检索结果-内蒙古大学图书馆

A New Fitness Function With Two Rankings for Evolutionary constrained multiobjective optimization

IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2021年第8期51卷 5005-5016页

作者： Ma, Zhongwei Wang, Yong Song, Wu Cent South Univ Sch Comp Sci & Engn Changsha 410083 Peoples R China Cent South Univ Sch Automat Changsha 410083 Peoples R China Hainan Trop Ocean Univ Coll Elect & Informat Engn Sanya 572022 Peoples R China

Among the constraint-handling techniques (CHTs) in constrained multiobjective optimization, constrained dominance principle (CDP) is simple, flexible, nonparametric, and easy to be embedded into multiobjective evolutionary algorithms. However, CDP always prefers constraints to objectives, which tends to cause premature convergence. To overcome this drawback, we propose a new CHT on the basis of CDP. In our CHT, the fitness function of each solution is defined as the weighted sum of two rankings: one is the solution's ranking based on CDP and the other is the solution's ranking based on Pareto dominance (i.e., without considering any constraints). It is evident that these two rankings favor the "feasibility" and "optimality" of each solution, respectively. More importantly, the weight employed in the fitness function is related to the proportion of feasible solutions in the current population, enabling the population to adaptively balance constraints and objectives in the evolutionary process. The effectiveness of our CHT is evaluated on three test suites and the experimental results demonstrate that our CHT shows better or highly competitive performance compared with other representative CHTs.

关键词： Sociology Evolutionary computation Pareto optimization Technological innovation Tuning constrained multiobjective optimization constraint-handling techniques evolutionary algorithms fitness function

来源：评论

学校读者我要写书评

暂无评论

Synchronous Wireless Sensor and Sink Placement Method Using Dual-Population Co-evolutionary constrained multiobjective optimization Algorithm

引用

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2023年第6期19卷 7561-7571页

作者： Yu, Qianqian Yang, Chen Dai, Guangming Peng, Lei Chen, Xiaoyu China Univ Geosci Sch Comp Sci Wuhan 430074 Peoples R China Beijing Inst Technol Sch Automat Beijing 100081 Peoples R China

Optimal wireless sensor placement (OWSP) plays a pivotal role in structural health monitoring. This study proposes a method to determine the simultaneous placement of sensors and sinks that minimizes energy consumption and maximizes information effectiveness. Network connectivity and reliability are critical constraints that determine the lifetime of wireless sensor networks. In this study, OWSP was formulated as a constrained multi-objective optimization problem with mixed-integer programming. Accordingly, a dual-population constrained multiobjective optimization (DCCMO) algorithm, which includes new crossover and mutation operators, was developed. In DCCMO, weak cooperation between two offspring populations is exploited to improve the efficiency of the solution search. The performance of DCCMO was compared to that of five other state-of-the-art algorithms using numerical examples with varying network parameters. DCCMO not only successfully matches the constrained Pareto front but also balances energy consumption and information effectiveness while exhibiting greater diversity and faster convergence than all other tested algorithms.

关键词： constrained multiobjective optimization mixed-integer programming (MIP) optimal sensor and sink placement structural health monitoring wireless sensor networks (WSNs)

来源：评论

学校读者我要写书评

暂无评论

Cooperative multiobjective Evolutionary Algorithm With Propulsive Population for constrained multiobjective optimization

引用

IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2022年第6期52卷 3476-3491页

作者： Wang, Jiahai Li, Yanyue Zhang, Qingfu Zhang, Zizhen Gao, Shangce Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou 510275 Peoples R China Sun Yat Sen Univ Minist Educ Key Lab Machine Intelligence & Adv Comp Guangzhou 510275 Peoples R China Sun Yat Sen Univ Guangdong Key Lab Big Data Anal & Proc Guangzhou 510275 Peoples R China City Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China Univ Toyama Fac Engn Toyama 9308555 Japan

Convergence, diversity and feasibility are three important issues when solving constrained multiobjective optimization problems (CMOPs). To deal with the balance among convergence, diversity and feasibility well, this article proposes a cooperative multiobjective evolutionary algorithm with propulsive population (CMOEA-PP) for solving CMOPs. CMOEA-PP has two populations, including propulsive population and normal population, and these two populations work cooperatively. Specifically, propulsive population focuses on convergence. Normal population gives priority to feasibility and is obligated to maintain diversity. To cross through the infeasible region and reach the Pareto front (PF), propulsive population does not consider constraints in the early stage and only considers constraints in the later stage. To further accelerate the speed of convergence, propulsive population only searches for corner solutions and center solutions, while normal population searches for the whole PF. As a result, propulsive population can cross through the infeasible region because of the lack of attention to feasibility. In addition, propulsive population also can guide and accelerate the convergence of the evolutionary process. Comprehensive experiment results on several sets of benchmark problems demonstrate that CMOEA-PP is better than existing state-of-the-art competitors.

关键词： constrained multiobjective optimization constraint handling cooperative populations propulsive population

来源：评论

学校读者我要写书评

暂无评论

Cooperative Differential Evolution Framework for constrained multiobjective optimization

引用

IEEE TRANSACTIONS ON CYBERNETICS 2019年第6期49卷 2060-2072页

作者： Wang, Jiahai Liang, Guanxi Zhang, Jun Sun Yat Sen Univ Dept Comp Sci Guangzhou 510275 Guangdong Peoples R China Sun Yat Sen Univ Key Lab Machine Intelligence & Adv Comp Minist Educ Guangzhou 510275 Guangdong Peoples R China Sun Yat Sen Univ Guangdong Key Lab Big Data Anal & Proc Guangzhou 510275 Guangdong Peoples R China South China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Guangdong Peoples R China

This paper presents a cooperative differential evolution framework (CCMODE) for constrained multiobjective optimization, and two instantiations of the CCMODE framework are implemented. The proposed framework has (M+1) populations, including M subpopulations for constrained single-objective optimization and an archive population for constrained M-objective optimization. Each subpopulation performs its own constrained single-objective differential evolution to optimize the assigned constrained single-objective optimization problem. For the archive population, the constraint handling techniques (CHTs) are modified for constrained multiobjective optimization. The proposed framework takes the advantage of existing effective constrained single-objective optimization algorithms, and extends them to deal with constrained multiobjective optimization problems. In two instantiations, two CHTs are implemented in CCMODE framework, respectively. Experiment results on several sets of benchmark problems with two, three, and many objectives show that the proposed algorithm is better than existing state-of-the-art constrained multiobjective evolutionary algorithms. The effectiveness of the subpopulations is also discussed.

关键词： constrained multiobjective optimization constraint handling cooperative populations differential evolution (DE)

来源：评论

学校读者我要写书评

暂无评论

A Coevolutionary Framework for constrained multiobjective optimization Problems

引用

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 2021年第1期25卷 102-116页

作者： Tian, Ye Zhang, Tao Xiao, Jianhua Zhang, Xingyi 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 Minist Educ Key Lab Intelligent Comp & Signal Proc Hefei 230601 Peoples R China Nankai Univ Res Ctr Logist Tianjin 300071 Peoples R China Univ Surrey Dept Comp Sci Guildford GU2 7XH Surrey England Southern Univ Sci & Technol Dept Comp Sci & Engn Shenzhen 518055 Peoples R China

constrained multiobjective optimization problems (CMOPs) are challenging because of the difficulty in handling both multiple objectives and constraints. While some evolutionary algorithms have demonstrated high performance on most CMOPs, they exhibit bad convergence or diversity performance on CMOPs with small feasible regions. To remedy this issue, this article proposes a coevolutionary framework for constrained multiobjective optimization, which solves a complex CMOP assisted by a simple helper problem. The proposed framework evolves one population to solve the original CMOP and evolves another population to solve a helper problem derived from the original one. While the two populations are evolved by the same optimizer separately, the assistance in solving the original CMOP is achieved by sharing useful information between the two populations. In the experiments, the proposed framework is compared to several state-of-the-art algorithms tailored for CMOPs. High competitiveness of the proposed framework is demonstrated by applying it to 47 benchmark CMOPs and the vehicle routing problem with time windows.

关键词： Coevolution constrained multiobjective optimization evolutionary algorithm vehicle routing problem

来源：评论

学校读者我要写书评

暂无评论

Shift-Based Penalty for Evolutionary constrained multiobjective optimization and Its Application

引用

IEEE TRANSACTIONS ON CYBERNETICS 2023年第1期53卷 18-30页

作者： Ma, Zhongwei Wang, Yong Cent South Univ Sch Automat Changsha 410083 Peoples R China Hunan Xiangjiang Artificial Intelligence Acad Changsha 410083 Peoples R China

This article presents a new constraint-handling technique (CHT), called shift-based penalty (ShiP), for solving constrained multiobjective optimization problems. In ShiP, infeasible solutions are first shifted according to the distributions of their neighboring feasible solutions. The degree of shift is adaptively controlled by the proportion of feasible solutions in the current parent and offspring populations. Then, the shifted infeasible solutions are penalized based on their constraint violations. This two-step process can encourage infeasible solutions to approach/enter the feasible region from diverse directions in the early stage of evolution, and guide diverse feasible solutions toward the Pareto optimal solutions in the later stage of evolution. Moreover, ShiP can achieve an adaptive transition from both diversity and feasibility in the early stage of evolution to both diversity and convergence in the later stage of evolution. ShiP is flexible and can be embedded into three well-known multiobjective optimization frameworks. Experiments on benchmark test problems demonstrate that ShiP is highly competitive with other representative CHTs. Further, based on ShiP, we propose an archive-assisted constrained multiobjective evolutionary algorithm (CMOEA), called ShiP(+), which outperforms two other state-of-the-art CMOEAs. Finally, ShiP is applied to the vehicle scheduling of the urban bus line successfully.

关键词： constrained multiobjective optimization constraint-handling techniques (CHTs) evolutionary algorithms (EAs) penalty shift

来源：评论

学校读者我要写书评

暂无评论

A Multiform optimization Framework for constrained multiobjective optimization

引用

IEEE TRANSACTIONS ON CYBERNETICS 2023年第8期53卷 5165-5177页

作者： Jiao, Ruwang Xue, Bing Zhang, Mengjie Victoria Univ Wellington Sch Engn & Comp Sci Wellington 6140 New Zealand

constrained multiobjective optimization problems (CMOPs) pose great difficulties to the existing multiobjective evolutionary algorithms (MOEAs), in terms of constraint handling and the tradeoffs between diversity and convergence. The constraints divide the search space into feasible and infeasible regions. A key to solving CMOPs is how to effectively utilize the information of both feasible and infeasible solutions during the optimization process. In this article, we propose a multiform optimization framework to solve a CMOP task together with an auxiliary CMOP task in a multitask setting. The proposed framework is designed to conduct a search in different sizes of feasible space that is derived from the original CMOP task. The derived feasible space is easier to search and can provide a useful inductive bias to the search process of the original CMOP task, by leveraging the transferable knowledge shared between them, thereby helping the search to toward the Pareto optimal solutions from both the infeasible and feasible regions of the search space. The proposed framework is instantiated in three kinds of MOEAs: 1) dominance-based;2) decomposition-based;and 3) indicator-based algorithms. Experiments on four sets of benchmark test problems demonstrate the superiority of the proposed method over four representative constraint-handling techniques. In addition, the comparison against five state-of-the-art-constrained MOEAs demonstrates that the proposed approach outperforms these contender algorithms. Finally, the proposed method is successfully applied to solve a real-world antenna array synthesis problem.

关键词： optimization Task analysis Statistics Sociology Pareto optimization Convergence Search problems constrained multiobjective optimization multiform optimization (MFO) transfer optimization

来源：评论

学校读者我要写书评

暂无评论

A Survey on Evolutionary constrained multiobjective optimization

引用

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 2023年第2期27卷 201-221页

作者： Liang, Jing Ban, Xuanxuan Yu, Kunjie Qu, Boyang Qiao, Kangjia Yue, Caitong Chen, Ke Tan, Kay Chen Zhengzhou Univ Sch Elect Engn Zhengzhou 450001 Peoples R China Zhongyuan Univ Technol Sch Elect & Informat Engn Zhengzhou 450007 Peoples R China Hong Kong Polytech Univ Dept Comp Hong Kong Peoples R China

Handling constrained multiobjective optimization problems (CMOPs) is extremely challenging, since multiple conflicting objectives subject to various constraints require to be simultaneously optimized. To deal with CMOPs, numerous constrained multiobjective evolutionary algorithms (CMOEAs) have been proposed in recent years, and they have achieved promising performance. However, there has been few literature on the systematic review of the related studies currently. This article provides a comprehensive survey for evolutionary constrained multiobjective optimization. We first review a large number of CMOEAs through categorization and analyze their advantages and drawbacks in each category. Then, we summarize the benchmark test problems and investigate the performance of different constraint handling techniques (CHTs) and different algorithms, followed by some emerging and representative applications of CMOEAs. Finally, we discuss some new challenges and point out some directions of the future research in the field of evolutionary constrained multiobjective optimization.

关键词： optimization Convergence Benchmark testing Pareto optimization Statistics Sociology Evolutionary computation Benchmark test problems constrained multiobjective optimization constraint handling evolutionary algorithms

来源：评论

学校读者我要写书评

暂无评论

A Competitive and Cooperative Swarm Optimizer for constrained multiobjective optimization Problems

引用

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 2023年第5期27卷 1313-1326页

作者： Ming, Fei Gong, Wenyin Li, Dongcheng Wang, Ling Gao, Liang China Univ Geosci Sch Comp Sci Wuhan 430074 Peoples R China Univ Texas Dallas Dept Comp Sci Richardson TX 75080 USA Tsinghua Univ Dept Automat Beijing 100084 Peoples R China Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Peoples R China

Solving multiobjective optimization problems (MOPs) through metaheuristic methods gets considerable attention. Based on the classical variation operators, several enhanced operators, as well as multiobjective optimization evolutionary algorithms, have been developed. Among these operators, the competitive swarm optimizer (CSO) exhibits promising performance. However, it encounters difficulties when tackling constrained MOPs (CMOPs) with large objective spaces or complex infeasible regions. In this article, a competitive and cooperative swarm optimizer is proposed, which contains two particle update strategies: 1) the CSO provides faster convergence speed to accelerate the approximation of the Pareto front and 2) the cooperative swarm optimizer suggests a mutual-learning strategy to enhance the ability to jump out of local feasible regions or local optima. We also present a new algorithm for CMOPs. The results on four benchmark suites with 47 instances demonstrate the superiority of our approach compared with other state-of-the-art methods. Additionally, its effectiveness on large-scale CMOPs has also been verified.

关键词： Competitive swarm optimizer (CSO) constrained multiobjective optimization evolutionary algorithm (EA) large-scale optimization

来源：评论

学校读者我要写书评

暂无评论

Balancing Constraints and Objectives by Considering Problem Types in constrained multiobjective optimization

引用

IEEE TRANSACTIONS ON CYBERNETICS 2023年第1期53卷 88-101页

作者： Xiang, Yi Yang, Xiaowei Huang, Han Wang, Jiahai South China Univ Technol Sch Software Engn Guangzhou 510006 Peoples R China South China Univ Technol Sch Software Engn Minist Educ Guangzhou 510006 Peoples R China South China Univ Technol Minist Educ Key Lab Big Data & Intelligent Robot Guangzhou 510006 Peoples R China Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou 510275 Peoples R China

constrained multiobjective optimization problems widely exist in real-world applications. To handle them, the balance between constraints and objectives is crucial, but remains challenging due to non-negligible impacts of problem types. In our context, the problem types refer particularly to those determined by the relationship between the constrained Pareto-optimal front (PF) and the unconstrained PF. Unfortunately, there has been little awareness on how to achieve this balance when faced with different types of problems. In this article, we propose a new constraint handling technique (CHT) by taking into account potential problem types. Specifically, inspired by the prior work, problems are classified into three primary types: 1) I;2) II;and 3) III, with the constrained PF being made up of the entire, part and none of the unconstrained counterpart, respectively. Clearly, any problem must be one of the three types. For each possible type, there exists a tailored mechanism being used to handle the relationships between constraints and objectives (i.e., constraint priority, objective priority, or the switch between them). It is worth mentioning that exact problem types are not required because we just consider their possibilities in the new CHT. Conceptually, we show that the new CHT can make a tradeoff among different types of problems. This argument is confirmed by experimental studies performed on 38 benchmark problems, whose types are known, and a real-world problem (with unknown types) in search-based software engineering. Results demonstrate that within both decomposition-based and nondecomposition-based frameworks, the new CHT can indeed achieve a good tradeoff among different problem types, being better than several state-of-the-art CHTs.

关键词： optimization Switches Statistics Sociology Software engineering Taxonomy Software algorithms constrained multiobjective optimization constraint handling techniques (CHTs) problem types tradeoff model (ToM)

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：