检索结果-内蒙古大学图书馆

A knowledge driven two-stage co-evolutionary algorithm for constrained multi-objective optimization

EXPERT SYSTEMS WITH APPLICATIONS 2025年 274卷

作者： Zhang, Wei Liu, Jianchang Li, Lin Liu, Yuanchao Wang, Honghai Northeastern Univ Natl Frontiers Sci Ctr Ind Intelligent & Syst Opti Shenyang Peoples R China Northeastern Univ Coll Informat Sci & Engn Shenyang Peoples R China

In recent years, constrained multi-objective optimization problems (CMOPs) have received wide attention. However, most solving methods for CMOPs still cannot balance objectives and constraints well since constraints make CMOPs have the complicated constrained Pareto front (CPF). This implies that utilizing the related CPF information may be helpful to solve CMOPs. Inspired by the human ability that summarizes the information into different knowledge to address different problems, a knowledge driven two-stage co-evolutionary algorithm (KTcoEA) for CMOPs is developed, including knowledge generation and knowledge application stages. The knowledge generation stage aims at generating two kinds of knowledge: explicit knowledge and implicit knowledge. To this end, main and auxiliary populations co-evolve towards the CPF and unconstrained Pareto front in turn, and then two kinds of knowledge are generated by storing non-dominated solutions and analyzing the correlation of two populations, respectively. Based on the implicit knowledge, reasonable evolution strategies and environmental selection manners are designed for the knowledge application stage, where the explicit knowledge acts as the main population. In this way, the main population can find the complete CPF (i.e., well balance objectives and constraints). In addition, to ensure the generated knowledge quality, a dynamic cooperation mechanism is proposed, which can dynamically adjust the focus of two populations on objectives and constraints based on the performance requirement and evolution status. Experimental results on five benchmark test suites and five real-world applications demonstrate that KTcoEA is better than seven state-of-the-art algorithms on most test problems.

关键词： constrained multi-objective optimization co-evolutionary algorithm Knowledge Two-stage Dynamic cooperation strategy

来源：评论

学校读者我要写书评

暂无评论

A multi-dimensional co-evolutionary algorithm for multi-objective resource-constrained flexible flowshop with robotic transportation

引用

APPLIED SOFT coMPUTING 2025年 170卷

作者： Li, Jia-ke Li, Rong-hao Li, Jun-qing Yu, Xin Xu, Ying Yunnan Normal Univ Dept Math Kunming 650500 Yunnan Peoples R China HengXing Univ Sch Informat Engn Qingdao 266199 Peoples R China China Univ Min & Technol Sch Informat & Control Engn Xuzhou Peoples R China

In this study, a realistic flexible or hybrid flowshop scheduling problem (HFS) is investigated, in which the following constraints are embedded, i.e., resource-dependent processing, robotic arm loading, and transportation. To solve the considered problem, a multi-dimensional co-evolutionary algorithm (MDCEA) is proposed to minimize makespan and total energy consumption (TEC) simultaneously. First, in the MDCEA, solutions are encoded by a three-dimensional vector with a two-phase decoding heuristic. Then, the initialized population is divided into three subsets to focus on different search tasks. To improve the efficiency of the global search task, a dual-population-based variable dimension cooperative search method is developed. In addition, to explore the promising non-dominated solutions in different dimensions, a Q-learning-based dimension detection search method is designed for the local search task. Finally, to keep the diversity in the evolutionary process, a knowledge-based individual transfer strategy is conducted for populations. The proposed algorithm was tested on 25 randomly generated instances, and detailed comparisons verified the efficiency and robustness compared to six state-of-the-art algorithms was achieved.

关键词： Hybrid flowshop scheduling Resource constraint Transportation co-evolutionary algorithm

来源：评论

学校读者我要写书评

暂无评论

co-evolutionary algorithm: An efficient approach for bilevel programming problems

引用

ENGINEERING OPTIMIZATION 2014年第3期46卷 361-376页

作者： Li, Hecheng Fang, Lei Qinghai Normal Univ Dept Math Minist Educ Key Lab Tibetan Informat Proc Xining Peoples R China Chinese Acad Sci Acad Math & Syst Sci Beijing Peoples R China Nankai Univ Sch Business Tianjin 300071 Peoples R China

The bilevel programming problem involves two optimization problems, which is hierarchical, strongly NP-hard and very challenging for most existing optimization approaches. An efficient universal co-evolutionary algorithm is developed in this article to deal with various bilevel programming problems. In the proposed algorithm, evolutionary algorithms are used to explore the leader's and the follower's decision-making spaces interactively. Unlike other existing approaches, in the suggested procedure the follower's problem is solved in two phases. First, an evolutionary algorithm is run for a few generations to obtain an approximation of lower level solutions. In the second phase, from all approximate solutions obtained above, only a small number of good points are selected and evolved again by a newly designed multi-criteria evolutionary algorithm. The technique refines some candidate solutions and can efficiently reduce the computational cost of obtaining feasible solutions. Proof-of-principle experiments demonstrate the efficiency of the proposed approach.

关键词： co-evolutionary algorithm bilevel programming problems approximate solutions optimal solutions

来源：评论

学校读者我要写书评

暂无评论

Multi-strategy competitive-cooperative co-evolutionary algorithm and its application

引用

INFORMATION SCIENCES 2023年第1期635卷 328-344页

作者： Zhou, Xiangbing Cai, Xing Zhang, Hua Zhang, Zhiheng Jin, Ting Chen, Huayue Deng, Wu Sichuan Tourism Univ Sch Informat & Engn Chengdu 610100 Peoples R China Nanjing Forestry Univ Sch Sci Nanjing 210037 Peoples R China Nanjing Univ Sci & Technol Sch Comp Sci Nanjing 210094 Peoples R China China West Normal Univ Sch Comp Sci Nanchong 637002 Peoples R China Civil Aviat Univ China Sch Elect Informat & Automat Tianjin 300300 Peoples R China

In order to effectively solve multi-objective optimization problems (MOPs) and fully balance uni-formity and convergence, a multi-strategy competitive-cooperative co-evolutionary algorithm based on adaptive random competition and neighborhood crossover, namely MScoEA is developed in this paper. In the MScoEA, a new adaptive random competition strategy is designed to determine whether one sub-population loses diversity through the performance. A random competition pro-cess is executed to increase the sub-population diversity in order to compete for participation op-portunities in the next iteration. And the extra population is employed to store the found non -dominated solutions. A new neighborhood crossover strategy is designed to enhance the local search ability. Finally, three different types of multi-objective benchmark functions are selected to verify the effectiveness of the MScoEA. The experiment results show that the MScoEA can effec-tively balance convergence and uniformity, and obtains better optimization performance and robustness by comparing with other algorithms. The convergence performance of the adaptive random competition and the neighborhood crossover strategies are also analyzed in detail.

关键词： Multi-objective optimization co-evolutionary algorithm competition and cooperation Multi-strategy Optimization performance

来源：评论

学校读者我要写书评

暂无评论

cooperative co-evolutionary algorithm for multi-objective optimization problems with changing decision variables

引用

INFORMATION SCIENCES 2022年 607卷 278-296页

作者： Xu, Biao Gong, Dunwei Zhang, Yong Yang, Shengxiang Wang, Ling Fan, Zhun Zhang, Yonggang Shantou Univ Dept Elect Engn Shantou 515063 Peoples R China Jilin Univ Key Lab Symbol Computat & Knowledge Engn Minist Educ Changchun 130012 Peoples R China Key Lab Digital Signal & Image Proc Guangdong Prov Shantou 515063 Peoples R China China Univ Min & Technol Sch Informat & Control Engn Xuzhou 221116 Peoples R China De Montfort Univ Ctr Computat Intelligence Sch Comp Sci & Informat Leicester LE1 9BH England Tsinghua Univ Dept Automat Beijing 100084 Peoples R China

Multi-objective optimization problems (MOPs) with changing decision variables exist in the actual industrial production and daily life, which have changing Pareto sets and complex relations among decision variables and are difficult to solve. In this study, we present a cooperative co-evolutionary algorithm by dynamically grouping decision variables to effectively tackle MOPs with changing decision variables. In the presented algorithm, decision variables are grouped into a series of groups using maximum entropic epistasis (MEE) at first, with decision variables in different groups owning a weak dependency. Subsequently, a sub-population is generated to solve decision variables in each group with an existing multi-objective evolutionary algorithm (MOEA). Further, a complete solution including all the decision variables is achieved through the cooperation among sub populations. Finally, when a decision variable is added or deleted from the existing problem, the grouping of decision variables is dynamically adjusted based on the correlation between the changed decision variable and existing groups. To verify the performance of the developed method, the presented method is compared with five popular methods by tackling eight benchmark optimization problems. The experimental results reveal that the presented method is superior in terms of diversity, convergence, and spread of solutions on most benchmark optimization problems.(c) 2022 Elsevier Inc. All rights reserved.

关键词： Dynamic optimization Multi-objective optimization co-evolutionary algorithm Grouping correlation

来源：评论

学校读者我要写书评

暂无评论

On generating interpretable and precise fuzzy systems based on Pareto multi-objective cooperative co-evolutionary algorithm

引用

APPLIED SOFT coMPUTING 2011年第1期11卷 1284-1294页

作者： Zhang, Yong Wu, Xiao-bei Xing, Zong-yi Hu, Wei-Li Nanjing Univ Sci & Technol Sch Automat Nanjing Jiangsu Peoples R China

A novel approach to construct a set of interpretable and precise fuzzy systems based on the Pareto multi-objective cooperative co-evolutionary algorithm (PMOCCA) is proposed in this paper. Firstly, feature selection is used to reduce the dimensionality of the data in order to improve the performance and reduce computational burden. Secondly, the fuzzy clustering algorithm is employed to identify the initial fuzzy system. Thirdly, the PMOCCA is carried out to evolve the initial fuzzy system to optimize the number of rules, the antecedents of the rules and the parameters of the antecedents simultaneously. Furthermore, the interpretability-driven simplification techniques are used iteratively to reduce the fuzzy systems, thus the interpretability of the fuzzy systems is improved. Finally, the proposed approach is applied to benchmark problems, and the results show its validity. Published by Elsevier B. V.

关键词： Fuzzy classification systems Fuzzy clustering Pareto-optimal solution co-evolutionary algorithm Interpretability

来源：评论

学校读者我要写书评

暂无评论

An improved co-evolutionary algorithm for green manufacturing by integration of recovery option selection and disassembly planning for end-of-life products

引用

INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2016年第18期54卷 5567-5593页

作者： Meng, Kai Lou, Peihuang Peng, Xianghui Prybutok, Victor Nanjing Univ Aeronaut & Astronaut Coll Mech & Elect Engn Nanjing Jiangsu Peoples R China Eastern Washington Univ Coll Business & Publ Adm Dept Management Spokane WA 99202 USA Univ North Texas Dept Informat Technol & Decis Sci Coll Business Denton TX USA

There is a strong need for recovery decision-making for end-of-life (EOL) products to satisfy sustainable manufacturing requirements. This paper develops and tests a profit maximisation model by simultaneously integrating recovery option selection and disassembly planning. The proposed model considers the quality of EOL components. This paper utilises an integrated method of multi-target reverserecursion and partial topological sorting to generate a feasible EOL solution that also reduces the complexity of genetic constraints handling. In order to determine recovery options, disassembly level and disassembly sequence simultaneously, this paper develops an improved co-evolutionary algorithm (ICA) to search for an optimal EOL solution. The proposed algorithm adopts the evolutionary mechanism of localised interaction and endosymbiotic competition. Further, an advanced local search operator is introduced to improve convergence performance, and a global disturbance strategy is also suggested to prevent premature convergence. Finally, this paper conducts a series of computational experiments under various scenarios to validate the meta-heuristic integrated decision-making model proposed and the superiority of the developed ICA. The results show that the proposed approach offers a strong and flexible decision support tool for intelligent recovery management in a ubiquitous information environment. We discuss the theoretical and practical contributions of this paper and implications for future research.

关键词： end-of-life product recovery disassembly planning co-evolutionary algorithm meta-heuristics computational experiments

来源：评论

学校读者我要写书评

暂无评论

A multi-objective co-evolutionary algorithm for energy and cost-oriented mixed-model assembly line balancing with multi-skilled workers

引用

EXPERT SYSTEMS WITH APPLICATIONS 2024年 236卷

作者： Zhang, Zikai Chica, Manuel Tang, Qiuhua Li, Zixiang Zhang, Liping Wuhan Univ Sci & Technol Key Lab Met Equipment & Control Technol Minist Educ Wuhan 430081 Peoples R China Wuhan Univ Sci & Technol Hubei Key Lab Mech Transmiss & Mfg Engn Wuhan 430081 Peoples R China Wuhan Univ Sci & Technol Precis Mfg Inst Wuhan 430081 Peoples R China Univ Granada DaSCI Data Sci & Computat Intelligence Andalusian Res Inst Granada 18071 Spain Univ Newcastle Sch Elect Engn & Comp Callaghan NSW 2308 Australia

Energy-saving, one of the most significant strategies for green manufacturing, has become the focus of more and more scholars and enterprise managers. Hence, this work addresses the minimization of energy and cost requirements on mixed-model multi-manned assembly line balancing with multi-skilled workers. A new mixed integer linear programming model is proposed to define the problem. Additionally, a novel multi-objective co-evolutionary algorithm is designed to achieve the trade-off between energy and cost requirements. This algorithm includes a two-layer solution representation to achieve full coverage of the solution space and a new decoding mechanism with idle time reduction. A collaborative initialization as the first stage of the algorithm is extended to get high-quality and great-diversity initial solutions. A self-learning evolution for each sub-population with four problem-specific evolutionary operators is developed to explore task or worker assignment sequences, and a dual-cooperation strategy is proposed to enhance the interaction between sub populations. The final experiments, based on 269 instances, demonstrate that the improvement components are effective and the proposed algorithm is superior to seven latest multi-objective evolutionary algorithms from numerical, statistical and differential analyses.

关键词： Multi-objective optimization co-evolutionary algorithm Energy requirement Mixed-model multi-manned assembly line balancing Multi-skilled workers

来源：评论

学校读者我要写书评

暂无评论

A dynamic-ranking-assisted co-evolutionary algorithm for constrained multimodal multi-objective optimization

引用

SWARM AND evolutionary coMPUTATION 2024年 91卷

作者： Li, Guoqing Zhang, Weiwei Yue, Caitong Wang, Yirui Xin, Yu Gao, Kui Ningbo Univ Fac Elect Engn & Comp Sci Ningbo 315211 Peoples R China Zhengzhou Univ Light Ind Sch Comp Sci & Technol Zhengzhou 450000 Peoples R China Zhengzhou Univ Sch Elect & Informat Engn Zhengzhou 450001 Peoples R China Fuzhou Univ Coll Comp & Data Sci Fuzhou 350108 Peoples R China

constrained multimodal multi-objective optimization problems (CMMOPs) are characterized by multiple constrained Pareto sets (CPSs) sharing the same constrained Pareto front (CPF). The challenge lies in efficiently identifying equivalent CPSs while maintaining a balance among convergence, diversity, and constraints. Addressing this challenge, we propose a dynamic-ranking-based constraint handling technique implemented in a co-evolutionary algorithm, named DRCEA, specifically designed for solving CMMOPs. To search for equivalent CPSs, we introduce a co-evolutionary framework involving two populations: a convergence-first population and a constraint-first population. The co-evolutionary framework facilitates knowledge transfer and sustains diverse solutions. Subsequently, a dynamic ranking strategy is employed with dynamic weight parameters that consider both dominance and constraint relationships among individuals. Within the convergence-first population, the weight parameter for convergence gradually decreases, while the constraint parameter increases. conversely, in the constraint-first population, the weight parameter for constraints gradually decreases, while the convergence parameter increases. This approach ensures a well-balanced consideration of convergence and constraints within the two distinct populations. Experimental results on the CMMOP test suite and the real-world CMMOP test scenario validate the effectiveness of the proposed dynamic-ranking-based constraint handling technique, demonstrating the superiority of DRCEA over seven state-of-the-art algorithms.

关键词： Dynamic ranking co-evolutionary algorithm constrained multimodal multi-objective opti- mization convergence-first constraint-first

来源：评论

学校读者我要写书评

暂无评论

A novel multi-objective co-evolutionary algorithm based on decomposition approach

引用

APPLIED SOFT coMPUTING 2018年 73卷 50-66页

作者： Liang, Zhengping Wang, Xuyong Lin, Qiuzhen Chen, Fei Chen, Jianyong Ming, Zhong Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen Peoples R China

Recently, co-evolution mechanism is exploited to solve multi-objective optimization problems by using multiple subpopulations on a cooperative manner, such as co-evolutionary multi-swarm particle swarm optimization (CMPSO) based on the multiple subpopulations for multi-objective optimization (MPMO) framework, which is also extended to cooperative multi-objective differential evolution (CMODE). Although their approaches of optimizing each objective with each subpopulation are effective, the evolution and selection methods conducted on external archive are also important for co-evolution, as they significantly impact a lot on the quality and the distribution of final solutions. In this paper, we present a novel multi-objective co-evolutionary algorithm based on decomposition approach (MCEA), also using the subpopulation to enhance each objective. A more powerful DE operator with an adaptive parameters control is run on both of multiple subpopulations and external archive, which helps to improve each objective and diversify the tradeoff solutions on external archive. Moreover, computational resource assignment is also realized between each subpopulation and external archive. Once one objective stops to be enhanced, this objective may find its optimal value and more computational resource can be assigned to evolve other objectives and external archive. By this way, the tradeoff between all the objectives can be well balanced and external archive also has more opportunities for evolution. After evaluating the proposed algorithm on 31 benchmark test problems, such as the ZDT, DTLZ, WFG, OF series problems, the experimental results show that MCEA presents some advantages over two co-evolutionary algorithms (i.e., CMPSO and CMODE) and several state-of-the-art multi-objective evolutionary algorithms (i.e., NSGAII, SPEA2, MOEA/D-DE, MOEA/D-STM, MOEA/D-FRRMAB and MOEA/D-IR). (C) 2018 Elsevier B.V. All rights reserved.

关键词： co-evolutionary algorithm Multi-objective optimization Differential evolution Resource assignment

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：