检索结果-内蒙古大学图书馆

Multi-objective particle swarm optimization with R2 indicator and adaptive method

COMPLEX & INTELLIGENT SYSTEMS 2021年第5期7卷 2697-2710页

作者： Gu, Qinghua Jiang, Mengke Jiang, Song Chen, Lu Xian Univ Architecture & Technol Sch Management Xian 710055 Shaanxi Peoples R China Xian Univ Architecture & Technol Xian Key Lab Intelligent Ind Percept Calculat & D Xian 710055 Shaanxi Peoples R China Xian Univ Architecture & Technol Sch Resources Engn Xian 710055 Shaanxi Peoples R China

Multi-objective particle swarm optimization algorithms encounter significant challenges when tackling many-objective optimization problems. This is mainly because of the imbalance between convergence and diversity that occurs when increasing the selection pressure. In this paper, a novel adaptive MOPSO (ANMPSO) algorithm based on R2 contribution and adaptive method is developed to improve the performance of MOPSO. First, a new global best solutions selection mechanism with R2 contribution is introduced to select leaders with better diversity and convergence. Second, to obtain a uniform distribution of particles, an adaptive method is used to guide the flight of particles. Third, a re-initialization strategy is proposed to prevent particles from trapping into local optima. Empirical studies on a large number (64 in total) of problem instances have demonstrated that ANMPSO performs well in terms of inverted generational distance and hyper-volume metrics. Experimental studies on the practical application have also revealed that ANMPSO could effectively solve problems in the real world.

关键词： many-objective optimization problems Multi-objective particle swarm optimization R2 contribution Adaptive strategy

来源：评论

学校读者我要写书评

暂无评论

A many-objective Test Problem for Visually Examining Diversity Maintenance Behavior in a Decision Space 11

A Many-Objective Test Problem for Visually Examining Diversi...

引用

13th Annual Genetic and Evolutionary Computation Conference (GECCO)

作者： Ishibuchi, Hisao Akedo, Naoya Nojima, Yusuke Osaka Prefecture Univ Grad Sch Engn Sakai Osaka 5998531 Japan

ISBN: (纸本)9781450305570

Recently distance minimization problems in a two-dimensional decision space have been utilized as many-objective test problems to visually examine the behavior of evolutionary multi-objective optimization (EMO) algorithms. Such a test problem is usually defined by a single polygon where the distance from a solution to each vertex is minimized in the decision space. We can easily generate different test problems from different polygons. We can also easily generate test problems with multiple equivalent Pareto optimal regions using multiple polygons of the same shape and the same size. Whereas these test problems have a number of advantages, they have no clear relevance to real-world situations since they are artificially generated unrealistic test problems. In this paper, we generate a distance minimization problem from a real-world map. Our test problem has four objectives, which are to minimize the distances to the nearest elementary school, junior high school, railway station, and convenience store. Using our test problem, we examine the behavior of well-known and frequently-used EMO algorithms in terms of their diversity maintenance ability in the two-dimensional decision space.

关键词： Evolutionary multiobjective optimization (EMO) many-objective optimization problems decision space diversity

来源：评论

学校读者我要写书评

暂无评论

An Analysis of Parameters of Decomposition-based MOEAs on many-objective optimization

An Analysis of Parameters of Decomposition-based MOEAs on Ma...

引用

IEEE Congress on Evolutionary Computation (IEEE CEC) as part of the IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： Montero, Elizabeth Zapotecas-Martinez, Saul Univ Tecn Federico Santa Maria Dept Comp Sci Valparaiso Chile Univ Autonoma Metropolitana Dept Appl Math & Syst Mexico City DF Mexico

ISBN: (纸本)9781509060177

This paper presents an analysis of parameters defined within some of the most representative decomposition-based multi-objective evolutionary algorithms (MOEAs) namely: MOEA/D, MOEA/D-DE, and MOEA/D-DRA. Our main interest is focused in the many-objective context, where decompositionbased MOEAs have been successfully applied, but a lack of analysis on the relevance of their parameters is evidently observable. We review the literature related to those parameter values that have been commonly adopted and we perform some experiments oriented to validate these decisions. Our results show that some alternative parameter configurations can allow these methods to obtain better solutions than the standard values. Moreover, some of our recommendations can conduct to inspect in detail the design of these algorithms.

关键词： decomposition-based MOEAs many-objective optimization problems parameter setting problem tuning methods

来源：评论

学校读者我要写书评

暂无评论

A many-objective Particle Swarm optimization Based On Virtual Pareto Front

A Many-Objective Particle Swarm Optimization Based On Virtua...

引用

IEEE Congress on Evolutionary Computation (IEEE CEC) as part of the IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： Wu, Bolin Hu, Wang He, Zhenan Jiang, Min Yen, Gary G. Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu Sichuan Peoples R China Sichuan Univ Coll Comp Sci Chengdu Sichuan Peoples R China Xiamen Univ Dept Cognit Sci & Technol Xiamen Fujian Peoples R China Oklahoma State Univ Sch Elect & Comp Engn Stillwater OK 74078 USA

ISBN: (纸本)9781509060177

A many-objective problems (MaOP) refer to the optimization problem involving more than three objectives. Particle swarm optimization (PSO) is one of the potential heuristic methods suited for solving MaOPs. The personal best selection strategy, the global best selection strategy, and the archive maintenance strategy are the three key components in the design of a many-objective Particle Swarm optimization (MaOPSO). The personal best and global best selection strategies determine the direction where particles will fly. The archive maintenance strategy has an important impact on convergence and diversity of its algorithm. In MaOPs, the high dimensionality in the objective space decreases the probability of a solution to be dominated by the other solutions in the population. Thus, it becomes more difficult for PSO to select the good leaders from so many non-dominated solutions. In this paper, a virtual Inverted Generational Distance indicator is proposed to evaluate the comprehensive quality of a solution in the external archive according to a constructed virtual Pareto front (vPF). Accordingly, a new indicator-based MaOPSO using vPF (MaOPSO/vPF) is developed to improve the convergence and diversity of the approximate Pareto front. Experimental results on the MaF test suites demonstrate that the proposed MaOPSO/vPF performs better than some selected competing Multi-objective optimization Evolutionary Algorithms.

关键词： virtual Pareto front inverted generational distance particle swarm optimization many-objective optimization problems

来源：评论

学校读者我要写书评

暂无评论

Pareto Dominance-based MOEA with Multiple Ranking methods for many-objective optimization 8

Pareto Dominance-based MOEA with Multiple Ranking methods fo...

引用

8th IEEE Symposium Series on Computational Intelligence (IEEE SSCI)

作者： Palakonda, Vikas Ghorbanpour, Samira Mallipeddi, Rammohan Kyungpook Natl Univ Sch Elect Engn Daegu 702701 South Korea

ISBN: (纸本)9781538692769

Pareto Dominance-based Multi-objective Evolutionary algorithms (PDMOEAs) have issues while handling many-objective optimization problems (MaOPs) due to the lack of selection pressure provided by the Pareto dominance to guide the search process towards the convergence. Hence, most of the PDMOEAs proposed rely on additional selection criterion to establish preferences between the solutions. In this paper, we propose a PDMOEA with multiple ranking methods (PDMOEA-MR), an extension to the proposed PDMOEA with ranking methods for MaOPs which assigns priority rank based upon Ranking methods and niche radius. In PDMOEA with ranking methods for MaOPs, ranking methods such as Average rank (AR) in PDMOEA-AR, and weighted sum of objectives (WS) in PDMOEA-WS, are used. Instead of using two ranking methods separately, in the proposed PDMOEA_MR, both the ranking methods AR and WS are incorporated into a common framework and a different strategy is adopted to assign priority rank. The performance of proposed method is analyzed on 16 test problems.

关键词： Evolutionary Algorithms many-objective optimization problems Convergence Diversity Pareto dominance Ranking methods

来源：评论

学校读者我要写书评

暂无评论

A many-objective evolutionary algorithm based on decomposition with dynamic resource allocation for irregular optimization

引用

Frontiers of Information Technology & Electronic Engineering 2020年第8期21卷 1171-1190页

作者： Ming-gang DONG Bao LIU Chao JING College of Information Science and Engineering Guilin University of TechnologyGuilin 541004China Guangxi Key Laboratory of Embedded Technology and Intelligent System Guilin 541004China Guangxi Key Laboratory of Trusted Software Guilin University of Electronic&TechnologyGuilin 541004China

The multi-objective optimization problem has been encountered in numerous fields such as high-speed train head shape design,overlapping community detection,power dispatch,and unmanned aerial vehicle *** address such issues,current approaches focus mainly on problems with regular Pareto front rather than solving the irregular Pareto *** this situation,we propose a many-objective evolutionary algorithm based on decomposition with dynamic resource allocation(Ma OEA/D-DRA)for irregular *** proposed algorithm can dynamically allocate computing resources to different search areas according to different shapes of the problem’s Pareto *** evolutionary population and an external archive are used in the search process,and information extracted from the external archive is used to guide the evolutionary population to different search *** evolutionary population evolves with the Tchebycheff approach to decompose a problem into several subproblems,and all the subproblems are optimized in a collaborative *** external archive is updated with the method of rithms using a variety of test problems with irregular Pareto *** results show that the proposed algorithèm out-p￡performs these five algorithms with respect to convergence speed and diversity of population *** comparison with the weighted-sum approach and penalty-based boundary intersection approach,there is an improvement in performance after integration of the Tchebycheff approach into the proposed algorithm.

关键词： many-objective optimization problems Irregular Pareto front External archive Dynamic resource allocation Shift-based density estimation Tchebycheff approach

来源：评论

学校读者我要写书评

暂无评论

many-objective evolutionary algorithm based on parallel distance for handling irregular Pareto fronts

引用

SWARM AND EVOLUTIONARY COMPUTATION 2024年 86卷

作者： Wei, Zichen Wang, Hui Wang, Shuai Zhang, Zhixia Cui, Zhihua Wang, Feng Peng, Hu Zhao, Jia Nanchang Inst Technol Sch Informat Engn Nanchang Peoples R China Taiyuan Univ Sci & Technol Sch Comp Sci & Technol Taiyuan Peoples R China Wuhan Univ Sch Comp Sci Wuhan 430072 Peoples R China Jiujiang Univ Sch Comp & Big Data Sci Jiujiang 332005 Peoples R China

In recent years, various many -objective evolutionary algorithms (MaOEAs) have been proved to be successful in solving many -objective optimization problems (MaOPs). However, the performance of most MaOEAs is seriously affected when handling MaOPs with irregular Pareto fronts (PFs). In this paper, a new MaOEA variant based on parallel distance (called PDMaOEA) is proposed to solve MaOPs with irregular PFs. Firstly, two new metrics based on parallel distance are designed. The first one termed diversity metric can adapt to irregular PFs. The second one called comprehensive selection metric can consider both diversity and convergence simultaneously. Based on the two metrics, a mating selection method and an environmental selection strategy are proposed. In the mating selection, solutions with good convergence or diversity are chosen to improve the quality of offspring population. In the environmental selection, the selection pressure is significantly enhanced by the two metrics. Experimental study is validated on 19 irregular problems with different shapes of PFs. Performance of the proposed PDMaOEA is compared with six state-of-the-art algorithms. Statistical analysis shows that the proposed approach is competitive in handling MaOPs with irregular PFs.

关键词： many-objective evolutionary algorithms (MaOEAs) many-objective optimization problems (MaOPs) optimization Irregular Pareto front Parallel distance

来源：评论

学校读者我要写书评

暂无评论

many-objective evolutionary algorithm based on three-way decision

引用

EGYPTIAN INFORMATICS JOURNAL 2023年第3期24卷

作者： Cui, Zhihua Li, Bingting Lan, Zhuoxuan Xu, Yubin Taiyuan Univ Sci & Technol Shanxi Key Lab Big Data Anal & Parallel Comp Taiyuan Peoples R China

In recent years, many-objective optimization problems have been widely used. however, with the increase of the number of objectives, the difficulty of solving increases exponentially, and the imbalance between convergence and diversity becomes more serious. In view of the above problems, this paper combines the idea of three-way decision, redesigns the environment selection strategy, and proposes a many-objective optimization algorithm based on three-way decision. Firstly, the distance from the individual to the ideal point is used as an index to measure individual convergence, the minimum distance from the individual to other solutions is used as an indicator to measure individual diversity, and the individuals with good convergence and good diversity are selected separately by combining the thresholds of the three-way decision;and secondly, A dynamic threshold acquisition method is designed to further improve the performance of the algorithm;Finally, it is proved that the algorithm can effectively balance convergence and diversity through tests of different data sets, so as to verify the feasibility and effectiveness of the algorithm & COPY;2023 Production and hosting by Elsevier B.V. on behalf of Faculty of Computers and Information, Cairo University. This is an open access article under the CC BY-NC-ND license (http://***/ licenses/by-nc-nd/4.0/).

关键词： many-objective optimization problems many-objective evolutionary algorithm Three-way decision Convergence Diversity

来源：评论

学校读者我要写书评

暂无评论

Reference-point-based Brain Storm optimization Algorithm for many-objective optimization

Reference-point-based Brain Storm Optimization Algorithm for...

引用

第32届中国过程控制会议(CPCC2021)

作者： Yali Wu Xiaoxiao Quan Jinjin Qi Yulong Wang School of Automation and Information Engineering Xi'an University of Technology Shaanxi Province Key Laboratory of Complex System Control and Intelligent Information Processing

Brain storm optimization algorithm,as a new type of swarm intelligence optimization algorithm,has attracted the attention of many researchers as soon as it was put ***,it has been applied to various many-objective,multi-modal and practical engineering *** this paper,a brain storm optimization algorithm based on reference points was proposed to solve many-objective optimization ***,a series of reference points are introduced into the target space,and the population is clustered in an adaptive clustering method centered on the reference point poles to guide the evolution of the ***,the idea based on decomposition is combined with the mechanism of brain storm optimization algorithm to solve many-objective optimization ***,a mechanism to update the external archive set based on the strategy of enhanced dominance and weighted boundary intersection is proposed to maintain the convergence and diversity of the *** simulation results on the DTLZ series of test function sets show that the proposed algorithm based on reference points is effective.

关键词： Brain Storm optimization Algorithm Reference Point many-objective optimization problems adaptive cluster

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：