检索结果-内蒙古大学图书馆

multimodal multiobjective optimization algorithm with fine-grained special crowding distance and dual-space selection mechanism

引用

JOURNAL OF SUPERCOMPUTING 2025年第6期81卷 1-45页

作者： Li, Wei Ping, Zeming Wang, Lei Xian Univ Technol Sch Comp Sci & Engn Xian 710048 Peoples R China Shaanxi Key Lab Network Comp & Secur Technol Xian 710048 Peoples R China

Many multimodal multiobjective optimization algorithms strive to solve multimodal multiobjective optimization problems (MMOPs), which have multiple equivalent Pareto optimal solution sets (PSs) in the decision space, and these PSs correspond to the same Pareto front in the objective space. Although these algorithms have the advantage of enhanced search capabilities, there are still various challenges in addressing MMOPs, such as incomplete Pareto optimal sets and unevenly distributed Pareto optimal sets. To address these problems, this paper presents a multimodal multiobjective optimization algorithm with a fine-grained special crowding distance and a dual-space selection mechanism. In the proposed algorithm, a fine-grained special crowding distance (FSCD) is used to measure the comprehensive crowding distances of individuals in the decision and objective spaces. Next, an FSCD-based reproduction strategy is employed to select an exemplar and generate high-quality offspring. Finally, environmental selection based on dual spaces is proposed to improve the convergence of the population without decreasing its diversity. To verify the effectiveness of the proposed algorithm, a series of experiments are carried out on CEC2019 benchmark problems and a path planning optimization problem. The experimental results indicate that the proposed algorithm outperforms its peer competitors.

关键词： multimodal multiobjective optimization Fine-grained special crowding distance Subset selection Dual-space

来源：评论

学校读者我要写书评

暂无评论

multimodal multiobjective optimization in Feature Selection

Multimodal Multiobjective Optimization in Feature Selection

引用

IEEE Congress on Evolutionary Computation (IEEE CEC)

作者： Yue, C. T. Liang, J. J. Qu, B. Y. Yu, K. J. Song, H. Zhengzhou Univ Sch Elect Engn Zhengzhou Henan Peoples R China Zhongyuan Univ Technol Sch Elect & Informat Engn Zhengzhou Henan Peoples R China Univ Melbourne Royal Melbourne Inst Technol Sch Sci Melbourne Vic Australia

ISBN: (纸本)9781728121536

In feature selection, the number of selected features and the classification accuracy are two common objectives to be optimized. However, few studies pay attention to which features are selected. In many feature selection problems, different feature subsets with the same number of selected features can achieve similar classification accuracy. These are multimodal multiobjective optimization (MMO) problems in feature selection. In this paper, the MMO problems in feature selection are described in detail. Then, the great significance and importance to find these different feature subsets are discussed. Two modified MMO algorithms are used to solve the MMO feature selection problems. Simulation results show that these MMO algorithms can find more feature subsets than unimodal optimization algorithms.

关键词： multimodal multiobjective optimization feature selection particle swarm optimization

来源：评论

学校读者我要写书评

暂无评论

A Multipopulation Evolutionary Algorithm for Solving Large-Scale multimodal multiobjective optimization Problems

引用

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 2021年第3期25卷 405-418页

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

multimodal multiobjective optimization problems (MMOPs) widely exist in real-world applications, which have multiple equivalent Pareto-optimal solutions that are similar in the objective space but totally different in the decision space. While some evolutionary algorithms (EAs) have been developed to find the equivalent Pareto-optimal solutions in recent years, they are ineffective to handle large-scale MMOPs having a large number of variables. This article thus proposes an EA for solving large-scale MMOPs with sparse Pareto-optimal solutions, i.e., most variables in the optimal solutions are 0. The proposed algorithm explores different regions of the decision space via multiple subpopulations and guides the search behavior of the subpopulations via adaptively updated guiding vectors. The guiding vector for each subpopulation not only provides efficient convergence in the huge search space but also differentiates its search direction from others to handle the multimodality. While most existing EAs solve MMOPs with 2-7 decision variables, the proposed algorithm is shown to be effective for benchmark MMOPs with up to 500 decision variables. Moreover, the proposed algorithm also produces a better result than state-of-the-art methods for the neural architecture search.

关键词： Computer architecture Pareto optimization Statistics Sociology Microprocessors optimization Extraterrestrial measurements Evolutionary algorithm (EA) large-scale optimization multimodal multiobjective optimization neural architecture search sparse Pareto-optimal solutions

来源：评论

学校读者我要写书评

暂无评论

A Framework to Handle multimodal multiobjective optimization in Decomposition-Based Evolutionary Algorithms

引用

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 2020年第4期24卷 720-734页

作者： Tanabe, Ryoji Ishibuchi, Hisao Southern Univ Sci & Technol Shenzhen Key Lab Computat Intelligence Univ Key Lab Evolving Intelligent Syst Guangdong Dept Comp Sci & Engn Shenzhen 518055 Peoples R China

multimodal multiobjective optimization is to locate (almost) equivalent Pareto optimal solutions as many as possible. While decomposition-based evolutionary algorithms have good performance for multiobjective optimization, they are likely to perform poorly for multimodal multiobjective optimization due to the lack of mechanisms to maintain the solution space diversity. To address this issue, this article proposes a framework to improve the performance of decomposition-based evolutionary algorithms for multimodal multiobjective optimization. Our framework is based on three operations: 1) assignment;2) deletion;and 3) addition operations. One or more individuals can be assigned to the same subproblem to handle multiple equivalent solutions. In each iteration, a child is assigned to a subproblem based on its objective vector, i.e., its location in the objective space. The child is compared with its neighbors in the solution space assigned to the same subproblem. The performance of improved versions of six decomposition-based evolutionary algorithms by our framework is evaluated on various test problems regarding the number of objectives, decision variables, and equivalent Pareto optimal solution sets. Results show that the improved versions perform clearly better than their original algorithms.

关键词： Pareto optimization Indexes Evolutionary computation Spatial diversity Decision making Benchmark testing Decomposition-based evolutionary algorithms multimodal multiobjective optimization reference vector-based evolutionary algorithms solution space diversity

来源：评论

学校读者我要写书评

暂无评论

Hierarchy Ranking Method for multimodal multiobjective optimization With Local Pareto Fronts

引用

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 2023年第1期27卷 98-110页

作者： Li, Wenhua Yao, Xingyi Zhang, Tao Wang, Rui Wang, Ling Natl Univ Def Technol Coll Syst Engn Changsha 410073 Peoples R China Natl Univ Def Technol Hunan Key Lab Multi Energy Syst Intelligent Interc Changsha 410073 Peoples R China Tsinghua Univ Dept Automat Beijing 100084 Peoples R China

multimodal multiobjective problems (MMOPs) commonly arise in real-world situations where distant solutions in decision space share a very similar objective value. Traditional multimodal multiobjective evolutionary algorithms (MMEAs) prefer to pursue multiple Pareto solutions that have the same objective values. However, a more practical situation in engineering problems is that one solution is slightly worse than another in terms of objective values, while the solutions are far away in the decision space. In other words, such problems have global and local Pareto fronts (PFs). In this study, we proposed several benchmark problems with several local PFs. Then, we proposed an evolutionary algorithm with a hierarchy ranking method (HREA) to find both the global and the local PFs based on the decision maker's preference. Regarding HREA, we proposed a local convergence quality evaluation method to better maintain diversity in the decision space. Moreover, a hierarchy ranking method was introduced to update the convergence archive. The experimental results show that HREA is competitive compared with other state-of-the-art MMEAs for solving the chosen benchmark problems.

关键词： Convergence optimization Benchmark testing Clustering algorithms Systems engineering and theory Statistics Space missions Diversity-preserving mechanisms evolutionary computation local Pareto front (PF) multimodal multiobjective optimization

来源：评论

学校读者我要写书评

暂无评论

A Diversity-Enhanced Subset Selection Framework for multimodal multiobjective optimization

引用

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 2022年第5期26卷 886-900页

作者： Peng, Yiming Ishibuchi, Hisao Southern Univ Sci & Technol Guangdong Prov Key Lab Brain Inspired Intelligent Dept Comp Sci & Engn Shenzhen 518055 Peoples R China

multimodality is commonly seen in real-world multiobjective optimization problems (MOPs). In such optimization problems, namely, multimodal MOPs (MMOPs), multiple decision vectors can be projected to the same solution in the objective space (i.e., there are multiple implementations corresponding to that solution). Therefore, the diversity in the decision space is very important for the decision maker when tackling MMOPs. Subset selection methods have been widely used in the field of evolutionary multiobjective optimization for selecting well-distributed solutions (in the objective space) to be presented to the decision maker. However, since most subset selection methods do not consider the diversity of solutions in the decision space, they are not suitable for MMOPs. In this article, we aim to clearly demonstrate the usefulness of subset selection for multimodal multiobjective optimization. We propose a novel subset selection framework that can be easily integrated into existing multimodal multiobjective optimization algorithms. By selecting a prespecified number of solutions with good diversity in both the objective and decision spaces from all the examined solutions, the proposed framework significantly improves the performance of state-of-the-art multimodal multiobjective optimization algorithms on various test problems.

关键词： optimization Evolutionary computation Search problems Standards Spatial diversity Maintenance engineering Linear programming Decision space diversity multimodal multiobjective optimization objective space diversity subset selection

来源：评论

学校读者我要写书评

暂无评论

Weighted Indicator-Based Evolutionary Algorithm for multimodal multiobjective optimization

引用

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 2021年第6期25卷 1064-1078页

作者： Li, Wenhua Zhang, Tao Wang, Rui Ishibuchi, Hisao Natl Univ Def Technol Coll Syst Engn Changsha 410073 Peoples R China Natl Univ Def Technol Key Lab Multi Energy Syst Intelligent Interconnec Coll Syst Engn Changsha 410073 Peoples R China Southern Univ Sci & Technol Dept Comp Sci & Engn Guangdong Prov Key Lab Brain Inspired Intelligent Shenzhen 518055 Peoples R China

multimodal multiobjective problems (MMOPs) arise frequently in the real world, in which multiple Pareto-optimal solution (PS) sets correspond to the same point on the Pareto front. Traditional multiobjective evolutionary algorithms (MOEAs) show poor performance in solving MMOPs due to a lack of diversity maintenance in the decision space. Thus, recently, many multimodal MOEAs (MMEAs) have been proposed. However, for most existing MMEAs, the convergence performance in the objective space does not meet expectations. In addition, many of them cannot always obtain all equivalent Pareto solution sets. To address these issues, this study proposes an MMEA based on a weighted indicator, termed MMEA-WI. The algorithm integrates the diversity information of solutions in the decision space into an objective space performance indicator to maintain the diversity in the decision space and introduces a convergence archive to ensure a more effective approximation of the Pareto-optimal front (PF). These strategies can readily be applied to other indicator-based MOEAs. The experimental results show that MMEA-WI outperforms some state-of-the-art MMEAs on the chosen benchmark problems in terms of the inverted generational distance (IGD) and IGD in the decision space (IGDX) metrics.

关键词： optimization Convergence Statistics Sociology Pareto optimization Task analysis Sensitivity Diversity-preserving mechanisms evolutionary computation indicator-based algorithms multimodal multiobjective optimization

来源：评论

学校读者我要写书评

暂无评论

A Bi-Objective Evolutionary Algorithm for multimodal multiobjective optimization

引用

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 2024年第1期28卷 168-177页

作者： Wei, Zhifang Gao, Weifeng Gong, Maoguo Yen, Gary G. Xidian Univ Sch Math & Stat Xian 710126 Peoples R China Xidian Univ Int Res Ctr Intelligent Percept & Computat Key Lab Intelligent Percept & Image Understanding Minist Educ Xian 710071 Peoples R China Oklahoma State Univ Sch Elect & Comp Engn Stillwater OK 74078 USA

multimodal multiobjective optimization problems (MMOPs) possess multiple Pareto optimal sets (PSs) corresponding to the identical Pareto optimal front (PF). To handle MMOPs, we propose a bi-objective evolutionary algorithm (BOEA), which transforms an MMOP into a bi-objective optimization problem. This problem is constructed by the penalty boundary intersection technique and a diversity indicator to obtain multiple PSs. The first objective reflects the population convergence and factors in the population diversity in the objective space, while the other objective concentrates more on the population diversity in the decision space. Furthermore, an environmental selection strategy is designed to choose the offspring solutions, which consists of nondominated sorting based on the transformed optimization problem and hierarchical clustering for selecting promising solutions. Experiments on 34 MMOPs demonstrate that BOEA performs better than selected state-of-the-art representatives, including 22 MMOPs from CEC2019 and 12 imbalanced MMOPs. In addition, the effectiveness of BOEA is further validated by six feature selection problems in real-world applications.

关键词： optimization Convergence Pareto optimization Statistics Sociology Sorting Evolutionary computation multimodal multiobjective optimization multiobjective optimization Pareto optimal front (PF) Pareto optimal set (PS)

来源：评论

学校读者我要写书评

暂无评论

A Review of Evolutionary multimodal multiobjective optimization

引用

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 2020年第1期24卷 193-200页

作者： Tanabe, Ryoji Ishibuchi, Hisao Southern Univ Sci & Technol Univ Key Lab Evolving Intelligent Syst Guangdong Dept Comp Sci & Engn Shenzhen Key Lab Computat Intelligence Shenzhen 518055 Peoples R China

multimodal multiobjective optimization aims to find all Pareto optimal solutions, including overlapping solutions in the objective space. multimodal multiobjective optimization has been investigated in the evolutionary computation community since 2005. However, it is difficult to survey existing studies in this field because they have been independently conducted and do not explicitly use the term "multimodal multiobjective optimization." To address this issue, this letter reviews the existing studies of evolutionary multimodal multiobjective optimization, including studies published under names that are different from multimodal multiobjective optimization. Our review also clarifies open issues in this research area.

关键词： Evolutionary algorithms multimodal multiobjective optimization performance indicators test problems

来源：评论

学校读者我要写书评

暂无评论

Modified Brain Storm optimization Algorithm for Solving multimodal multiobjective optimization Problems 14th

Modified Brain Storm Optimization Algorithm for Solving Mult...

引用

14th International Conference on Swarm Intelligence (ICSI)

作者： Liu, Yue Cheng, Shi Wang, Xueping Shan, Yuyuan Lu, Hui Shaanxi Normal Univ Sch Comp Sci Xian 710119 Shaanxi Peoples R China Beihang Univ Sch Elect & Informat Engn Beijing 100191 Peoples R China

ISBN: (纸本)9783031366215;9783031366222

Finding more solutions from different Pareto-optimal sets (PSs) in the decision space and maintaining the diversity of solutions at the Pareto front (PF) in the objective space are the two objectives of multimodal multiobjective optimization (MMO). This paper proposes a brain storm optimization algorithm with a non-dominated special crowding distance sorting strategy (BSO-SCD) to ensure a sufficient number of good enough solutions and diversity in both the decision and objective spaces. Based on the brain storm optimization (BSO) algorithm, the BSO-SCD algorithm enhances the ability of diversity maintenance and has three steps. Firstly, k-means clustering divides the population into multiple subpopulations in the decision space to help locate various optimal solutions. Secondly, the non-dominated special crowding distance sorting (SCD) strategy, considering the diversity of decision space and objective space simultaneously, is used to find each subpopulation cluster center and stored in an archive. Finally, the solutions in the archive are selected after the iteration. The performance of the BSO-SCD algorithm and the other five algorithms is verified on 12 MMO benchmark functions. Experimental results show that the BSO-SCD algorithm could find as many equivalent PSs as possible in the decision space and guarantee a good PF distribution in the objective space.

关键词： Brain storm optimization multimodal multiobjective optimization multiobjective optimization multimodal 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：