检索结果-内蒙古大学图书馆

Angle-based constrained Dominance Principle in MOEA/D for constrained multi-objective optimization Problems

Angle-based Constrained Dominance Principle in MOEA/D for Co...

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

作者： Fan, Zhun Li, Wenji Cai, Xinye Hu, Kaiwen Lin, Huibiao Li, Hui Shantou Univ Dept Elect Engn Shantou 515063 Guangdong Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing 210016 Jiangsu Peoples R China Xi An Jiao Tong Univ Sch Math & Stat Xian 710049 Shaanxi Peoples R China

ISBN: (纸本)9781509006229

This paper proposes a new constraint handling method named Angle-based constrained Dominance Principle (ACDP). Unlike the original constrained Dominance Principle (CDP), this approach adopts the angle information of the objective functions to enhance the population's diversity in the infeasible region. To be more specific, given two infeasible solutions, if the angle of the solutions is greater than a given threshold, they are considered to be non-dominated by each other. For a feasible solution and an infeasible solution, if the angle of the solutions is less than a given threshold, the feasible solution is better, otherwise they are non-dominated. To verify the proposed constraint handling approach ACDP, eight test problems CMOP1 to CMOP8 are introduced. The suggested algorithm MOEA/D-ACDP is compared with MOEA/D-CDP and NSGA-II-CDP on CMOP1 to CMOP8. The experimental results demonstrate that ACDP performs better than CDP in the framework of MOEA/D, and MOEA/D-ACDP is significantly better than NSGA-II-CDP, especially on the test instances with the very low ratio of feasible region against the whole objective space.

关键词： Constraint-handling Techniques constrained multi-objective optimization

来源：评论

学校读者我要写书评

暂无评论

An archive-based two-stage evolutionary algorithm for constrained multi-objective optimization problems

引用

SWARM AND EVOLUTIONARY COMPUTATION 2022年第0期75卷

作者： Bao, Qian Wang, Maocai Dai, Guangming Chen, Xiaoyu Song, Zhiming Li, Shuijia China Univ Geosci Sch Comp Sci Wuhan 430074 Peoples R China China Univ Geosci Hubei Key Lab Intelligent Geoinformat Proc Wuhan 430074 Peoples R China 388 Lumo Rd Wuhan Hubei Peoples R China

An important factor in constrained multi-objective evolutionary algorithms (CMOEAs) is how to make optimal use of the information of feasible and infeasible solutions. To fully utilize these promising solutions, this paper proposes an archive-based two-stage evolutionary algorithm, called AT-CMOEA, for solving constrained multi-objective optimization problems (CMOPs). AT-CMOEA divides the evolutionary process into two stages - exploration and exploitation. The purpose of the exploration stage is to encourage a broader exploration of the search space to discover some promising regions, which is achieved by two populations with different priorities of constraints and objectives. In the exploitation stage, the goal is to obtain a set of well-distributed Pareto-optimal solutions by utilizing the useful archived information found during the exploration stage, allowing the search resources to be focused on the region of interest. Then, the two populations cooperatively converge to the constrained Pareto front (CPF). Comprehensive experiments on a series of benchmark test problems and three real-world CMOPs demonstrate the competitiveness of our method when compared with other representative algorithms in terms of effectiveness and reliability in finding a set of well-distributed optimal solutions.

关键词： constrained multi-objective optimization Exploration Exploitation Two-stage Cooperative two populations

来源：评论

学校读者我要写书评

暂无评论

A novel two-archive evolutionary algorithm for constrained multi-objective optimization with small feasible regions

引用

KNOWLEDGE-BASED SYSTEMS 2022年 237卷 107693-107693页

作者： Xia, Mingming Dong, Minggang Guilin Univ Technol Sch Informat Sci & Engn Guilin 541004 Peoples R China Guangxi Key Lab Embedded Technol & Intelligent Sy Guilin 541004 Peoples R China

constrained multi-objective evolutionary algorithms (CMOEAs) have been extensively studied in recent years. However, the performance of most of traditional CMOEAs is unsatisfied for constrained multi-objective optimization problems (CMOPs) with small feasible regions. Based on the idea of two-archive, this paper proposes a novel two-archive evolutionary algorithm for constrained multi objective optimization with small feasible regions. Specifically, we maintain two archives, named convergence-oriented archive (CA) and diversity-oriented archive (DA). To handle the CMOPs which feasible regions are small and far from the unconstrained Pareto front (PF), a cooperation-based mating selection mechanism is proposed. To strike a balance among convergence, diversity, and feasibility, a high-quality solution selection mechanism is proposed, which can help the CA approach PF from different directions and balance the convergence and diversity. To provide better diversity, a dynamic selection strategy is designed to update DA according to the status of the CA. In addition, in order to make the population evenly distributed in feasible regions, a replacement mechanism of the ideal point is designed. Compared with the four state-of-the-art constrained multi-objective evolutionary optimization algorithms, comprehensive experiments on a series of benchmark problems fully demonstrate the superiority of the proposed algorithm in terms of increment p and hypervolume (HV). (c) 2021 Elsevier B.V. All rights reserved.

关键词： constrained multi-objective optimization Two-archive algorithm Evolutionary algorithm Convergence Diversity

来源：评论

学校读者我要写书评

暂无评论

A two-stage evolutionary algorithm based on three indicators for constrained multi-objective optimization

引用

EXPERT SYSTEMS WITH APPLICATIONS 2022年第0期195卷 116499-116499页

作者： Dong, Jun Gong, Wenyin Ming, Fei Wang, Ling China Univ Geosci Sch Comp Sci Wuhan 430074 Peoples R China Tsinghua Univ Dept Automat Beijing 100084 Peoples R China

One of the key issues in solving constrained multi-objective optimization problems (CMOPs) is balancing the three indicators of convergence, diversity, and feasibility. We believe at different stages of the evolution, different indicators should be emphasized. This paper proposes a two-stage constrained multi-objective evolutionary algorithm (CMOEA) with different emphases on the three indicators. In Stage-I, the Pareto nondominated sorting and the unbiased two-objective model are used to evaluate the three indicators. The purpose of Stage-I is to obtain solutions with good distribution and to prevent the population from falling into local optima. After Stage-I, almost all of the individuals in the population are distributed in the vicinity of all feasible areas. The goal of Stage-II is to quickly converge the population to the Pareto front (PF). Thirty benchmark CMOPs and four real-world problems were used to evaluate the performance of our algorithm. Experimental results indicate that our approach achieved significantly better results or was at least competitive to the compared eight state-of-the-art CMOEAs on most of the benchmark problems.

关键词： constrained multi-objective optimization Evolutionary algorithm Two-stage Convergence Diversity Feasibility

来源：评论

学校读者我要写书评

暂无评论

A simple two-stage evolutionary algorithm for constrained multi-objective optimization

引用

KNOWLEDGE-BASED SYSTEMS 2021年 228卷 107263-107263页

作者： Ming, Fei Gong, Wenyin Zhen, Huixiang Li, Shuijia Wang, Ling Liao, Zuowen China Univ Geosci Sch Comp Sci Wuhan 430074 Peoples R China Tsinghua Univ Dept Automat Beijing 100084 Peoples R China Beibu Gulf Univ Beibu Gulf Ocean Dev Res Ctr Qinzhou 535000 Peoples R China

The widespread existence of constrained multi-objective optimization problems (CMOPs) in practical applications encourages researchers to devote more efforts to the development of constrained multi objective evolutionary algorithms (CMOEAs). An efficient way of handling CMOPs is to balance the satisfaction of constrains and the optimization of objective functions. Although many approaches have been designed to this end, most of them are still unable to handle CMOPs with diverse characteristics effectively. To remedy this issue, a simple and generic two-stage framework is proposed in this paper to achieve better efficiency and versatility for CMOPs. Specifically, the whole search process is simply divided into two stages with different purposes: (i) Stage 1 focuses on approaching the unconstrained Pareto front (PF) and storing the obtained feasible solutions in the archive. During stage 1, the archive is updated to promote convergence and diversity;(ii) Stage 2 is mainly designed to obtain the constrained PF of CMOPs. First, the solutions in the archive are used to form the initial population. Afterwards, the constrained Pareto front (CPF) is extensively explored. Based on this framework, a CMOEA, referred to as C-TSEA, is presented. Fifty-seven instances in five benchmark test suites are chosen to evaluate the performance of our approach. Compared with seven advanced CMOEAs, the results demonstrate the superiority or at least competitiveness of C-TSEA. The generality of the proposed framework is also verified by integrating other methods in the proposed framework. (C) 2021 Elsevier B.V. All rights reserved.

关键词： constrained multi-objective optimization Evolutionary algorithm Two-stage framework constrained Pareto front

来源：评论

学校读者我要写书评

暂无评论

An adaptive tradeoff evolutionary algorithm with composite differential evolution for constrained multi-objective optimization

引用

SWARM AND EVOLUTIONARY COMPUTATION 2023年 83卷

作者： Feng, Jian Liu, Shaoning Yang, Shengxiang Zheng, Jun Liu, Jinze Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Peoples R China De Montfort Univ Sch Comp Sci & Informat Leicester LE1 9BH England

Convergence, diversity, and feasibility are crucial factors in solving constrained multi-objective optimization problems (CMOPs). Their imbalance can result in the algorithm failing to converge well to the Pareto front, especially when dealing with complex CMOPs. To address this issue, we propose an adaptive tradeoff evolutionary algorithm (ATEA), which can adjust the environment selection strategy based on the characteristics of problem, aiming to achieve a balance between convergence and diversity while ensuring feasibility of the population. The ATEA divides the search process into three phases: In the extended exploration phase, a global search is conducted using a guided constraint relaxation technique to enable the population to quickly traverse the infeasible region and approach the feasible region. In the tradeoff exploration phase, constraints are further detected and estimated to retain more feasible individuals and competing infeasible individuals, allowing the population to accurately identify all possible feasible regions and gradually expand towards the feasible boundary. The exploitation phase explores under-explored regions in the earlier phases with the aim of accelerating the convergence of the population and escaping from the local optima. Extensive experiments conducted on four benchmark test suites demonstrate that ATEA exhibits superior performance in three benchmark test suites compared with six other state-of-the-art algorithms.

关键词： Adaptive tradeoff model Composite differential evolution constrained multi-objective optimization Constraint handling techniques

来源：评论

学校读者我要写书评

暂无评论

A comparative study of evolutionary algorithms and particle swarm optimization approaches for constrained multi-objective optimization problems

引用

SWARM AND EVOLUTIONARY COMPUTATION 2024年 91卷

作者： McNulty, Alanna Ombuki-Berman, Beatrice Engelbrecht, Andries Brock Univ Dept Comp Sci St Catharines ON Canada Stellenbosch Univ Dept Ind Engn Stellenbosch South Africa Stellenbosch Univ Comp Sci Div Stellenbosch South Africa

Many real-world optimization problems contain multiple conflicting objectives as well as additional problem constraints. These problems are referred to as constrained multi-objective optimization problems (CMOPs). Many meta-heuristics for solving CMOPs, called constrained multi-objective meta-heuristics (CMOMHs) have been introduced in the literature, including those using particle swarm optimization (PSO)(Kennedy and Eberhart, 1995), genetic algorithms (GAs)(Man et al., 1996), and differential evolution (DE)(Storn and Price, 1997). CMOMHs can be grouped into four different classes: classic CMOMHs, co-evolutionary approaches, multi-stage approaches, and multi-tasking approaches. An extensive comparative study of twenty different CMOMHs on a wide variety of test problems, including real-world CMOPs in the fields of science and engineering, is conducted. A multi-swarm PSO approach called constrained multi-guide particle swarm optimization (ConMGPSO) is introduced and compared to the best-performing previous approaches according to the comparative study. The performance of each algorithm was found to be problem dependent, however the best overall approaches were ConMGPSO, paired-offspring constrained evolutionary algorithm (POCEA)(He et al., 2021), adaptive non-dominated sorting genetic algorithm III (A-NSGA-III)(Jain and Deb, 2014), and constrained multi-objective framework using Q-learning and evolutionary multi-tasking (CMOQLMT)(Ming and Gong, 2023). ConMGPSO and POCEA had the best performance on the CF benchmark set, which contains examples of bi-objective and tri-objective CMOPs with disconnected CPOFs. The CMOQLMT approach had the best performance on the DAS-CMOP benchmark set, which contain additional difficulty in terms of feasibility-, convergence-, and diversity-hardness. For the selected real-world CMOPs, A-NSGA-III had the best performance overall. ConMGPSO was shown to have the best performance on the process, design, and synthesis problems, and had compet

关键词： constrained multi-objective optimization Particle swarm optimization Differential evolution Genetic algorithms Real-world problems

来源：评论

学校读者我要写书评

暂无评论

A multi-input and dual-output wind speed interval forecasting system based on constrained multi-objective optimization problem and model averaging

引用

ENERGY CONVERSION AND MANAGEMENT 2024年 319卷

作者： Lv, Mengzheng Wang, Jianzhou Wang, Shuai Zhao, Yang Gao, Jialu Wang, Kang Dongbei Univ Finance & Econ Sch Stat Dalian 116025 Peoples R China Macau Univ Sci & Technol Inst Syst Engn Taipa 999078 Macau Peoples R China

The uncertainty analysis of wind speed forecasting using the Lower Upper Bound Estimation (LUBE) is an advanced interval prediction method that does not require assumptions about data distribution. However, previous studies have primarily relied on single neural network models, overlooking the benefits of model averaging. Moreover, they assumed symmetric upper and lower bounds of true values in training data, which may not hold for real data with asymmetric features. To address these issues, we propose a multi-input dual-output wind speed interval forecasting system (MDWSIFS). Utilizing neural network models, we create two different outputs for each model by scaling the output values with interval scaling coefficients 1 + gamma 1 and 1 - gamma 2, respectively. Subsequently, we propose two constrained multi-objective optimization problems and introduce non-dominated sorting genetic algorithm II (NSGA-II), a method that has been proven to be highly suitable for solving constrained bi-objective optimization problems. By using NSGA-II to optimize a multi-objective problem with coverage probability constraints, the optimal coefficients gamma 1 and gamma 2 are determined, thereby the prediction interval is defined. Finally, through a model averaging strategy integrated with several neural network models, we use NSGA-II to optimize the weights of sub-models to achieve a more accurate final prediction interval. The test results indicate the superiority of MDWSIFS over existing models, with the metric reaching unprecedented levels across multiple datasets. These findings not only signify an advancement in wind speed forecasting but also promise improved efficiency in wind energy utilization and reduced operational costs for power systems.

关键词： Lower and upper bound estimation Wind speed interval forecasting constrained multi-objective optimization problem Dual-output neural network Model averaging

来源：评论

学校读者我要写书评

暂无评论

Migration-based algorithm library enrichment for constrained multi-objective optimization and applications in algorithm selection

引用

INFORMATION SCIENCES 2023年第1期649卷

作者： Wang, Yan Zuo, Mingcheng Gong, Dunwei China Univ Min & Technol Sch Informat & Control Engn Xuzhou 221116 Jiangsu Peoples R China China Univ Min & Technol Artificial Intelligence Res Inst Xuzhou 221116 Jiangsu Peoples R China Qingdao Univ Sci & Technol Sch Informat Sci & Technol Qingdao 266061 Peoples R China

It is of necessity to select appropriate optimization algorithms from an algorithm library due to the universality of constrained multi-objective optimization problems and the suitability of intelligent optimization algorithms, which requires a rich optimization algorithm library. This paper proposes a migration-based method of enriching the algorithm library for constrained multi-objective optimization problems. After calculating the similarity between problems based on their landscape features, the proposed method calculates the migration probabilities of intelligent optimization algorithms solving similar problems based on the performance of each algorithm and the similarity between problems. According to the redundancy and compatibility of components, the algorithms with large migration probabilities enrich the algorithm library for solving the current problem. Based on the enhanced algorithm library, a Softmax regression model is trained to generate an optimal intelligent algorithm to solve the current problem. The proposed method is applied to solve a series of constrained multi-objective optimization benchmark problems and the operation optimization problems of an integrated coal mine energy system, and the experimental results verify its effectiveness and feasibility.

关键词： constrained multi-objective optimization Intelligent optimization algorithm Algorithm library enrichment Migration Integrated coal mine energy system

来源：评论

学校读者我要写书评

暂无评论

Deep reinforcement learning assisted novelty search in Voronoi regions for constrained multi-objective optimization

引用

SWARM AND EVOLUTIONARY COMPUTATION 2024年 91卷

作者： Yang, Yufei Zhang, Changsheng Liu, Yi Ning, Jiaxu Guo, Ying Northeastern Univ Software Coll Shenyang 110819 Peoples R China Shenyang Ligong Univ Sch Informat Sci & Engn Shenyang 110159 Peoples R China Ningxia Inst Sci & Technol Coll Comp Sci & Engn Shizuishan 753000 Peoples R China

Solving constrained multi-objective optimization problems (CMOPs) requires optimizing multiple conflicting objectives while satisfying various constraints. Existing constrained multi-objective evolutionary algorithms (CMOEAs) cross infeasible regions by ignoring constraints. However, these methods might neglect promising search directions, leading to insufficient exploration of the search space. To address this issue, this paper proposes a deep reinforcement learning assisted constrained multi-objective quality-diversity algorithm. The proposed algorithm designs a diversity maintenance mechanism to promote evenly coverage of the final solution set on the constrained Pareto front. Specifically, first, a novelty-oriented archive is created using a centroid Voronoi tessellation, which divides the search space into a desired number of Voronoi regions. Each region acts as a repository of non-dominated solutions with different phenotypic characteristics to provide diversity information and supplementary evolutionary trails. Secondly, to improve resource utilization, a deep Q-network is adopted to learn a policy to select suitable Voronoi regions for offspring generation based on their novelty scores. The exploration of these regions aims to find a set of diverse, high-performing solutions to accelerate convergence and escape local optima. Compared with eight state-of-the-art CMOEAs, experimental studies on four benchmark suites and nine real-world applications demonstrate that the proposed algorithm exhibits superior or at least competitive performance, especially on problems with discrete and narrow feasible regions.

关键词： constrained multi-objective optimization Quality-diversity algorithm Novelty search Deep reinforcement learning

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：