检索结果-内蒙古大学图书馆

Learning-Driven Algorithm With Dual Evolution Patterns for Solving large-scale multiobjective optimization Problems

IEEE ACCESS 2025年 13卷 30976-30992页

作者： Song, Mingshuo Song, Wei Wee Lai, Khin Jiangnan Univ Sch Artificial Intelligence & Comp Sci Jiangsu Prov Engn Lab Pattern Recognit & Computat Wuxi 214122 Peoples R China Univ Malaya Dept Biomed Engn Kuala Lumpur 50603 Malaysia

Recently, a series of evolutionary algorithms have been proposed to enhance the search efficiency when handling large-scale multiobjective optimization problems (LSMOPs). Among them, learning-driven evolutionary algorithms have shown to be promising by learning the related evolutionary pattern with a specific neural network. However, the learned evolutionary patterns primarily focus on convergence while ignoring diversity, which may potentially cause an inadequate search. In this paper, we propose a learning-driven algorithm with dual evolution patterns (DEPLA) for solving LSMOPs. The main idea is to learn two evolution patterns in the evolutionary process, one focusing on convergence and the other on diversity, which would be helpful to produce promising solutions efficiently. To be specific, DEPLA first divides the population into two parts to be updated with different strategies. A half of the solutions with good convergence and distribution will be updated with the learned evolution pattern focusing on convergence, while the rest of the solutions are repositioned by another learned evolution pattern towards diversity. Through learning the dual evolution patterns in both convergence and diversity, DEPLA is capable of efficiently tackling LSMOPs. Abundant experiments are conducted on the well-known LSMOPs test suites with up to 10,000 decision variables. The experimental results demonstrate that our DEPLA outperforms six state-of-the-art large-scale multiobjective evolutionary algorithms in handling LSMOPs.

关键词： Convergence Search problems Training Neural networks Vectors optimization Marine vehicles Indexes Focusing Evolutionary computation large-scale multiobjective optimization evolutionary pattern learning learning-aided evolution differential evolution neural network

来源：评论

学校读者我要写书评

暂无评论

Learning improvement representations to accelerate evolutionary large-scale multiobjective optimization

引用

INFORMATION SCIENCES 2025年 705卷

作者： Liu, Songbai Wang, Zeyi Ma, Lijia Chen, Jianyong Zhou, Xun Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen 518060 Peoples R China Hong Kong Polytech Univ Dept Data Sci & Artificial Intelligence Hong Kong Peoples R China

large-scale multi-objective optimization problems present significant challenges to traditional evolutionary algorithms due to the exponentially increased search space and computational burden. To address these issues, we propose a novel framework that integrates improvement representation learning into the evolutionary optimization process. It employs neural models to capture performance improvement patterns by learning from the transitions between suboptimal and superior solutions, which are then used to guide the generation of higher-quality offspring. These learnable evolutionary generators explore both the original search space and the learned representation space, enabling more effective navigation and accelerated convergence toward global optima. The proposed framework incorporates simulated binary crossover and differential evolution operators, ensuring adaptability to diverse problem landscapes. Comparative experiments on widely studied benchmark problems demonstrate that our approach achieves comparable cost in computational resources while delivering superior convergence efficiency and solution quality compared to state-of-the-art algorithms. This performance boost is particularly notable for benchmarks with up to 10,000 decision variables, where traditional methods often struggle. These results highlight the potential of combining evolutionary algorithms with representation learning to address the critical challenges of scaling-up optimizations.

关键词： Evolutionary algorithm large-scale multiobjective optimization Improvement representation learning

来源：评论

学校读者我要写书评

暂无评论

An adjoint feature-selection-based evolutionary algorithm for sparse large-scale multiobjective optimization

引用

COMPLEX & INTELLIGENT SYSTEMS 2025年第2期11卷 1-18页

作者： Zhang, Panpan Yin, Hang Tian, Ye Zhang, Xingyi Xian Univ Technol Sch Elect Engn Xian 710048 Peoples R China Anhui Univ Inst Phys Sci & Informat Technol Hefei 230601 Peoples R China Anhui Univ Sch Comp Sci & Technol Hefei 230601 Peoples R China

Sparse large-scale multiobjective optimization problems (sparse LSMOPs) are characterized by an enormous number of decision variables, and their Pareto optimal solutions consist of a majority of decision variables with zero values. This property of sparse LSMOPs presents a great challenge in terms of how to rapidly and precisely search for Pareto optimal solutions. To deal with this issue, this paper proposes an adjoint feature-selection-based evolutionary algorithm tailored for tackling sparse LSMOPs. The proposed optimization strategy combines two distinct feature selection approaches. Specifically, the paper introduces the sequential forward selection approach to investigate independent sparse distribution, denoting it as the best sequence of decision variables for generating a high-quality initial population. Furthermore, it introduces the Relief approach to determine the relative sparse distribution, identifying crucial decisive variables with dynamic updates to guide the population in a promising evolutionary direction. Experiments are conducted on eight benchmark problems and two real-world problems, and experimental results verify that the proposed algorithm outperforms the existing state-of-the-art evolutionary algorithms for solving sparse LSMOPs.

关键词： large-scale multiobjective optimization Sparse optimization Evolutionary algorithm Adjoint feature selection Sparse distribution

来源：评论

学校读者我要写书评

暂无评论

A Flexible Ranking-Based Competitive Swarm Optimizer for large-scale Continuous multiobjective optimization

引用

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 2025年第1期29卷 247-261页

作者： Gao, Xiangzhou Song, Shenmin Zhang, Hu Wang, Zhenkun Harbin Inst Technol Ctr Control Theory & Guidance Technol Harbin 150001 Peoples R China Beijing Electromech Engn Inst Sci & Technol Complex Syst Control & Intelligent A Beijing 100074 Peoples R China Southern Univ Sci & Technol Sch Syst Design & Intelligent Mfg Shenzhen 518055 Peoples R China

Due to the curse of dimensionality, the search efficiency of existing operators in large-scale decision space deteriorates dramatically. The competitive swarm optimizer (CSO)-based framework has great potential in tackling large-scale single-objective optimization problems. However, the existing CSOs only focus on the loser to winner learning paradigm and neglect the significance of the winner determination mechanism for large-scale search, which makes the algorithm difficult to escape from local optima. To remedy this issue, a flexible ranking-based CSO has been tailored for handling large-scale multiobjective optimization problems (MOPs). Concretely, a novel winner determination strategy is introduced to broadly identify high-quality individuals in the population to enhance diversity maintenance. In addition, a special competitive mechanism is adopted to guide the search direction, which is capable of efficiently increasing search space utilization. The simulation results validate that the proposed algorithm can significantly enhance the exploration and exploitation ability of the conventional CSO, and outperforms several state-of-the-art large-scale multiobjective optimization algorithms on both large-scale benchmark MOPs and application examples.

关键词： Sociology optimization Search problems Convergence Aerospace electronics Maintenance engineering Heuristic algorithms Competitive swarm optimizer (CSO) large-scale multiobjective optimization search paradigm

来源：评论

学校读者我要写书评

暂无评论

Evolutionary large-scale multiobjective optimization via Autoencoder-Based Problem Transformation

引用

IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2024年第4期8卷 2709-2722页

作者： Liu, Songbai Li, Jun Lin, Qiuzhen Tian, Ye Li, Jianqiang Tan, Kay Chen Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen 518060 Peoples R China Anhui Univ Inst Phys Sci & Informat Technol Key Lab Intelligent Comp & Signal Proc Minist Educ Hefei 230601 Peoples R China Hong Kong Polytech Univ Dept Comp Hong Kong Peoples R China

Addressing the challenge of efficiently handling high-dimensional search spaces in solving large-scale multiobjective optimization problems (LMOPs) becomes an emerging research topic in evolutionary computation. In response, this paper proposes a new evolutionary optimizer with a tactic of autoencoder-based problem transformation (APT). The APT involves creating an autoencoder to learn the relative importance of each variable by competitively reconstructing the dominated and non-dominated solutions. Using the learned importance, all variables are divided into multiple groups without consuming any function evaluations. The number of groups dynamically increases according to the population's evolutionary status. Each variable group has an associated autoencoder, transforming the search space into an adaptable small-scale representation space. Thus, the search process occurs within these dynamic representation spaces, leading to effective production of offspring solutions. To assess the effectiveness of APT, extensive testing is performed on benchmark suites and real-world LMOPs, encompassing variable sizes ranging from 10(3) to 10(4). The comparative results demonstrate the advantages of our proposed optimizer in solving these LMOPs with a limited budget of 10(5) function evaluations.

关键词： Evolutionary algorithm large-scale multiobjective optimization autoencoder problem transformation

来源：评论

学校读者我要写书评

暂无评论

Evolutionary Multitasking for large-scale multiobjective optimization

引用

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 2023年第4期27卷 863-877页

作者： Liu, Songbai Lin, Qiuzhen Feng, Liang Wong, Ka-Chun Tan, Kay Chen Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen 518060 Peoples R China City Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen 518060 Peoples R China Chongqing Univ Coll Comp Sci Chongqing 400044 Peoples R China City Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China Hong Kong Polytech Univ Dept Comp Hong Kong Peoples R China

Evolutionary transfer optimization (ETO) has been becoming a hot research topic in the field of evolutionary computation, which is based on the fact that knowledge learning and transfer across the related optimization exercises can improve the efficiency of others. However, rare studies employ ETO to solve large-scale multiobjective optimization problems (LMOPs). To fill this research gap, this article proposes a new multitasking ETO algorithm via a powerful transfer learning model to simultaneously solve multiple LMOPs. In particular, inspired by adversarial domain adaptation in transfer learning, a discriminative reconstruction network (DRN) model (containing an encoder, a decoder, and a classifier) is created for each LMOP. At each generation, the DRN is trained by the currently obtained nondominated solutions for all LMOPs via backpropagation with gradient descent. With this well-trained DRN model, the proposed algorithm can transfer the solutions of source LMOPs directly to the target LMOP for assisting its optimization, can evaluate the correlation between the source and target LMOPs to control the transfer of solutions, and can learn a dimensional-reduced Pareto-optimal subspace of the target LMOP to improve the efficiency of transfer optimization in the large-scale search space. Moreover, we propose a real-world multitasking LMOP suite to simulate the training of deep neural networks (DNNs) on multiple different classification tasks. Finally, the effectiveness of the proposed algorithm has been validated in this real-world problem suite and the other two synthetic problem suites.

关键词： Evolutionary algorithm (EA) large-scale multiobjective optimization multitasking transfer learning

来源：评论

学校读者我要写书评

暂无评论

Evolutionary large-scale multiobjective optimization for Ratio Error Estimation of Voltage Transformers

引用

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 2020年第5期24卷 868-881页

作者： He, Cheng Cheng, Ran Zhang, Chuanji Tian, Ye Chen, Qin Yao, Xin Southern Univ Sci & Technol Dept Comp Sci & Engn Univ Key Lab Evolving Intelligent Syst Guangdong Shenzhen 518055 Peoples R China Huazhong Univ Sci & Technol Sch Elect & Elect Engn State Key Lab Adv Electromagnet Engn & Technol Wuhan 430074 Peoples R China Anhui Univ Inst Phys Sci & Informat Technol Hefei 230601 Peoples R China Univ Birmingham Sch Comp Sci CERCIA Birmingham B15 2TT W Midlands England

Ratio error (RE) estimation of the voltage transformers (VTs) plays an important role in modern power delivery systems. Existing RE estimation methods mainly focus on periodical calibration but ignore the time-varying property. Consequently, it is difficult to efficiently estimate the state of the VTs in real time. To address this issue, we formulate a time-varying RE estimation (TREE) problem into a large-scale multiobjective optimization problem, where the multiple objectives and inequality constraints are formulated by statistical and physical rules extracted from the power delivery systems. Furthermore, a set of TREE problems from different substations is systematically formulated into a benchmark test suite for characterizing their different properties. The formulation of these TREE problems not only transfers an expensive RE estimation task to a relatively cheaper optimization problem but also promotes the research in large-scale multiobjective optimization by providing a real-world benchmark test suite with complex variable interactions and correlations to different objectives. To the best of our knowledge, this is the first time to formulate a real-world problem into a benchmark test suite for large-scale multiobjective optimization, and it is also the first work proposing to solve TREE problems via evolutionary multiobjective optimization.

关键词： Voltage measurement optimization Calibration Substations Estimation Error analysis Benchmark testing Benchmark test suite inequality constraint large-scale multiobjective optimization time-varying ratio error estimation (TREE) voltage transformer (VT)

来源：评论

学校读者我要写书评

暂无评论

Solving large-scale multiobjective optimization Problems With Sparse Optimal Solutions via Unsupervised Neural Networks

引用

IEEE TRANSACTIONS ON CYBERNETICS 2021年第6期51卷 3115-3128页

作者： Tian, Ye Lu, Chang Zhang, Xingyi Tan, Kay Chen Jin, Yaochu Anhui Univ Key Lab Intelligent Comp & Signal Proc Minist Educ Inst Phys Sci & Informat Technol Hefei 230601 Peoples R China City Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China Anhui Univ Sch Comp Sci & Technol Minist Educ Key Lab Intelligent Comp & Signal Proc Hefei 230601 Peoples R China Univ Surrey Dept Comp Sci Guildford GU2 7XH Surrey England

Due to the curse of dimensionality of search space, it is extremely difficult for evolutionary algorithms to approximate the optimal solutions of large-scale multiobjective optimization problems (LMOPs) by using a limited budget of evaluations. If the Pareto-optimal subspace is approximated during the evolutionary process, the search space can be reduced and the difficulty encountered by evolutionary algorithms can be highly alleviated. Following the above idea, this article proposes an evolutionary algorithm to solve sparse LMOPs by learning the Pareto-optimal subspace. The proposed algorithm uses two unsupervised neural networks, a restricted Boltzmann machine, and a denoising autoencoder to learn a sparse distribution and a compact representation of the decision variables, where the combination of the learnt sparse distribution and compact representation is regarded as an approximation of the Pareto-optimal subspace. The genetic operators are conducted in the learnt subspace, and the resultant offspring solutions then can be mapped back to the original search space by the two neural networks. According to the experimental results on eight benchmark problems and eight real-world problems, the proposed algorithm can effectively solve sparse LMOPs with 10000 decision variables by only 100000 evaluations.

关键词： optimization Neural networks Evolutionary computation Search problems Computer science Sociology Statistics Denoising autoencoder (DAE) large-scale multiobjective optimization Pareto-optimal subspace restricted Boltzmann machine (RBM) sparse Pareto-optimal solutions

来源：评论

学校读者我要写书评

暂无评论

Counterintuitive Experimental Results in Evolutionary large-scale multiobjective optimization

引用

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 2022年第6期26卷 1609-1616页

作者： Pang, Lie Meng Ishibuchi, Hisao Shang, Ke Southern Univ Sci & Technol Dept Comp Sci & Engn Guangdong Prov Key Lab Brain Inspired Intelligent Shenzhen 518055 Peoples R China

Recently, large-scale multiobjective optimization has received increasing attention from the evolutionary multiobjective optimization (EMO) community. This has led to the emergence of a specialized research area called evolutionary large-scale multiobjective optimization (ELMO). In general, it is believed that multiobjective optimization problems become more difficult as the number of decision variables increases. However, the following two counterintuitive observations are obtained from careful examinations of recent ELMO studies. One is that experimental results on some large-scale multiobjective test problems were improved by increasing the number of decision variables. The other is that better results were obtained for some other large-scale multiobjective test problems by conventional EMO algorithms (EMOAs) than state-of-the-art ELMO algorithms (ELMOAs). These observations suggest that ELMOAs have not always been evaluated on appropriate test problems. Moreover, their performance is not always better than the performance of conventional EMOAs. In this letter, we first re-examine the performance of ELMOAs and conventional EMOAs on a wide variety of scalable multiobjective test problems. Then, counterintuitive experimental results are analyzed using the anytime performance evaluation scheme and distributions of randomly generated initial solutions. Based on the analysis, suggestions on how to handle large-scale multiobjective test problems with counterintuitive results are proposed.

关键词： Anytime performance evaluation evolutionary multiobjective optimization (EMO) large-scale multiobjective optimization test problems

来源：评论

学校读者我要写书评

暂无评论

Improved Evolutionary Operators for Sparse large-scale multiobjective optimization Problems

引用

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 2024年第2期28卷 460-473页

作者： Kropp, Ian Nejadhashemi, A. Pouyan Deb, Kalyanmoy Ohio Northern Univ Dept Elect & Comp Engn & Comp Sci Ada OH 45810 USA Michigan State Univ Dept Elect & Comp Engn E Lansing MI 48824 USA

Many critical science, societal, and engineering fields contain large-scale multiobjective optimization problems (LSMOPs), comprised of many decision variables. However, as the number of decision variables increases, optimization algorithms face exponentially large search spaces, thereby exhibiting a degraded performance. Nonetheless, LSMOPs whose optimal solutions correspond to sparse variable vectors can be solved more efficiently by evolutionary multiobjective optimization (EMO) algorithms. Despite the great recent strides in developing generic EMO algorithms for sparse LSMOPs, there is still room for improvement. Specifically, algorithms still struggle to find convergent and diverse Pareto fronts in an acceptable amount of time when solving sparse LSMOPs with thousands of decision variables. To better solve sparse LSMOPs, we propose a novel set of evolutionary operators to adapt small-scale EMO algorithms for sparse LSMOPs. These simple, novel, and effective operators include varied striped sparse population sampling (VSSPS), sparse simulated binary crossover (S-SBX), and sparse polynomial mutation (S-PM). These operators, combined with nondominated sorting genetic algorithm II (NSGA-II), make the proposed S-NSGA-II algorithm. S-NSGA-II runs near-universally faster than existing methods for problems containing up to 6400 decision variables, while performing as well as or better than contemporary sparse LSMOP algorithms with respect to hypervolume, especially, with problems with larger than 5000 decision variables.

关键词： optimization Statistics Sociology Genomics Bioinformatics Runtime Convergence Crossover evolutionary optimization large-scale multiobjective optimization mutation population sampling sparse optimization

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：