检索结果-内蒙古大学图书馆

A novel approach of many-objective particle swarm optimization with cooperative agents based on an inverted generational distance indicator

引用

INFORMATION SCIENCES 2023年第1期623卷 220-241页

作者： Kouka, Najwa BenSaid, Fatma Fdhila, Raja Fourati, Rahma Hussain, Amir Alimi, Adel M. Univ Sfax Natl Engn Sch Sfax ENIS Res Grp Intelligent Machines BP 1173 Sfax 3038 Tunisia Edinburgh Napier Univ Sch Comp Edinburgh Scotland Univ Johannesburg Fac Engn & Built Environm Dept Elect & Elect Engn Sci Johannesburg South Africa

Most evolutionary algorithms, including particle swarm optimization (PSO), use Pareto dominance as a major selection criterion and face significant challenges when dealing with many-objective problems. To tackle this issue, this paper proposes a novel algorithm, ter-med: many-objective PSO with Cooperative Agents (MaOPSO-CA). This exploits an Inverted Generational Distance (IGD) indicator in two innovative ways: firstly, as a leader selection method to select the preferable solution in terms of convergence and diversity, and, secondly, as an archiving method to decide which non-dominated solutions are kept in a bounded archive. The proposed strategy significantly promotes selection pressure toward the Pareto front. The results indicate that the IGD-based selection circumvents the issue of a large ratio of non-dominated solutions that exist in MaOPs. Moreover, a multi-swarm is investigated and modeled as a Multi-Agent System (MAS), so that knowl-edge sharing among different sub-swarms is easily improved through automated negotia-tion. The effectiveness of our proposed algorithm is validated with numerous experimental studies in solving 110 benchmark testing instances with up to twenty objectives. Experimental results demonstrate the effectiveness of the new algorithm compared to recent state-of-the-art methods. Finally, the application of MaOPSO-CA to a challenging, real-world water resource-management problem is shown to produce very encouraging results, demonstrating its potential as a benchmark resource for the research community.(c) 2022 Elsevier Inc. All rights reserved.

关键词： many-objective optimization problems Particle swarm optimization Multi-agent system Automated negotiation

来源：评论

学校读者我要写书评

暂无评论

Binary search based boundary elimination selection in many-objective evolutionary optimization

引用

APPLIED SOFT COMPUTING 2017年 60卷 689-705页

作者： Yu, Guo Shen, Ruimin Zheng, Jinhua Li, Miqing Zou, Juan Liu, Yuan Xiangtan Univ Sch Informat Engn Xiangtan Hunan Peoples R China Hengyang Normal Univ Sch Comp Sci & Technol Hengyang Hunan Peoples R China Univ Surrey Dept Comp Sci Guildford GU2 7XH Surrey England Univ Birmingham Sch Comp Sci Birmingham B15 2TT W Midlands England

In many-objective optimization, the balance between convergence and diversity is hard to maintain, while the dominance resistant solutions (DRSs) could further harm the balance particularly in high-dimensional objective space. Thus, this paper proposes a novel selection strategy - boundary elimination selection based on binary search (called BESBS), trying to avoid the impact of DRSs during the optimization and achieve a good balance between the convergence and diversity simultaneously. During the environmental selection, the binary search (BS) is used to adaptively adjust the epsilon value in the epsilon-dominance relationship and assist in detecting the well-distributed neighbors for the elite solutions. Then the epsilon value obtained by BS is used for serving the boundary elimination selection (BES) to guarantee the stability of the elite population. To improve the convergence, BES is mainly designed to select individuals approximating to the ideal point. By modifying the fitness of solutions and choosing solutions in terms of the shuffled sequence of objective axis, the DRSs will be eliminated during the selection. Thus, BESBS could achieve a good balance between the convergence and diversity and avoid the impact from DRSs simultaneously. From a series of experiments with 35 instances, the experimental results have shown that BESBS is competitive against 8 state-of-art many-objective evolutionary algorithms. (C) 2017 Elsevier B.V. All rights reserved.

关键词： many-objective optimization problems Boundary elimination selection Binary search

来源：评论

学校读者我要写书评

暂无评论

A two-stage many-objective evolutionary algorithm with dynamic generalized Pareto dominance

引用

INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS 2022年第11期37卷 9833-9862页

作者： Wang, Hui Wei, Zichen Yu, Gan Wang, Shuai Wu, Jiali Liu, Jiawen Nanchang Inst Technol Sch Informat Engn Nanchang Jiangxi Peoples R China Fuyang Normal Univ Sch Informat Engn Fuyang Peoples R China

many-objective evolutionary algorithms (MaOEAs) are widely used to solve many-objective optimization problems. As the number of objectives increases, it is difficult to achieve a balance between the population diversity and the convergence. Additionally, the selection pressure decreases rapidly. To tackle these issues, this paper proposes a two-stage many-objective evolutionary algorithm with dynamic generalized Pareto dominance (called TS-DGPD). First, a two-stage method is utilized for environmental selection. The first stage employs the cosine distance to accelerate the convergence. The second stage uses L p ${L}_{p}$-norm maintain the population diversity. Moreover, a dynamic generalized Pareto dominance (DGPD) is used to increase the selection pressure of the population. To evaluate the performance of TS-DGPD, we compare it with several other MaOEAs on two benchmark sets with 3, 5, 8, 10, 15, and 20 objectives. Experimental results show that TS-DGPO performs satisfactorily on convergence and diversity.

关键词： generalized Pareto dominance many-objective evolutionary algorithms many-objective optimization problems multiobjective optimization two-stage selection

来源：评论

学校读者我要写书评

暂无评论

Hybrid many-objective particle swarm optimization algorithm for green coal production problem

引用

INFORMATION SCIENCES 2020年第0期518卷 256-271页

作者： Cui, Zhihua Zhang, Jiangjiang Wu, Di Cai, Xingjuan Wang, Hui Zhang, Wensheng Chen, Jinjun Taiyuan Univ Sci & Technol Complex Syst & Computat Intelligence Lab Taiyuan 030024 Peoples R China Nanchang Inst Technol Sch Informat Engn Nanchang 330099 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Intelligent Control & Management Co Beijing 100190 Peoples R China Swinburne Univ Technol Dept Comp Sci & Software Engn Melbourne Vic 3000 Australia

The key aspect in coal production is realizing safe and efficient mining to maximize the utilization of the resources. A requirement for sustainable economic development is realizing green coal production, which is influenced by factors of coal economic, energy, ecological, coal gangue economic and social benefits. To balance these factors, this paper proposes a many-objective optimization model with five objectives for green coal production. Furthermore, a hybrid many-objective particle swarm optimization (HMaPSO) algorithm is designed to solve the established model. A new offspring of the alternative pool is generated by employing different evolutionary operators. The environmental selection mechanism is adopted to select and store the excellent solutions. Two sets of experiments are performed to verify the effectiveness of the proposed approach: First, the HMaPSO algorithm is tested on the DTLZ functions, and its performance is compared with that of several widely used many-objective algorithms. Second, the HMaPSO algorithm is applied to solve the many-objective green coal production optimization model. The computational results demonstrate the effectiveness of the proposed approach, and the simulation results prove that the designed approach can provide promising choices for decision makers in regional planning. (C) 2020 Elsevier Inc. All rights reserved.

关键词： Coal production many-objective optimization problems Evolutionary operators Particle swarm optimization (PSO)

来源：评论

学校读者我要写书评

暂无评论

A multistage evolutionary algorithm for many-objective optimization

引用

INFORMATION SCIENCES 2022年 589卷 531-549页

作者： Shen, Jiangtao Wang, Peng Dong, Huachao Li, Jinglu Wang, Wenxin Northwestern Polytech Univ Sch Marine Sci & Technol Xian 710072 Peoples R China

Convergence and diversity are of high significance to many-objective optimization, which are considered by most state-of-the-art many-objective evolutionary algorithms (MaOEAs) simultaneously. However, it is not easy to balance them during the optimization process due to their conflicting nature. This study proposes a multistage MaOEA to address this issue, where convergence and diversity are processed respectively at different optimization stages. At the first stage, the population approaches Pareto front rapidly and the diversity is ignored. After the population is converged, the diversity will be emphasized by applying the decision variable clustering method at the second stage. When the population achieves high convergence and diversity, the algorithm will enter the last stage, where the quality of the solution set is fine-tuned by substituting those solutions with worse convergence and diversity degrees. As demonstrated by the experimental results with peer competitors on common benchmark problems, that the proposed algorithm is promising. (C) 2022 Elsevier Inc. All rights reserved.

关键词： many-objective evolutionary algorithms many-objective optimization problems Multistage optimization Convergence and diversity

来源：评论

学校读者我要写书评

暂无评论

Non-dominated sorting on performance indicators for evolutionary many-objective optimization

引用

INFORMATION SCIENCES 2021年 551卷 23-38页

作者： Wang, Hao Sun, Chaoli Zhang, Guochen Fieldsend, Jonathan E. Jin, Yaochu Taiyuan Univ Sci & Technol Sch Elect Informat Engn Taiyuan 030024 Peoples R China Taiyuan Univ Sci & Technol Dept Comp Sci & Technol Taiyuan 030024 Peoples R China Univ Exeter Dept Comp Sci Exeter EX4 4QF Devon England Univ Surrey Dept Comp Sci Guildford GU2 7XH Surrey England

Much attention has been paid to evolutionary multi-objective optimization approaches to efficiently solve real-world engineering problems with multiple conflicting objectives. However, the loss of selection pressure and the non-uniformity in the distribution of the Pareto optimal solutions in the objective space can impede both dominance-based and decomposition-based multi-objective optimizers when solving many-objective problems. In this work, we circumvent this issue by exploiting two performance indicators, and use these in an optimizer's environmental selection via non-dominated sorting. This effectively converts the original many-objective problem into a bi-objective one. Our convergence performance criterion tries to balance the performance of individuals in different parts of the objective space. The angle between solutions on objective space is adopted to measure the diversity of each individual. Using these solutions can be separated into different layers easily, which is often not possible for the original many-objective optimization representation. The performance of the proposed method is evaluated on the DTLZ benchmark problems with up to 30 objectives, and MaF test suite with 10,15, 20 and 30 objectives. The experimental results show that our proposed method is competitive compared to six recently proposed algorithms, especially for solving problems with a large number of objectives. (C) 2020 Elsevier Inc. All rights reserved.

关键词： many-objective optimization problems Performance indicator Non-dominated sorting Environmental selection

来源：评论

学校读者我要写书评

暂无评论

A many-objective population extremal optimization algorithm with an adaptive hybrid mutation operation

引用

INFORMATION SCIENCES 2019年 498卷 62-90页

作者： Chen, Min-Rong Zeng, Guo-Qiang Lu, Kang-Di South China Normal Univ Sch Comp Sci Guangzhou 510631 Guangdong Peoples R China Wenzhou Univ Natl Local Joint Engn Lab Digitalize Elect Design Wenzhou 325035 Peoples R China Donghua Univ Dept Automat Coll Informat Sci & Technol Shanghai 201620 Peoples R China

many-objective optimization problems abbreviated as MaOPs with more than three objectives have attracted increasing interests due to their widely existing in a variety of real world applications. This paper presents a novel many-objective population extremal optimization called MaOPEO-HM algorithm for MaOPs by introducing a reference set based many-objective optimization mechanism into a recently developed population extremal optimization framework and designing an adaptive hybrid mutation operation for updating the population. Despite of the successful applications of extremal optimization in different kinds of numerical and engineering optimization problems, it has never been explored to the many-objective optimization domain so far. Because most of the existing many-objective evolutionary algorithms are usually guided by a single mutation operation, which has insufficient ability to exploit the search space of MaOPs and will get stuck at any local efficient front, it is the first attempt to design a novel hybrid mutation scheme in MaOPEO-HM algorithm by combining the advantages of polynomial mutation operator and multi-non-uniform mutation operator effectively. The experiment results for DTLZ test problems with 3, 5, 8, 10, and 15 objectives and WFG test problems with 3, 5, and 8 objectives have demonstrated the superiority of the proposed MaOPEO-HM to five state-of-the-art decomposition-based many-objective evolutionary algorithms including NSGA-III, RVEA, EFR-RR, theta-DEA, and MOEA/DD and two non-decomposition-based algorithms in-cluding GrEA and Two_Arch2. Furthermore, the great ability of the designed adaptive hybrid mutation operation incorporated into many-objective population extremal optimization (MaOPEO) has also been illustrated by comparing MaOPEO-HM and two MaOPEO algorithms only based on traditional multi-non-uniform mutation or polynomial mutation for DTLZ problems. (C) 2019 Elsevier Inc. All rights reserved.

关键词： many-objective optimization problems many-objective evolutionary algorithms many-objective population extremal optimization Adaptive hybrid mutation operation

来源：评论

学校读者我要写书评

暂无评论

many-objective Evolutionary Algorithm with Adaptive Reference Vector

引用

INFORMATION SCIENCES 2021年 563卷 70-90页

作者： Zhang, Maoqing Wang, Lei Li, Wuzhao Hu, Bo Li, Dongyang Wu, Qidi Tongji Univ Sch Elect & Informat Engn Shanghai 201804 Peoples R China Jiaxing Vocat Technol Coll Sch Intelligent Mfg Jiaxing 314036 Zhejiang Peoples R China

Convergence is always a major concern for many-objective optimization problems. Over the past few decades, various methods have been designed for measuring the convergence. However, according to our mathematical and empirical analyses, most of these methods are more focused on the convergence, and may neglect the exploration of boundary solutions, resulting in the incomplete Pareto fronts and the poor extent of spread achieved among the obtained non-dominated solutions. Regarding this issue, this paper proposes a many-objective Evolutionary Algorithm with Adaptive Reference Vector (MaOEA-ARV). In MaOEA-ARV, an adaptive reference vector strategy is designed to dynamically adjust the reference vectors according to the current distribution of candidate solutions for ensuring the spread and convergence simultaneously. Additionally, a hierarchical clustering strategy is employed to adaptively partition candidate solutions into multiple clusters for the diversity of candidate solutions. Experimental results on DTLZ, BT, ZDT and WFG test suites with up to 12 objectives demonstrate the effectiveness of MaOEA-ARV. (c) 2021 Elsevier Inc. All rights reserved.

关键词： many-objective optimization problems Convergence Spread Adaptive reference vector strategy Hierarchical clustering strategy

来源：评论

学校读者我要写书评

暂无评论

A decomposition-based many-objective evolutionary algorithm with optional performance indicators

引用

COMPLEX & INTELLIGENT SYSTEMS 2022年第6期8卷 5157-5176页

作者： Wang, Hao Sun, Chaoli Yu, Haibo Li, Xiaobo Taiyuan Univ Sci & Technol Dept Elect & Comp Engn Taiyuan 030024 Peoples R China Taiyuan Univ Sci & Technol Sch Comp Sci & Technol Taiyuan 030024 Peoples R China North Univ China Inst Big Data & Visual Comp Taiyuan 030051 Peoples R China

Evolutionary algorithms (EAs) have shown excellent performance for solving optimization problems with multiple objectives as they can get a set of compromising solutions on a single run. However, when the number of objectives increases, an efficient selection is significant to find a good set of solutions. In this paper, a decomposition-based many-objective evolutionary algorithm with optional performance indicators is proposed, in which the decomposition strategy is utilized to convert a many-objective optimization problem into a set of single-objective optimization problems, and the criterion to select a solution for the next generation along each reference is randomly set to convergence or diversity performance. The performance of the proposed method is evaluated on two sets of benchmark problems, and the experimental results showed the efficiency of the proposed method compared with seven state-of-the-art MaOEAs.

关键词： Decomposition-based evolutionary optimization Performance indicators many-objective optimization problems

来源：评论

学校读者我要写书评

暂无评论

An integrated algorithm for multi-agent fault-tolerant scheduling based on MOEA

引用

FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2019年 94卷 51-61页

作者： Wu, Binghong Hao, Kuangrong Cai, Xin Wang, Tong Donghua Univ Minist Educ Engn Res Ctr Digitized Text & Apparel Technol Shanghai Peoples R China Donghua Univ Coll Informat Sci & Technol Shanghai PR Peoples R China

With the increasing functionality of multi-agent systems, fault tolerance has become an essential requirement, especially for workflow with interdependencies among tasks. In this paper, we propose a scheme that combines a novel intrinsic-plasticity-inspired rescheduling execution model (IPIREM) with an improved NSGA-III with knee points. The IPIREM provides a relatively stable evaluation without any estimation of the failure distribution when transient failures happen. Based on the evaluation result of IPIREM, the multi-objective optimization problem is solved by use of an improved NSGA-III, which accelerates convergence and promotes diversity. Finally, experimental results show that the proposed algorithm is effective in enhancing efficiency of the workflow. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Fault-tolerant Intrinsic-plasticity-inspired rescheduling execution model many-objective optimization problems NSGA-III Knee point Multi-agent systems

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：