求解大规模稀疏优化问题的高维多目标萤火虫算法  

Many-objective firefly algorithm for solving large-scale sparse optimization problems

作　　者：赵嘉[1,2] 胡秋敏 肖人彬[3] 潘正祥 崔志华[5] 樊棠怀[1,2] ZHAO Jia;HU Qiu-min;XIAO Ren-bin;PAN Zheng-xiang;CUI Zhi-hua;FAN Tang-huai(School of Information Engineering,Nanchang Institute of Technology,Nanchang 330099,China;Nanchang Key Laboratory of IoT Perception and Collaborative Computing for Smart City,Nanchang Institute of Technology,Nanchang 330099,China;School of Artificial Intelligence and Automation,Huazhong University of Science and Technology,Wuhan 430074,China;Institute of Computer Science and Engineering,Shandong University of Science and Technology,Qingdao 266590,China;College of Computer Science and Technology,Taiyuan University of Technology,Taiyuan 030024,China)

机构地区：[1]南昌工程学院信息工程学院,南昌330099 [2]南昌工程学院南昌市智慧城市物联感知与协同计算重点实验室,南昌330099 [3]华中科技大学人工智能与自动化学院,武汉430074 [4]山东科技大学计算机科学与工程学院,山东青岛266590 [5]太原科技大学计算机科学与技术学院,太原030024

出　　处：《控制与决策》2024年第12期3989-3996,共8页Control and Decision

基　　金：国家自然科学基金项目(52069014)。

摘　　要：多目标萤火虫算法在处理大规模稀疏多目标优化问题时难以保证Pareto最优解的稀疏性,当优化问题的目标维数过大时,将导致Pareto支配失效和收敛速度变慢.鉴于此,提出一种基于动态评分和邻域搜索的高维多目标萤火虫算法(SMaOFA).首先,所提出算法基于双编码混合集成的方式生成稀疏的初始种群,并提出动态评分策略,此策略在每轮迭代时动态更新决策变量得分,为后续迭代提供先验知识,以保证解集的稀疏性;然后,根据模糊支配概念以及萤火虫间的欧氏距离提出邻域搜索策略,摒弃全吸引模型对算法收敛速度的影响,同时避免目标维数过大导致的Pareto支配失效;最后,引入线性调整因子改进萤火虫的位置更新公式,提升种群的搜索能力.实验结果表明,处理大规模稀疏多目标优化问题时,所提出算法具备高效的性能.The multi-objective firefly algorithm is difficult to ensure the sparsity of the Pareto optimal solutions when dealing with large-scale sparse multi-objective optimization problems,and when the objective dimension of the optimization problem is too large,it will also lead to the failure of Pareto dominance and the slowdown of convergence.In view of this,this paper proposes a many-objective firefly algorithm based on dynamic scoring and neighborhood search(SMaOFA).The algorithm generates sparse initial population based on the dual-coding hybrid ensemble,and proposes a dynamic scoring strategy,which dynamically updates the decision variable score at each round of iteration to provide prior knowledge for subsequent iterations to ensure the sparsity of the solution set.According to the concept of fuzzy dominance and the Euclidean distance between fireflies,a neighborhood search strategy is proposed,which discards the influence of the full attraction model on the convergence speed of the algorithm,and avoids the failure of Pareto dominance caused by the large objective dimension.The linear adjustment factor is introduced to improve the position update formula of fireflies and improve the search ability of the population.Experimental results show that the proposed algorithm has efficient performance when dealing with large-scale sparse multi-objective optimization problems.

关 键 词：萤火虫算法 高维多目标优化 大规模稀疏优化 稀疏性 收敛速度 动态评分 

分 类 号：TP139[自动化与计算机技术—控制理论与控制工程]