资源受限项目调度问题的云自适应混合细菌觅食算法求解  

Adaptive Hybrid Bacterial Foraging Optimization Algorithm Based on Cloud Theory for Solving Resource-constrained Project Scheduling Problem

作　　者：程翔[1] 刘升[1] 

机构地区：[1]上海工程技术大学管理学院,上海201620

出　　处：《小型微型计算机系统》2016年第12期2733-2738,共6页Journal of Chinese Computer Systems

基　　金：国家自然科学基金项目(61075115)资助;上海市教委科研创新基金重点项目(12ZZ185)资助

摘　　要：针对基本细菌觅食算法在求解资源受限项目调度问题时的早熟现象,提出云自适应混合细菌觅食优化算法.该算法根据细菌的适应度值将细菌种群分为优等、一般和劣等三个层次,优等、劣等层次分别采用合适的固定步长,由X条件云发生器自适应调整一般层次中细菌搜索步长,由于云模型云滴具有随机性和稳定倾向性特点,提高了算法的灵活性;并且还引入了粒子群算法自我认知和社会认知思想进行细菌位置更新,提高了算法全局搜索能力,加快了算法的收敛速度.通过对具体算例的模拟仿真对算法进行检验,结果表明该算法在求解资源受限项目调度问题时寻优能力更强,求解效率更高.For the basic bacteria foraging optimization algorithm existing premature phenomenon in solving resource-constrained project scheduling problem（ RCPSP）, an adaptive hybrid bacterial foraging optimization algorithm based on cloud theory （CAHBFO） is pro- posed in this paper. The bacterial is divided into three levels including excellent,common and poor based on the fitness of bacterial in this algorithm, it adopts suitable fixed-step respectively for excellent and poor level. The search step length in common level was adap- tively varied depending on X-conditional cloud generator. The new algorithm＇s flexibility would be better because of the stable tenden- cy and randomness property of the cloud model. Also the thought of self-cognition and social cognition in PSO is brought into the new algorithm to update bacteria＇s position, which improves global searching ability and speeds up the convergence speed of the algorithm. The tests on specific examples show that the proposed algorithm has stronger optimization ability and higher efficiency in solving RCPSP.

关 键 词：细菌觅食算法 云模型 自适应 粒子群算法 资源受限项目调度 

分 类 号：TP301[自动化与计算机技术—计算机系统结构]