摘要
提出并实现了一种新的蚁群优化(ACO)并行化策略SHOP(Sharing one pheromone matrix)．主要思想是基于多蚁群在解的构造过程和信息素更新过程中共享同一个信息素矩阵．以ACS和MMAS的SHOP并行实现为例,简要描述了SHOP设计思想和实现过程,尝试了ACS和MMAS并行混合．以对称TSP测试集为对象,将SHOP的实现与相应串行算法在相同计算环境下的实验结果比较,以及与现有的并行实现进行比较,结果表明SHOP并行策略相对于串行ACO及现有的并行策略具有一定的优势．
This paper proposes and implements a new approach to parallel ant colony optimization （ACO） algorithms. The principal idea is to make multiple ant colonies share and utilize only one pheromone matrix. We call our approach sharing one pheromone matrix （SHOP）. This paper briefly describes how to parallelize ACS and MMAS by SHOP strategy, and tries to hybridize these two in parallel. By tackling symmetric travelling salesman problems, this paper compares SHOP-ACO implementation with the relevant sequential ACO algorithms under fair computing environment, as well as with the existing parallel ACO algorithms. The experimental results indicate that SHOP strategy is superior to the sequential ACO algorithms and the existing parallel strategies.
出处
《自动化学报》
EI
CSCD
北大核心
2007年第4期418-421,共4页
Acta Automatica Sinica
基金
江苏省自然科学基金(2003030)资助~~
关键词
蚁群优化
并行
共享信息素矩阵
Ant colony optimization, parallelization, shaving one pheromone matrix
