作　　者： (崔莹）;

机构地区： 南京财经大学信息工程学院,南京210023

出　　处： 《计算机系统应用》 2017年第9期195-199,共5页

摘　　要： 通过对求解虚拟企业资源结盟博弈问题与求解经典SAT问题相似性的分析,提出了一种求解虚拟企业资源结盟博弈的启发式群智能优化算法.算法融合萤火虫优化算法与布谷鸟优化算法部分原理,并设计可行的交叉算子以及变异优化算子,能够修复不可行解并保持种群多样性.实验结果表明本文算法的迭代次数与搜索到的稳定联盟数成线性增长,较启发式遗传算法有着更好的爬山性能和搜索能力. A heuristic swarm intelligent optimization algorithm for coalitional resources games in virtual enterprises is proposed by comparing the similarity of coalitional resources games in virtual enterprises with the classic SAT problem.The algorithm fuses portions of the principles of Glowworm swarm optimization algorithm and Cuckoo Search optimization algorithm. It designs the feasible cross operator and the mutational operator, which can repair infeasible solution and maintain diversity. Experimental results indicate that the algorithm＇s iterations linearly increase with the stable coalitions ever found. Compared with the heuristic genetic algorithm, it performs better in hill-climbing performance and searching efficiency.

关 键 词： 资源结盟博弈 虚拟企业 问题 群智能优化算法