Leader-Based Parallel Cross Entropy Algorithm for Maximum Clique Problem

Abstract

为了提高交叉熵算法求解最大团问题(maximum clique problem,MCP)的性能,提出一种领导者-跟随者协作求解的并行策略来实现交叉熵算法,从而达到减少计算时间和保障解的质量这两方面的平衡.算法中领导者活跃在并行处理器之间采集数据,并根据当前获得信息对跟随者作出决策;受控的跟随者则主要根据领导者的决策信息自适应地调整搜索空间,完成各自的集团产生任务.采用了OpenMPI在MIMD平台上实现了该算法,并应用到MCP基准测试问题上.加速比和效率分析结果表明,算法具有很好的加速比和效率.而与其它几种当前最好的启发式算法相比,结果表明算法相对于基于种群的启发式算法有一定的性能改善.;The Cross Entropy method is a new search strategy for combinatorial optimization problems. However, it usually needs considerable computational time to achieve good solution quality. This paper introduces a Cross Entropy algorithm for solving maximum clique problem (MCP). To make the Cross Entropy algorithm faster, this paper proposes a leader-based cooperative parallel strategy. Unlike the widely used coarse-grained parallel strategy, our method has a leader, who can move around the parallel processors and collect data actively, and several followers whose main job are simply to sample the cliques guided by the leader via transition matrix. To evaluate the performance of the algorithm, this paper implements the algorithm using OpenMPI on MIMD architecture, and applies it on the MCP benchmark problems. The speedup and efficiency are analyzed, and the results are compared with those obtained by four other best heuristic algorithms. The results show that the presented method has achieved good performance among those best population-based heuristics.