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Variable Reconstruction for Evolutionary Expensive Large-Scale Multiobjective Optimization and Its Application on Aerodynamic Design 认领 引用 被引量：1
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作者 Jianqing Lin Cheng He +1 位作者 Ye Tian Linqiang Pan 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2025年第4期719-733,共15页
Expensive multiobjective optimization problems(EMOPs)are complex optimization problems exacted from realworld applications,where each objective function evaluation(FE)involves expensive computations or physical experi... Expensive multiobjective optimization problems(EMOPs)are complex optimization problems exacted from realworld applications,where each objective function evaluation(FE)involves expensive computations or physical experiments.Many surrogate-assisted evolutionary algorithms(SAEAs)have been designed to solve EMOPs.Nevertheless,EMOPs with large-scale decision variables remain challenging for existing SAEAs,leading to difficulties in maintaining convergence and diversity.To address this deficiency,we proposed a variable reconstructionbased SAEA(VREA)to balance convergence enhancement and diversity maintenance.Generally,a cluster-based variable reconstruction strategy reconstructs the original large-scale decision variables into low-dimensional weight variables.Thus,the population can be rapidly pushed towards the Pareto set(PS)by optimizing low-dimensional weight variables with the assistance of surrogate models.Population diversity is improved due to the cluster-based variable reconstruction strategy.An adaptive search step size strategy is proposed to balance exploration and exploitation further.Experimental comparisons with four state-of-the-art SAEAs are conducted on benchmark EMOPs with up to 1000 decision variables and an aerodynamic design task.Experimental results demonstrate that VREA obtains well-converged and diverse solutions with limited real FEs. 展开更多
关键词 Aerodynamic design large-scale optimization multiobjective evolutionary algorithm surrogate model variable reconstruction
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The Constrained Mean-Semivariance Portfolio Optimization Problem with the Support of a Novel Multiobjective Evolutionary Algorithm 认领 引用 被引量：1
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作者 K. Liagkouras K. Metaxiotis 《Journal of Software Engineering and Applications》 2013年第7期22-29,共8页
The paper addresses the constrained mean-semivariance portfolio optimization problem with the support of a novel multi-objective evolutionary algorithm (n-MOEA). The use of semivariance as the risk quantification meas... The paper addresses the constrained mean-semivariance portfolio optimization problem with the support of a novel multi-objective evolutionary algorithm (n-MOEA). The use of semivariance as the risk quantification measure and the real world constraints imposed to the model make the problem difficult to be solved with exact methods. Thanks to the exploratory mechanism, n-MOEA concentrates the search effort where is needed more and provides a well formed efficient frontier with the solutions spread across the whole frontier. We also provide evidence for the robustness of the produced non-dominated solutions by carrying out, out-of-sample testing during both bull and bear market conditions on FTSE-100. 展开更多
关键词 Multiobjective Optimization Evolutionary Algorithms Portfolio Optimization
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MULTIOBJECT OPTIMIZATION OF A CENTRIFUGAL IMPELLER USING EVOLUTIONARY ALGORITHMS 认领 引用 被引量：3
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作者 LiJun LiuLijun FengZhenping 《Chinese Journal of Mechanical Engineering》 EI CAS 2004年第3期389-393,共5页
Application of the multiobjective evolutionary algorithms to the aerodynamicoptimization design of a centrifugal impeller is presented. The aerodynamic performance of acentrifugal impeller is evaluated by using the th... Application of the multiobjective evolutionary algorithms to the aerodynamicoptimization design of a centrifugal impeller is presented. The aerodynamic performance of acentrifugal impeller is evaluated by using the three-dimensional Navier-Stokes solutions. Thetypical centrifugal impeller is redesigned for maximization of the pressure rise and blade load andminimization of the rotational total pressure loss at the given flow conditions. The Bezier curvesare used to parameterize the three-dimensional impeller blade shape. The present method obtains manyreasonable Pareto optimal designs that outperform the original centrifugal impeller. Detailedobservation of the certain Pareto optimal design demonstrates the feasibility of the presentmultiobjective optimization method tool for turbomachinery design. 展开更多
关键词 Centrifugal impeller Navier-Stokes solver Evolutionary algorithms Multiobjective optimization Design
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A Generative Adversarial Network Guided Evolutionary Algorithm for Large-scale Sparse Multiobjective Optimization 认领 引用
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作者 Zhuanlian Ding Junzhe Liu +2 位作者 Dengdi Sun Xingyi Zhang Bin Luo 《Machine Intelligence Research》 EI CSCD 2026年第1期263-280,共18页
In recent decades,great progress has been made in learnable multiobjective evolutionary algorithms(MOEAs)in the field of evolutionary computations.However,existing learnable MOEAs have not been equipped with powerful ... In recent decades,great progress has been made in learnable multiobjective evolutionary algorithms(MOEAs)in the field of evolutionary computations.However,existing learnable MOEAs have not been equipped with powerful strategies for addressing the grand series associated with sparse large-scale multiobjective optimization problems(sparse LSMOPs),which include the curse of dimensionality and unknown sparsity characteristics.This work proposes a generative adversarial network(GAN)-guided evolutionary algorithm for solving sparse LSMOPs.GAN-aided offspring generation is adopted at each generation to generate high-quality sparse offspring solutions to improve the search performance,owing to the GAN’s powerful learning and generative capabilities.Specifically,random interpolation and discretization strategies are utilized to prevent mode collapse and falling into local optima,thereby generating promising sparse offspring solutions.The experimental results on both benchmark and real-world problems verify the superior performance of the proposed algorithm compared with the state-of-the-art evolutionary algorithms. 展开更多
关键词 Evolutionary algorithm generative adversarial network(GAN) sparse large-scale multiobjective optimization
Fuzzy Constraint Dominance Strategy for Constrainted Multiobjective Optimization Problems With Multiple Constraints 认领 引用
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作者 Weixiong Huang Rui Wang +2 位作者 Tao Zhang Sheng Qi Ling Wang 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2025年第12期2455-2472,共18页
Solving constrained multiobjective optimization problems(CMOPs) is a highly challenging work. Numerous complex nonlinear constraints significantly add to the complexity of CMOPs, resulting in an exceptionally intricat... Solving constrained multiobjective optimization problems(CMOPs) is a highly challenging work. Numerous complex nonlinear constraints significantly add to the complexity of CMOPs, resulting in an exceptionally intricate feasible region.Makes it difficult for the algorithm to search for the complete constraint PF. In addition, under the influence of multiple complex nonlinear constraints, the conventional calculation method of overall constraint violation is inefficient for assessing the quality of infeasible solutions, potentially misguiding the evolutionary direction of the population. In response to these challenges, this paper proposes the fuzzy constraint dominance strategy(FCDS).This novel approach facilitates nuanced comparisons of solutions to strike a better balance between objectives and constraints. The fuzzy constraint violation introduced in FCDS mitigates the misleading impact of complex nonlinear constraints. Moreover,FCDS divides the solution process of complex CMOP into multiple stages from easy to difficult, and uses adaptive methods to increase the difficulty level of the problem. Systematic experiments on four test suites and three real-world applications have conclusively demonstrated the superior competitiveness of FCDS against leading algorithms. 展开更多
关键词 Constrained multiobjective optimization constrainthandling technologies evolutionary algorithm(EA) fuzzy constraint dominance
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Multiobjective Optimization of Simulated Moving Bed by Tissue P System 认领 引用 被引量：8
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作者 黄亮 孙磊 +1 位作者 王宁 金晓明 《Chinese Journal of Chemical Engineering》 SCIE EI CAS 2007年第5期683-690,共8页
The binaphthol enantiomers separation process using simulation moving bed technology is simulated with the true moving bed approach （TMB）. In order to systematically optimize the process with multiple productive obj... The binaphthol enantiomers separation process using simulation moving bed technology is simulated with the true moving bed approach （TMB）. In order to systematically optimize the process with multiple productive objectives, this article develops a variant of tissue P system （TPS）. Inspired by general tissue P systems, the special TPS has a tissue-like structure with several membranes. The key rules of each membrane are the communication rule and mutation rule. These characteristics contribute to the diversity of the population, the conquest of the multimodal of objective function, and the convergence of algorithm. The results of comparison with a popular algorithm——the non-dominated sorting genetic algorithm 2（NSGA-2） illustrate that the new algorithm has satisfactory performance. Using the algorithm, this study maximizes synchronously several conflicting objectives, purities of different products, and productivity. 展开更多
关键词 simulated moving bed tissue P systems multiobjective optimization Pareto optimality evolutionary algorithm binaphthol enantiomers separation process
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A self-adaptive linear evolutionary algorithm for solving constrained optimization problems 认领 引用 被引量：1
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作者 Kezong TANG Jingyu YANG +1 位作者 Shang GAO Tingkai SUN 《控制理论与应用（英文版）》 2010年第4期533-539,共7页
In many real-world applications of evolutionary algorithms,the fitness of an individual requires a quantitative measure.This paper proposes a self-adaptive linear evolutionary algorithm （ALEA） in which we introduce ... In many real-world applications of evolutionary algorithms,the fitness of an individual requires a quantitative measure.This paper proposes a self-adaptive linear evolutionary algorithm （ALEA） in which we introduce a novel strategy for evaluating individual＇s relative strengths and weaknesses.Based on this strategy,searching space of constrained optimization problems with high dimensions for design variables is compressed into two-dimensional performance space in which it is possible to quickly identify ＇good＇ individuals of the performance for a multiobjective optimization application,regardless of original space complexity.This is considered as our main contribution.In addition,the proposed new evolutionary algorithm combines two basic operators with modification in reproduction phase,namely,crossover and mutation.Simulation results over a comprehensive set of benchmark functions show that the proposed strategy is feasible and effective,and provides good performance in terms of uniformity and diversity of solutions. 展开更多
关键词 Multiobjective optimization Evolutionary algorithms Pareto optimal solution Linear fitness function
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Evolutionary Algorithm with Ensemble Classifier Surrogate Model for Expensive Multiobjective Optimization 认领 引用
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作者 LAN Tian 《Transactions of Nanjing University of Aeronautics and Astronautics》 EI CSCD 2020年第S1期76-87,共12页
For many real-world multiobjective optimization problems,the evaluations of the objective functions are computationally expensive.Such problems are usually called expensive multiobjective optimization problems(EMOPs).... For many real-world multiobjective optimization problems,the evaluations of the objective functions are computationally expensive.Such problems are usually called expensive multiobjective optimization problems(EMOPs).One type of feasible approaches for EMOPs is to introduce the computationally efficient surrogates for reducing the number of function evaluations.Inspired from ensemble learning,this paper proposes a multiobjective evolutionary algorithm with an ensemble classifier(MOEA-EC)for EMOPs.More specifically,multiple decision tree models are used as an ensemble classifier for the pre-selection,which is be more helpful for further reducing the function evaluations of the solutions than using single inaccurate model.The extensive experimental studies have been conducted to verify the efficiency of MOEA-EC by comparing it with several advanced multiobjective expensive optimization algorithms.The experimental results show that MOEA-EC outperforms the compared algorithms. 展开更多
关键词 multiobjective evolutionary algorithm expensive multiobjective optimization ensemble classifier surrogate model
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Managing Software Testing Technical Debt Using Evolutionary Algorithms 认领 引用 被引量：1
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作者 Muhammad Abid Jamil Mohamed K.Nour 《Computers, Materials & Continua》 SCIE EI 2022年第10期735-747,共13页
Technical debt(TD)happens when project teams carry out technical decisions in favor of a short-term goal(s)in their projects,whether deliberately or unknowingly.TD must be properly managed to guarantee that its negati... Technical debt(TD)happens when project teams carry out technical decisions in favor of a short-term goal(s)in their projects,whether deliberately or unknowingly.TD must be properly managed to guarantee that its negative implications do not outweigh its advantages.A lot of research has been conducted to show that TD has evolved into a common problem with considerable financial burden.Test technical debt is the technical debt aspect of testing(or test debt).Test debt is a relatively new concept that has piqued the curiosity of the software industry in recent years.In this article,we assume that the organization selects the testing artifacts at the start of every sprint.Implementing the latest features in consideration of expected business value and repaying technical debt are among candidate tasks in terms of the testing process(test cases increments).To gain the maximum benefit for the organization in terms of software testing optimization,there is a need to select the artifacts(i.e.,test cases)with maximum feature coverage within the available resources.The management of testing optimization for large projects is complicated and can also be treated as a multi-objective problem that entails a trade-off between the agile software’s short-term and long-term value.In this article,we implement a multi-objective indicatorbased evolutionary algorithm(IBEA)for fixing such optimization issues.The capability of the algorithm is evidenced by adding it to a real case study of a university registration process. 展开更多
关键词 Technical debt software testing optimization large scale agile projects evolutionary algorithms multiobjective optimization indicatorbased evolutionary algorithm(IBEA) pareto front
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A Hybrid Optimization Technique Coupling an Evolutionary and a Local Search Algorithm for Economic Emission Load Dispatch Problem 认领 引用 被引量：1
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作者 A. A. Mousa Kotb A. Kotb 《Applied Mathematics》 2011年第7期890-898,共9页
This paper presents an optimization technique coupling two optimization techniques for solving Economic Emission Load Dispatch Optimization Problem EELD. The proposed approach integrates the merits of both genetic alg... This paper presents an optimization technique coupling two optimization techniques for solving Economic Emission Load Dispatch Optimization Problem EELD. The proposed approach integrates the merits of both genetic algorithm (GA) and local search (LS), where it maintains a finite-sized archive of non-dominated solutions which gets iteratively updated in the presence of new solutions based on the concept of ε-dominance. To improve the solution quality, local search technique was applied as neighborhood search engine, where it intends to explore the less-crowded area in the current archive to possibly obtain more non-dominated solutions. TOPSIS technique can incorporate relative weights of criterion importance, which has been implemented to identify best compromise solution, which will satisfy the different goals to some extent. Several optimization runs of the proposed approach are carried out on the standard IEEE 30-bus 6-genrator test system. The comparison demonstrates the superiority of the proposed approach and confirms its potential to solve the multiobjective EELD problem. 展开更多
关键词 Economic Emission Load Dispatch Evolutionary Algorithms Multiobjective Optimization Local Search
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基于增强弱交互与LJ势能引导的双种群多模态多目标进化算法 认领 引用
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作者 贺娟娟 刘鸿伟 +1 位作者 张凯 葛明峰 《控制与决策》 EI CSCD 北大核心 2026年第3期651-663,共13页
多模态多目标优化(MMOP)作为多目标优化领域的一大挑战,要求算法不仅在目标空间获得高质量的帕累托解,还要在决策空间捕捉多个结构明显不同但等效的解.在这种双重需求下,目标空间强收敛性易掩盖决策空间多样性,导致解集结构单一化;与此... 多模态多目标优化(MMOP)作为多目标优化领域的一大挑战,要求算法不仅在目标空间获得高质量的帕累托解,还要在决策空间捕捉多个结构明显不同但等效的解.在这种双重需求下,目标空间强收敛性易掩盖决策空间多样性,导致解集结构单一化;与此同时,种群间交互的强弱失衡又分别引发种群同质化或协同失效等问题.MMOP已成为制约复杂系统优化性能的关键瓶颈.为此,提出一种基于增强弱交互与Lennard-Jones(LJ)势能引导机制的双种群协同进化算法.首先构建一种非对称信息交换机制,在交配与子代生成阶段由收敛性种群向多样性种群建立精英引导路径,有效兼顾多样性保持与进化效率;其次,环境选择策略由并行改为串行,强化种群异质性,减少对额外多样性策略的依赖,提升稳定性与鲁棒性;为提升种群在不同演化阶段的收敛性与多样性,设计一种基于LJ势能模型的自适应候选解选择策略,重新量化其交互权重,该策略有效实现了探索与开发的动态平衡.在多个典型MMOP测试函数上的实验结果表明,所提算法在解集多样性、帕累托逼近质量和优化效率方面均优于主流方法,展现出良好的泛化能力与工程应用潜力. 展开更多
关键词 多目标进化算法 多模态多目标优化问题 进化算法 差分进化算法 弱交互双种群协同进化 Lennard-Jones势能
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基于多源知识迁移策略的动态约束多目标进化算法 认领 引用
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作者 陈国玉 郭一楠 +4 位作者 杨潇 马天兵 李长河 袁亮 韩守飞 《计算机工程与应用》 EI CSCD 北大核心 2026年第7期156-167,共12页
动态约束多目标优化问题存在时变目标函数和约束条件,导致现有方法无法有效平衡变化响应的性能和效率。鉴于此,提出一种基于多源知识迁移策略的动态约束多目标进化算法(multi-source knowledge transfer based dynamic constrained mult... 动态约束多目标优化问题存在时变目标函数和约束条件,导致现有方法无法有效平衡变化响应的性能和效率。鉴于此,提出一种基于多源知识迁移策略的动态约束多目标进化算法(multi-source knowledge transfer based dynamic constrained multiobjective evolutionary algorithm,MSKTEA)。该算法设计知识提取策略,利用预测方法分别估计新环境帕累托解集和帕累托前沿,并进一步建立外部存档,以存储多样性的历史解集。随后,设计多源知识迁移策略,利用预测方法得到的时序知识,并基于时序知识在外部存档中提取的相似环境知识,进行知识迁移以生成新环境初始种群。实验结果表明,MSKTEA相较于多个当前较优的算法在处理动态约束多目标优化问题时具有较强的竞争力,能够有效平衡算法追踪动态帕累托最优的性能和效率。 展开更多
关键词 动态约束多目标优化 进化算法 时序知识 相似环境知识 知识迁移
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一种基于正态分布交叉的ε-MOEA 认领 引用 被引量：35
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作者 张敏 罗文坚 王煦法 《软件学报》 EI 北大核心 2009年第2期305-314,共10页
实数编码的多目标进化算法常使用模拟二进制交叉(simulated binary crossover,称SBX)算子.通过对SBX以及进化策略中变异算子进行对比分析,并引入进化策略中的离散重组算子,提出了一种正态分布交叉(normal distribution crossover,称NDX... 实数编码的多目标进化算法常使用模拟二进制交叉(simulated binary crossover,称SBX)算子.通过对SBX以及进化策略中变异算子进行对比分析,并引入进化策略中的离散重组算子,提出了一种正态分布交叉(normal distribution crossover,称NDX)算子.首先在一维搜索空间实例中对NDX与SBX算子进行比较和分析,然后将NDX算子应用于Deb等人提出的稳态多目标进化算法ε-MOEA(ε-dominance based multiobjective evolutionary algorithm)中.采用NDX算子的ε-MOEA(记为ε-MOEA/NDX)算法在多目标优化标准测试集ZDT和DTLZ的10个函数上进行了实验比较.实验结果和分析表明,采用NDX的ε-MOEA所求得的Pareto最优解集质量明显优于经典算法ε-MOEA/SBX和NSGA-Ⅱ. 展开更多
关键词 进化多目标优化 ε-MOEA(ε-dominance based multiobjective evolutionary algorithm) 正态分布交叉 模拟二进制交叉
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基于自适应进化策略的MOEA/D算法 认领 引用 被引量：6
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作者 耿焕同 周山胜 +1 位作者 韩伟民 周利发 《计算机工程与设计》 北大核心 2019年第4期1106-1113,共8页
针对MOEA/D算法单纯使用差分进化策略造成局部搜索能力弱、寻优精度低等问题,提出一种基于自适应进化策略的MOEA/D算法(MOEA/D-EA)。利用种群邻域更新信息构造进化状态判断机制,判断子问题的进化潜能和种群的进化状态;将子问题的进化潜... 针对MOEA/D算法单纯使用差分进化策略造成局部搜索能力弱、寻优精度低等问题,提出一种基于自适应进化策略的MOEA/D算法(MOEA/D-EA)。利用种群邻域更新信息构造进化状态判断机制,判断子问题的进化潜能和种群的进化状态;将子问题的进化潜能正反馈到反向学习模型,形成自适应的反向学习策略(AOBL);根据种群的进化状态选择不同的进化策略,以均衡算法的全局搜索与局部寻优能力。实验结果表明,该算法在收敛性、分布性和稳定性等方面均优于或部分优于其它对比算法。 展开更多
关键词 MOEA/D算法 进化潜能判断 反向学习 自适应进化策略 多目标优化算法
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基于改进MOEA/D的钢铁多介质能源计划优化 认领 引用 被引量：4
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作者 欧阳洪才 吴定会 +1 位作者 范俊岩 汪晶 《系统仿真学报》 CAS CSCD 北大核心 2023年第3期568-578,共11页
针对多介质钢铁能源计划模型存在变量较多、约束复杂和模型求解难度高等问题,提出基于自适应邻域的改进MOEA/D(decomposition-based multi-objective evolutionary algorithm)实现多介质能源计划优化。考虑分时电价特性和煤气柜的缓冲作... 针对多介质钢铁能源计划模型存在变量较多、约束复杂和模型求解难度高等问题,提出基于自适应邻域的改进MOEA/D(decomposition-based multi-objective evolutionary algorithm)实现多介质能源计划优化。考虑分时电价特性和煤气柜的缓冲作用,构建以最小化运行成本和总能耗的目标函数,设计能源介质供需和工序饱和度等模型约束;基于能源产耗规则的解码方法确定目标值,定义归一化的切比雪夫聚合函数和种群进化程度的自适应邻域更新,设计改进MOEA/D的能源计划优化算法。仿真对比实验验证了改进MOEA/D有效实现能源计划优化,提高解的收敛性,降低运行成本1.3%和能耗1.2%。 展开更多
关键词 能源计划 多目标 能耗 MOEA/D 邻域更新
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基于多目标进化算法混合框架的MOEA/D算法 认领 引用 被引量：7
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作者 田红军 汪镭 吴启迪 《系统仿真学报》 CAS CSCD 北大核心 2020年第2期201-216,共16页
针对混合多目标进化算法中如何设计全局搜索算法和局部搜索策略结合机制的难点问题以及提高多目标进化算法的求解性能,基于反馈控制思想,提出了一种系统化、模块化的全局优化与局部搜索相结合的混合MOEA/D算法,算法中设计了一种基于拥... 针对混合多目标进化算法中如何设计全局搜索算法和局部搜索策略结合机制的难点问题以及提高多目标进化算法的求解性能,基于反馈控制思想,提出了一种系统化、模块化的全局优化与局部搜索相结合的混合MOEA/D算法,算法中设计了一种基于拥挤熵的种群多样性度量方法;提出了基于简化二次逼近的局部搜索策略,以及针对MOEA/D的种群多样性增强策略。数值实验表明所提算法具有良好性能,可以兼顾算法求解的多样性和收敛性,所提混合框架可有效提升现有多目标进化算法的求解性能。 展开更多
关键词 多目标优化 进化算法 混合框架 MOEA/D 反馈控制
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一种基于MOEA/D的组合权重方法 认领 引用 被引量：10
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作者 程建华 董铭涛 赵琳 《控制与决策》 EI CSCD 北大核心 2021年第12期3056-3062,共7页
为了准确地求解组合权重的组合系数,将基于分解的多目标进化算法(multi-objective evolutionary algorithm based on decomposition,MOEA/D)思想引入评估领域,提出一种基于MOEA/D的组合权重方法.通常,利用加权和法将组合权重模型转化为... 为了准确地求解组合权重的组合系数,将基于分解的多目标进化算法(multi-objective evolutionary algorithm based on decomposition,MOEA/D)思想引入评估领域,提出一种基于MOEA/D的组合权重方法.通常,利用加权和法将组合权重模型转化为单目标模型时,模型加权系数难以准确确定.对此,引入MOEA/D算法的分解思想,将组合权重模型转化为多个单目标子模型.MOEA/D算法仅适用于无约束优化问题,而较为常用的惩罚函数法难以表达进化初期无可行解的情况,因而提出改进自适应惩罚函数(improved adaptive penalty function,IAPF),将组合权重模型转化为无约束优化模型.应用所提出方法与其他方法进行仿真实验,实验结果表明,所提出算法具有有效性. 展开更多
关键词 组合权重 多目标优化 约束 MOEA/D 自适应惩罚函数
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基于改进MOEA/D的车联网通信资源分配算法 认领 引用 被引量：7
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作者 郑丽萍 赵玉娟 费选 《计算机工程》 CAS CSCD 北大核心 2023年第5期191-197,共7页
为获得车联网通信资源分配的最优解,提出一种基于改进MOEA/D的车联网通信资源分配优化算法。将车联网资源请求的阻塞率和资源请求成功的总成本这2个相互冲突的网络通信资源分配要素作为网络通信资源分配的2个优化目标,根据车联网中行驶... 为获得车联网通信资源分配的最优解,提出一种基于改进MOEA/D的车联网通信资源分配优化算法。将车联网资源请求的阻塞率和资源请求成功的总成本这2个相互冲突的网络通信资源分配要素作为网络通信资源分配的2个优化目标,根据车联网中行驶车辆的特点,对请求资源车辆和提供资源车辆设置约束条件。在此基础上,采用自适应邻域策略平衡进化过程中种群的收敛性和分布性,并将迭代次数引入自适应度,调节交叉算子和变异算子,使种群中较差的个体也具有遗传性,从而保证种群的多样性。同时,随着迭代次数的增加,种群中较差个体遗传性降低,较好个体遗传能力增强,从而保证种群的优化。仿真结果表明,该算法针对最小化阻塞率和最小化成本这2个目标能够获得满意的优化效果,在迭代次数、车辆数和资源请求数变化情况下都存在最优解,在相同迭代次数下,与基于支配的多目标算法SPEA2和NSGA-II相比具有较低的阻塞率和较好的收敛性。 展开更多
关键词 车联网 通信资源分配 多目标进化算法 MOEA/D算法 阻塞率 成本
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一种基于新型邻域更新策略的MOEA/D算法 认领 引用 被引量：4
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作者 耿焕同 韩伟民 +1 位作者 周山胜 丁洋洋 《计算机科学》 CSCD 北大核心 2019年第5期191-197,共7页
针对MOEA/D算法求解复杂优化问题时,邻域更新策略的无限制替换易造成种群多样性缺失的问题,提出了一种基于新型邻域更新策略的MOEA/D算法(MOEA/D-ENU)。该算法在进化过程中对解的信息进行充分挖掘,按照邻域更新能力对产生的新解进行分类... 针对MOEA/D算法求解复杂优化问题时,邻域更新策略的无限制替换易造成种群多样性缺失的问题,提出了一种基于新型邻域更新策略的MOEA/D算法(MOEA/D-ENU)。该算法在进化过程中对解的信息进行充分挖掘,按照邻域更新能力对产生的新解进行分类,并针对不同类型的新解,自适应地采取不同的邻域更新策略,在保证种群收敛速度的同时,又兼顾了种群的多样性。实验中,选取ZDT,UF,CF等9个函数作为标准测试集,将改进后的算法MOEA/D-ENU与其他5种算法进行对比实验,并以IGD和HV为评估指标。实验结果表明新算法具有更好的收敛性和分布性。 展开更多
关键词 基于分解的多目标进化算法 挖掘解 分类 邻域更新策略
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改进自适应MOEA/D算法的楼宇负荷优化调度 认领 引用 被引量：8
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作者 易灵芝 林佳豪 +2 位作者 刘建康 罗显光 李旺 《计算机工程与应用》 CSCD 北大核心 2022年第2期295-302,共8页
针对负荷侧用户用电电费、新能源消纳率和用电峰谷差等问题,提出了一种改进的自适应基于分解的多目标进化算法,进行楼宇微电网签约住户可控负荷优化调度;通过分析负荷的用电特性,将用电负荷分为五类并分类建立数学模型、优化目标函数和... 针对负荷侧用户用电电费、新能源消纳率和用电峰谷差等问题,提出了一种改进的自适应基于分解的多目标进化算法,进行楼宇微电网签约住户可控负荷优化调度;通过分析负荷的用电特性,将用电负荷分为五类并分类建立数学模型、优化目标函数和约束条件;将广义分解与均匀分配相结合产生新的自适应权重向量使算法非支配解更接近真实帕累托前沿;采用历史经验的思想通过计数SBX和DE两种交叉算子对外部存档的贡献率,运用轮盘赌的方式实现自适应选择策略;通过特性约束条件映射对产生的子代点进行修正,间接地扩大了算法搜索空间,提高了种群多样性。通过测试函数验证了改进的AWS-MOEA/D算法的收敛性和优越性;在某小区楼宇住户调度仿真实验结果表明,所改进的算法在调度后能节省更多的电费,并有效地提高了新能源消纳率。 展开更多
关键词 楼宇微电网 自适应选择策略 自适应权重向量 基于分解的多目标进化算法(MOEA/D) 自动需求响应
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