检索结果-内蒙古大学图书馆

Knee-oriented expensive many-objective optimization via aggregation-dominance: A multi-task perspective

SWARM AND EVOLUTIONARY COMPUTATION 2025年 92卷

作者： Tang, Junfeng Wang, Handing Jin, Yaochu Xidian Univ Sch Artificial Intelligence Xian 710071 Peoples R China Westlake Univ Sch Engn Hangzhou 310030 Peoples R China

Given the costs to implement whole Pareto optimal solutions, users often prefer solutions of interest, like knee points, which represent naturally preferred solutions without a specific bias. Recent surrogate-assisted multi-objective evolutionary algorithms (SAMOEAs) incorporating knee identification techniques have been suggested, but most of them cannot find knee solutions for expensive many-objective optimization problems. This work proposes a Kriging-assisted evolutionary multi-task algorithm with aggregation-dominance. The aggregation-dominance approach identifies knee points on an estimated Pareto front, from which subproblems are created and solved in parallel via Kriging-assisted multi-task optimization for guiding search knee solutions. Additionally, our proposed infill solutions selection strategy focuses on re-evaluating solutions converging in regions of interest. Experimental results on knee-oriented benchmark problems show that our algorithm outperforms state-of-the-art methods, with aggregation-dominance surpassing five existing knee identification techniques. We also validate the algorithm's performance on the portfolio allocation problem.

关键词： expensive many-objective optimization Knee solution Pareto front estimation Kriging model Evolutionary multi-task optimization

来源：评论

学校读者我要写书评

暂无评论

An expensive many-objective optimization Algorithm Based on Efficient Expected Hypervolume Improvement

引用

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 2023年第6期27卷 1822-1836页

作者： Pang, Yong Wang, Yitang Zhang, Shuai Lai, Xiaonan Sun, Wei Song, Xueguan Dalian Univ Technol Sch Mech Engn Dalian 116024 Peoples R China

The expected hypervolume improvement (EHVI) is one of the most popular infill criteria for multiobjective optimization problems. Although it has a significant advantage in exploring potential Pareto-optimal solutions, it has rarely been applied in many-objective problems due to its high computational cost. To address this issue, this article proposes an expensive many-objective optimization algorithm based on the framework of nondominated sorting genetic algorithm III (NSGA-III) and assisted by the kriging surrogate models. In the proposed algorithm, the Monte Carlo sampling (MCS) method for EHVI estimation is improved by importance sampling, in which only one sampling process is required during the entire optimization process using a uniform distribution in normalized objective space. Considering the predicted uncertainty from the kriging model, an uncertainty-assisted nondominated sorting approach is proposed to substitute for the conventional approach in NSGA-III. In the proposed method, the predicted uncertainty is incorporated into the objective space as one independent dimension for nondominated sorting, which can enable the exploration of potential points with desirable EHVI values. In addition, the proposed algorithm considers the diversity of the solutions by de-emphasizing the pursuit of the best EHVI. The experimental results on benchmark problems demonstrate that the proposed EHVI calculation method can save computational costs compared with MCS and indicate the superiority of the proposed algorithm over the others.

关键词： Uncertainty Predictive models optimization Sorting Prediction algorithms Statistics Sociology Expected hypervolume improvement (EHVI) expensive many-objective optimization importance sampling (IS) kriging

来源：评论

学校读者我要写书评

暂无评论

A diverse/converged individual competition algorithm for computationally expensive many-objective optimization

引用

APPLIED INTELLIGENCE 2024年第3期54卷 2851-2866页

作者： Lin, Jie Zhang, Sheng Xin Zheng, Shao Yong Natl Nat Sci Fdn China Guangzhou Peoples R China

Surrogate-assisted evolutionary algorithms (SAEAs) are popular for solving expensive optimization problems. However, most existing SAEAs are designed for solving single-objective or multiobjective optimization problems with two or three objectives. Few works had been reported to deal with expensive many-objective optimization problems with more than three objectives because of two difficulties. One is the curse of dimensionality caused by many-objective problems, and the other is the fewer computational resources available in a limited time for expensive optimization problems. Since an effective selection method can better solve the many-objective optimization problems, high-efficiency search and accurate model can save computational resources for expensive optimization problems, this paper proposes a diverse/converged individual competition algorithm, which owns a novel diverse/converged individual competition selection mechanism, a hybrid search mechanism, and a segmentation approach. The diverse/converged individual competition selection mechanism maintains a good balance between the convergence and diversity of the selected solutions for solving many-objective optimization problems. The hybrid search mechanism performs a memetic search and genetic search at different stages of the evolution process to further generate superior solutions. The segmentation approach uses two different populations with small numbers to build two surrogate models which will predict different areas, and it can improve the accuracy of the prediction. The proposed algorithm is compared with several state-of-art algorithms on widely used benchmark functions. The experimental results show that the proposed algorithm performs significantly better than the compared algorithms.

关键词： expensive many-objective optimization Surrogate-assisted evolutionary algorithms Surrogate model

来源：评论

学校读者我要写书评

暂无评论

A Classification-Based Surrogate-Assisted Evolutionary Algorithm for expensive many-objective optimization

引用

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 2019年第1期23卷 74-88页

作者： Pan, Linqiang He, Cheng Tian, Ye Wang, Handing Zhang, Xingyi Jin, Yaochu Huazhong Univ Sci & Technol Key Lab Image Informat Proc & Intelligent Control Educ Minist China Sch Automat Wuhan 430074 Hubei Peoples R China Zhengzhou Univ Light Ind Sch Elect & Informat Engn Zhengzhou 450002 Henan Peoples R China Univ Surrey Dept Comp Sci Guildford GU2 7XH Surrey England Anhui Univ Key Lab Intelligent Comp & Signal Proc Minist Educ Sch Comp Sci & Technol Hefei 230039 Anhui Peoples R China Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110819 Liaoning Peoples R China

Surrogate-assisted evolutionary algorithms (SAEAs) have been developed mainly for solving expensive optimization problems where only a small number of real fitness evaluations are allowed. Most existing SAEAs are designed for solving low-dimensional single or multiobjective optimization problems, which are not well suited for many-objective optimization. This paper proposes a surrogate-assisted many-objective evolutionary algorithm that uses an artificial neural network to predict the dominance relationship between candidate solutions and reference solutions instead of approximating the objective values separately. The uncertainty information in prediction is taken into account together with the dominance relationship to select promising solutions to be evaluated using the real objective functions. Our simulation results demonstrate that the proposed algorithm outperforms the state-of-the-art evolutionary algorithms on a set of many-objective optimization test problems.

关键词： Classification expensive many-objective optimization Pareto dominance surrogate-assisted evolutionary optimization

来源：评论

学校读者我要写书评

暂无评论

A framework for expensive many-objective optimization with Pareto-based bi-indicator infill sampling criterion

引用

MEMETIC COMPUTING 2022年第2期14卷 179-191页

作者： Song, Zhenshou Wang, Handing Xu, Hongbin Xidian Univ Sch Artificial Intelligence Xian 710071 Shaanxi Peoples R China South China Univ Technol Sch Software Engn Guangzhou 510006 Peoples R China

Surrogate-assisted many-objective optimization is to locate Pareto optimal solutions using a limited number of function evaluations. Most existing surrogate-assisted evolutionary algorithms are designed to embed in a specific many-objective evolutionary algorithm. The Pareto-based bi-indicator infill sampling criterion has been proven to be effective in saving expensive evaluations in surrogate-assisted multi-objective evolutionary algorithms. It introduces two indicators measuring the convergence and diversity as two optimization objectives. In this paper, we extend our previous work to propose a generic framework with a Pareto-based bi-indicator infill sampling criterion for expensive many-objective optimization. The proposed framework gives a general method for traditional MOEAs to solve the expensive optimization task. We incorporate the proposed framework into the reference vectors guided evolutionary algorithm and compare it with another surrogate-assisted reference vectors guided evolutionary algorithm. Empirical studies on DTLZ problems with more than three objectives demonstrate the effectiveness and superiority of the proposed framework and the incorporated algorithm.

关键词： Pareto-based bi-indicatior infill sampling criterion Surrogate-assisted evolutionary algorithm expensive many-objective optimization

来源：评论

学校读者我要写书评

暂无评论

An improved bagging ensemble surrogate-assisted evolutionary algorithm for expensive many-objective optimization

引用

APPLIED INTELLIGENCE 2022年第6期52卷 5949-5965页

作者： Gu, Qinghua Zhang, Xiaoyue Chen, Lu Xiong, Naixue Xian Univ Architecture & Technol Sch Management Xian Peoples R China Xian Univ Architecture & Technol Xian Key Lab Intelligent Ind Percept Calculat & D Xian Peoples R China Northeastern State Univ Dept Math & Comp Sci Tahlequah OK USA

When the surrogate-assisted evolutionary algorithm is used to solve expensive many-objective optimization problems, the surrogate is used to approximate the expensive fitness functions. However, with the increase of the number of objectives, the approximate error of the surrogate will accumulate gradually, and the computational cost will also increase sharply. This paper proposes an improved bagging ensemble surrogate-assisted evolutionary algorithm (IBE-CSEA) to solve these problems. An ensemble classifier is used to classify the offspring instead of building the surrogate to approximate the fitness function of each objective. Firstly, a group of classification boundary individuals are selected one by one from the individuals evaluated by the expensive fitness function. All the individuals evaluated by the expensive fitness function are divided into two categories;Secondly, the individuals in these two categories are divided into the training set and the test set. The training set is used to train an improved bagging ensemble classifier. The test set is used to calculate the reliability of the classification;Finally, the classification results and the reliability are used to select the promising individuals for expensive fitness function evaluation. Compared with the current popular surrogate-assisted evolutionary algorithm, IBE-CSEA algorithm is more competitive.

关键词： expensive many-objective optimization Evolutionary algorithm Surrogate-assisted Improved bagging ensemble Classification

来源：评论

学校读者我要写书评

暂无评论

A Hybrid Surrogate-Assisted Evolutionary Algorithm for Computationally expensive many-objective optimization

A Hybrid Surrogate-Assisted Evolutionary Algorithm for Compu...

引用

IEEE Congress on Evolutionary Computation (IEEE CEC)

作者： Wan, Kanzhen He, Cheng Camacho, Auraham Shang, Ke Cheng, Ran Ishibuchi, Hisao Southern Univ Sci & Technol Shenzhen Key Lab Computat Intelligence Dept Comp Sci & Engn Shenzhen 518055 Guangdong Peoples R China Harbin Inst Technol Sch Comp Sci & Engn Harbin 150001 Heilongjiang Peoples R China CINVESTAV Tamaulipas Victoria Tamaulipas Mexico

ISBN: (纸本)9781728121536

many real-world optimization problems are challenging because the evaluation of solutions is computationally expensive. As a result, the number of function evaluations is limited. Surrogate-assisted evolutionary algorithms are promising approaches to tackle this kind of problems. However, their performance highly depends on the number of objectives. Thus, they may not be suitable for many-objective optimization. This paper proposes a novel hybrid algorithm for computationally expensive many-objective optimization, called C-M-EA. The proposed approach combines two surrogate-assisted evolutionary algorithms during the search process. We compare the performance of the proposed approach with seven multi-objective evolutionary algorithms. Our experimental results show that our approach is competitive for solving computationally expensive many-objective optimization problems.

关键词： Surrogate-assisted evolutionary optimization expensive many-objective optimization Hybrid optimization

来源：评论

学校读者我要写书评

暂无评论

A Kriging-assisted evolutionary algorithm with multiple infill sampling for expensive many-objective optimization

引用

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2024年 135卷

作者： Zhu, Qingling Kang, Gaoli Wu, Xunfeng Lin, Qiuzhen Tang, Huimei Chen, Jianyong Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen Peoples R China

Surrogate -assisted evolutionary algorithms (SAEAs) have been extensively used to solve computationally expensive multi -objective optimization problems (MOPs) as they can obtain a set of satisfyingly optimal solutions while remaining within a limited computational budget. Nevertheless, the expensive MOPs with more than three objectives have received little attention, and most existing SAEAs fail to achieve satisfactory results when solving them. Therefore, to fill this research gap, a Kriging-assisted evolutionary algorithm with multiple infill sampling for solving expensive many -objective optimization problems is proposed. In this paper, to balance exploration and exploitation, a new environmental selection operator is proposed, which is composed of three procedures conducted consecutively. In addition, a new multiple infill sampling strategy is proposed to select the most representative solutions for real function evaluations and model updates. Furthermore, to limit the computational costs of constructing/updating the surrogate model, a new archive update strategy is proposed to maneuver the training data set. In experiments, our method is verified on some benchmark problems. The experimental results demonstrate that the proposed algorithm shows promising performance when compared with four state-of-the-art SAEAs for solving expensive many -objective optimization problems.

关键词： Surrogate-assisted evolutionary algorithm Kriging model Infill sampling expensive many-objective optimization

来源：评论

学校读者我要写书评

暂无评论

A composite surrogate-assisted evolutionary algorithm for expensive many-objective optimization

引用

EXPERT SYSTEMS WITH APPLICATIONS 2024年 236卷

作者： Zhai, Zhaomin Tan, Yanyan Li, Xiaojie Li, Junqing Zhang, Huaxiang Shandong Normal Univ Sch Informat Sci & Engn Jinan 250358 Peoples R China Shandong Normal Univ Inst Data Sci & Technol Jinan Peoples R China

In many practical many-objective optimization problems, the computational cost of real evaluation is often high. To solve such problems, surrogate-assisted evolutionary algorithms (SAEAs) are effective methods that use low-cost surrogate models to approximate expensive real evaluation and select better-performing individuals for further evaluation. However, existing surrogate models accumulate prediction errors with increasing numbers of objectives, and the computational cost of constructing surrogate models also increases sharply. This paper proposes a composite surrogate-assisted evolutionary algorithm for expensive many-objective optimization. The crux of this method involves categorizing the surrogate models into two distinct types: a global model, constructed based on all costly real evaluations, and local models tailored to subsets derived from clustering all evaluations. Additionally, a new filling sampling criteria is proposed to guide the selection of solutions, considering the requirements of convergence, diversity, and uncertainty. Experimental results on multiple benchmark problems demonstrate that the proposed method is competitive compared to other popular surrogate-assisted evolutionary algorithms in solving expensive many-objective optimization problems within limited computational budgets.

关键词： expensive many-objective optimization Filling sampling criteria Evolutionary algorithms Surrogate assisted

来源：评论

学校读者我要写书评

暂无评论

A surrogate-assisted evolutionary algorithm for expensive many-objective optimization in the refining process

引用

SWARM AND EVOLUTIONARY COMPUTATION 2022年 69卷 100988-100988页

作者： Han, Dong Du, Wenli Wang, Xinjie Du, Wei East China Univ Sci & Technol Key Lab Smart Mfg Energy Chem Proc Minist Educ Shanghai 200237 Peoples R China

Computationally expensive optimization problems are difficult for an evolutionary algorithm within limited fitness evaluations, especially for many-objective optimization. To remedy this issue, a surrogate-assisted decomposition-based evolutionary algorithm is proposed in this work where the Kriging model is used to approximate each objective function. In order to balance exploration and exploitation, the model management strategy integrates the infill criterion with the distribution information of both weight vectors and population. Additionally, a one-by-one replacement strategy is developed to select a fixed number of training data to limit computational time for the model training as well as maintain the approximation accuracy. The empirical results show that the performance of the proposed algorithm is competitive to the state-of-the-art algorithms on a number of benchmarks. Finally, the proposed algorithm is applied for a real-world optimization problem in the refining process and exhibits superior performance.

关键词： Surrogate-assisted evolutionary optimization expensive many-objective optimization Model management Decomposition Engineering 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：