约束条件下基于改进免疫算法的项目工期进度优化  

Optimization of project schedule based on improved immune algorithm under constraints

作　　者：王淑苹 冯为民[2] 黄良辉 刘隽[4] Wang Shuping;Feng Weimin;Huang Lianghui;Liu Jun(School of Architecture and Art Design,Guangdong Nanhua Vocational College of Business,Guangzhou 510507,China;School of Civil and Transportation Engineering,Guangdong University of Technology,Guangzhou 510006,China;School of Civil Engineering,Guangdong Vocational and Technical College of Construction,Guangzhou 510440,China;School of Safety Science and Emergency Management,Wuhan University of Technology,Wuhan 430070,China)

机构地区：[1]广东南华工商职业学院建筑与艺术设计学院,广东广州510507 [2]广东工业大学土木与交通工程学院,广东广州510006 [3]广东建设职业技术学院土木工程学院,广东广州510440 [4]武汉理工大学安全科学与应急管理学院,湖北武汉430070

出　　处：《南京理工大学学报》2023年第5期685-691,共7页Journal of Nanjing University of Science and Technology

基　　金：广东省教育厅项目(2021KTSCX158);珠海大横琴股份重大科技项目(SG88-2018-444B3);广东省职业院校实习工作指导委员会项目(2023059)。

摘　　要：为了提高约束条件下项目工期进度优化水平,采用免疫算法(Immunity algorithms,IA)对工期进度目标函数进行优化求解,并采用鲸群算法(Whale swarm algorithm,WSA)对免疫算法进行改进,以进一步提高IA队项目工期进度的优化精度。首先,对项目工期样本特征进行编码和向量化,确定约束条件和工期进度目标函数,然后以目标函数为抗原,建立IA工期进度优化模型。接着,采用WSA算法搜寻与抗原亲和度最高的抗体,通过WSA算法的猎物搜索、螺旋运动和包围运动等获得亲和度最高个体即为本次最优解。最后,IA根据抗体浓度可重新生成抗体,形成新的种群进行下次迭代求解最优适应度的抗体,获得的最优抗体即为工期进度调度结果。试验结果表明,在相同约束条件下,通过合理设置WSA参数和IA的浓度阈值,相比于其他工期进度优化算法,WSA-IA算法能够获得更优的工期和成本。In order to improve the optimization level of project duration and schedule under constraint conditions,immune algorithm(IA)is used to optimize the objective function of duration and schedule,and whale swarm algorithm(WSA)is used to improve the immune algorithm,so as to further improve the optimization accuracy of IA team project duration and schedule.Firstly,the characteristics of the project duration samples are coded and vectorized to determine the constraint conditions and the objective function of the duration and progress.The objective function is used as the antigen to establish the IA duration and progress optimization model.Then,the WSA algorithm is used to search for the antibodies with the highest affinity with antigen.The prey search of the WSA algorithm is used and the optimal solution is the individual with the highest affinity obtained by spiral motion and surrounding motion.IA can regenerate antibodies according to the antibody concentration to form a new population for the next iteration to solve the optimal fitness of the antibody.The optimal antibody obtained here is the result of schedule scheduling.The experiments show that under the same constraint conditions,by reasonably setting the WSA parameters and the concentration threshold of IA,the WSA-IA algorithm can achieve better duration and cost than other scheduling optimization algorithms.

关 键 词：工期进度 免疫算法 约束条件 鲸群算法 

分 类 号：TP391.2[自动化与计算机技术—计算机应用技术]