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Even Search in a Promising Region for Constrained Multi-Objective Optimization

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IEEE/CAA Journal of Automatica Sinica 2024年第2期11卷 474-486页

作者： Fei Ming wenyin gong Yaochu Jin School of Computer Science China University of GeosciencesWuhan 430074China IEEE Faculty of Technology Bielefeld UniversityNorth Rhine-Westphalia 33619Germany School of Engineering Westlake UniversityHangzhou 310030China

In recent years, a large number of approaches to constrained multi-objective optimization problems(CMOPs) have been proposed, focusing on developing tweaked strategies and techniques for handling constraints. However, an overly finetuned strategy or technique might overfit some problem types,resulting in a lack of versatility. In this article, we propose a generic search strategy that performs an even search in a promising region. The promising region, determined by obtained feasible non-dominated solutions, possesses two general ***, the constrained Pareto front(CPF) is included in the promising region. Second, as the number of feasible solutions increases or the convergence performance(i.e., approximation to the CPF) of these solutions improves, the promising region shrinks. Then we develop a new strategy named even search,which utilizes the non-dominated solutions to accelerate convergence and escape from local optima, and the feasible solutions under a constraint relaxation condition to exploit and detect feasible regions. Finally, a diversity measure is adopted to make sure that the individuals in the population evenly cover the valuable areas in the promising region. Experimental results on 45 instances from four benchmark test suites and 14 real-world CMOPs have demonstrated that searching evenly in the promising region can achieve competitive performance and excellent versatility compared to 11 most state-of-the-art methods tailored for CMOPs.

关键词： Constrained multi-objective optimization even search evolutionary algorithms promising region real-world problems

Constrained Multi-Objective Optimization With Deep Reinforcement Learning Assisted Operator Selection

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IEEE/CAA Journal of Automatica Sinica 2024年第4期11卷 919-931页

作者： Fei Ming wenyin gong Ling Wang Yaochu Jin the School of Computer Science China University of GeosciencesWuhan 430074China the Department of Automation Tsinghua UniversityBeijing 100084China the Faculty of Technology Bielefeld UniversityNorth Rhine-Westphalia33619 BielefeldGermany

Solving constrained multi-objective optimization problems with evolutionary algorithms has attracted considerable *** constrained multi-objective optimization evolutionary algorithms(CMOEAs)have been developed with the use of different algorithmic strategies,evolutionary operators,and constraint-handling *** performance of CMOEAs may be heavily dependent on the operators used,however,it is usually difficult to select suitable operators for the problem at ***,improving operator selection is promising and necessary for *** work proposes an online operator selection framework assisted by Deep Reinforcement *** dynamics of the population,including convergence,diversity,and feasibility,are regarded as the state;the candidate operators are considered as actions;and the improvement of the population state is treated as the *** using a Q-network to learn a policy to estimate the Q-values of all actions,the proposed approach can adaptively select an operator that maximizes the improvement of the population according to the current state and thereby improve the algorithmic *** framework is embedded into four popular CMOEAs and assessed on 42 benchmark *** experimental results reveal that the proposed Deep Reinforcement Learning-assisted operator selection significantly improves the performance of these CMOEAs and the resulting algorithm obtains better versatility compared to nine state-of-the-art CMOEAs.

关键词： Constrained multi-objective optimization deep Qlearning deep reinforcement learning(DRL) evolutionary algorithms evolutionary operator selection

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基于帕累托前沿关系求解约束多目标优化问题

系统仿真学报 2024年第4期36卷 901-914页

作者：王昱博胡成玉龚文引中国地质大学计算机学院湖北武汉430074

为解决约束多目标优化问题中的平衡约束满足与目标函数优化以及可行域复杂等挑战,提出了基于不同帕累托前沿关系的分类搜索方法。提出一种双种群双阶段框架:进化一个辅助种群Pa和一个主种群Pm,并将进化过程分为学习阶段和搜索阶段。学... 详细信息

为解决约束多目标优化问题中的平衡约束满足与目标函数优化以及可行域复杂等挑战,提出了基于不同帕累托前沿关系的分类搜索方法。提出一种双种群双阶段框架:进化一个辅助种群Pa和一个主种群Pm,并将进化过程分为学习阶段和搜索阶段。学习阶段,种群Pa向UPF(unconstrained Pareto front)进行搜索,而种群Pm向CPF(constrained Pareto front)进行搜索,旨在探索UPF与CPF之间的关系;完成学习后,对不同问题的UPF与CPF关系进行分类,以指导后续搜索策略;在搜索阶段,根据不同的分类关系,调整种群Pa的搜索策略,旨在使种群Pa为种群Pm提供更有效的辅助信息。基于此算法框架,对不同类型约束多目标优化问题的帕累托前沿关系进行了分类,实现了对CPF更有效的搜索。实验结果表明:所提算法与其他7种先进的约束多目标优化算法相比具有更显著的性能优势。通过学习与利用UPF与CPF的关系,能够选择更合适的搜索策略去应对具有不同特性的约束多目标优化问题,以获得更具优势的最终解集。

关键词：约束多目标优化帕累托前沿关系双种群学习阶段搜索阶段

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Even Search in a Promising Region for Constrained Multi-Objective Optimization

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Data-driven evolutionary sampling optimization for expensive problems

自动化学报（英文版） 2024年第2期

作者： Fei Ming wenyin gong Yaochu Jin

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Journal of Systems Engineering and Electronics 2021年第2期32卷 318-330页

作者： ZHEN Huixiang gong wenyin WANG Ling School of Computer Science China University of GeosciencesWuhan 430074China Department of Automation Tsinghua UniversityBeijing 100084China

Surrogate models have shown to be effective in assisting evolutionary algorithms(EAs)for solving computationally expensive complex optimization ***,the effectiveness of the existing surrogate-assisted evolutionary algorithms still needs to be improved.A data-driven evolutionary sampling optimization(DESO)framework is proposed,where at each generation it randomly employs one of two evolutionary sampling strategies,surrogate screening and surrogate local search based on historical data,to effectively balance global and local *** DESO,the radial basis function(RBF)is used as the surrogate model in the sampling strategy,and different degrees of the evolutionary process are used to sample candidate *** sampled points by sampling strategies are evaluated,and then added into the database for the updating surrogate model and population in the next *** get the insight of DESO,extensive experiments and analysis of DESO have been *** proposed algorithm presents superior computational efficiency and robustness compared with five state-of-the-art algorithms on benchmark problems from 20 to 200 ***,DESO is applied to an airfoil design problem to show its effectiveness.

关键词： evolutionary algorithm(EA) surrogate model datadriven evolutionary sampling airfoil design

Competitive and Cooperative-Based Strength Pareto Evolutionary Algorithm for Green Distributed Heterogeneous Flow Shop Scheduling

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Intelligent Automation & Soft Computing 2023年第8期37卷 2077-2101页

作者： Kuihua Huang Rui Li wenyin gong Weiwei Bian Rui Wang College of System Engineering National University of Defense TechnologyChangsha410073China School of Computer Science China University of GeosciencesWuhan430074China Equipment General Technology Laboratory Beijing Mechanical Equipment Research InstituteBeijing100854China

This work aims to resolve the distributed heterogeneous permutation flow shop scheduling problem(DHPFSP)with minimizing makespan and total energy consumption(TEC).To solve this NP-hard problem,this work proposed a competitive and cooperative-based strength Pareto evolutionary algorithm(CCSPEA)which contains the following features:1)An initialization based on three heuristic rules is developed to generate a population with great diversity and convergence.2)A comprehensive metric combining convergence and diversity metrics is used to better represent the heuristic information of a solution.3)A competitive selection is designed which divides the population into a winner and a loser swarms based on the comprehensive metric.4)A cooperative evolutionary schema is proposed for winner and loser swarms to accelerate the convergence of global search.5)Five local search strategies based on problem knowledge are designed to improve convergence.6)Aproblem-based energy-saving strategy is presented to reduce ***,to evaluate the performance of CCSPEA,it is compared to four state-of-art and run on 22 instances based on the Taillard *** numerical experiment results demonstrate that 1)the proposed comprehensive metric can efficiently represent the heuristic information of each solution to help the later step divide the population.2)The global search based on the competitive and cooperative schema can accelerate loser solutions convergence and further improve the winner’s exploration.3)The problembased initialization,local search,and energy-saving strategies can efficiently reduce the makespan and TEC.4)The proposed CCSPEA is superior to the state-of-art for solving DHPFSP.

关键词： Distributed heterogeneous flow shop scheduling green scheduling SPEA2 competitive and cooperative

Load Optimization Scheduling of Chip Mounter Based on Hybrid Adaptive Optimization

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Complex System Modeling and Simulation 2023年第1期3卷 1-11页

作者： Xuesong Yan Hao Zuo Chengyu Hu wenyin gong Victor S.Sheng School of Computer Science China University of GeosciencesWuhan 430074China Department of Computer Science Texas Tech UniversityLubbockTX 79409−3104USA

A chip mounter is the core equipment in the production line of the surface-mount technology,which is responsible for finishing the mount *** is the most complex and time-consuming stage in the production ***,it is of great significance to optimize the load balance and mounting efficiency of the chip mounter and improve the mounting efficiency of the production *** this study,according to the specific type of chip mounter in the actual production line of a company,a maximum and minimum model is established to minimize the maximum cycle time of the chip mounter in the production *** production efficiency of the production line can be improved by optimizing the workload scheduling of each chip *** this basis,a hybrid adaptive optimization algorithm is proposed to solve the load scheduling problem of the *** hybrid algorithm is a hybrid of an adaptive genetic algorithm and the improved ant colony *** combines the advantages of the two algorithms and improves their global search ability and convergence *** experimental results show that the proposed hybrid optimization algorithm has a good optimization effect and convergence in the load scheduling problem of chip mounters.

关键词： Surface Mount Technology(SMT) chip mounter load optimization scheduling adaptive genetic algorithm ant colony algorithm

Nonlinear Equations Solving with Intelligent Optimization Algorithms: A Survey

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Complex System Modeling and Simulation 2021年第1期1卷 15-32页

作者： wenyin gong Zuowen Liao Xianyan Mi Ling Wang Yuanyuan Guo School of Computer Science China University of GeosciencesWuhan 430074China Development Research Center Beibu Gulf UniversityQinzhou 535000China College of Economics and Management Beibu Gulf UniversityQinzhou 535000China Department of Automation Tsinghua UniversityBeijing 100084China Cvent Company FrederictonE3B 5H8Canada.

Nonlinear Equations(NEs),which may usually have multiple roots,are ubiquitous in diverse *** of the main purposes of solving NEs is to locate as many roots as possible simultaneously in a single run,however,it is a difficult and challenging task in numerical *** recent years,Intelligent Optimization Algorithms(IOAs)have shown to be particularly effective in solving *** paper provides a comprehensive survey on IOAs that have been exploited to locate multiple roots of *** paper first revisits the fundamental definition of NEs and reviews the most recent development of the transformation ***,solving NEs with IOAs is reviewed,followed by the benchmark functions and the performance comparison of several state-of-the-art ***,this paper points out the challenges and some possible open issues for solving NEs.

关键词： Nonlinear Equations(NEs) Intelligent Optimization Algorithms(IOA) multiple roots location transformation techniques diversity preservation

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基于ε占优的正交多目标差分演化算法研究

计算机研究与发展 2009年第4期46卷 655-666页

作者：龚文引蔡之华中国地质大学计算机学院武汉430074

演化多目标优化是目前演化计算中热门研究方向之一.但是,要设计一种高效、鲁棒的演化多目标优化算法,使其找到接近最优和完整的非劣解集是一项很困难的任务.为了能有效求解多目标优化问题,提出了一种新的多目标差分演化算法.新算法具有... 详细信息

演化多目标优化是目前演化计算中热门研究方向之一.但是,要设计一种高效、鲁棒的演化多目标优化算法,使其找到接近最优和完整的非劣解集是一项很困难的任务.为了能有效求解多目标优化问题,提出了一种新的多目标差分演化算法.新算法具有如下特征:1)利用正交实验设计和连续空间量化的方法产生初始群体,使得初始群体中的个体可以均匀分布于搜索空间,并且可以使好的个体在演化过程中得到利用;2)采用Archive群体保存非劣解,并利用ε占优方法更新Archive群体,从而可以使算法较快获得分布很好的Pareto解集;3)为了加快算法收敛,提出一种基于随机选择和精英选择的混合选择机制.通过8个标准测试函数对新算法进行测试,并与其他一些多目标演化算法进行比较,其结果表明新算法可以有效逼近真实Pareto前沿且分布均匀,并且在收敛性和多样性的求解精度和稳定性上与其他算法相当.同时,通过实验的方法验证了新算法改进之处的有效性,并进一步讨论了差分演化算法中CR取值和混合选择机制中选择控制参数λ取值对算法性能的影响.

关键词：多目标优化差分演化算法正交实验设计 ε占优混合选择