒子群优化算法的求解运输问题的GAPSO算法,为避开对非可行解的处理,该算法对迭代过程也进行了特殊设计,从而简化了运用随机搜索算法解决运输问题的过程。最后给出了三个实例验证,通过对验证结果分析和比较,说明该算法在时间复杂度和收敛性方面都具有其优良性,是行之有效的。
The transportation problem is widely used. The large -scale issues are too complex for traditional methods, and it is difficult to deal with the constrained conditions for the stochastic search algorithms. A method for producing feasible solutions of transportation problem is designed based on its special constrained conditions, and the basic idea of this method is to convert constrained conditions into the algorithm design. Based on it, a method named GAPSO algorithm is brought forward to solve transportation problem by combining the genetic algorithm with particle swarm optimization, and it avoids dealing with the unfeasible solutions by special iterative process. Then the stochastic search algorithms for transportation problems are simplified. Finally, three examples are illustrated for the pro- posed approach, and its effectiveness and practicality are verified.
出处
《计算机仿真》
CSCD
2008年第2期286-289,共4页
Computer Simulation
关键词
运输问题
约束条件
遗传算法
粒子群优化算法
Transportation problem
Constrained condition
Genetic algorithms
Particle swarm optimization
