Particle Swarm Optimization with Transition Probability for Timetabling Problems
Particle Swarm Optimization with Transition Probability for Timetabling Problems
复制标题
具有转移概率的粒子群优化用于解决时间表问题
DOI：
10.1007/978-3-642-37213-1_27
复制
发表时间：
2013
期刊：
影响因子：
--
通讯作者：
Satoshi Chen
中科院分区：
文献类型：
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作者：
Hitoshi Kanoh;Satoshi Chen
在本文中，我们提出了一种新的算法来解决大学排课问题，使用粒子群优化（PSO）。粒子群优化算法正越来越多地应用于获得许多数值优化问题的近似最优解。然而，人们也越来越认识到，PSO不解决约束满足问题，以及其他元算法。本文将转移概率引入粒子群优化算法来解决这一问题。使用筑波大学的时间表的实验表明，这种方法是一种比进化策略更有效的解决方案。
In this paper, we propose a new algorithm to solve university course timetabling problems using a Particle Swarm Optimization (PSO). PSOs are being increasingly applied to obtain near-optimal solutions to many numerical optimization problems. However, it is also being increasingly realized that PSOs do not solve constraint satisfaction problems as well as other meta-heuristics do. In this paper, we introduce transition probability into PSO to settle this problem. Experiments using timetables of the University of Tsukuba showed that this approach is a more effective solution than an Evolution Strategy.