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A cooperative coevolution algorithm for multi-objective fuzzy distributed hybrid flow shop

KNOWLEDGE-BASED SYSTEMS 2020年 194卷 105536-105536页

作者： Zheng, Jie Wang, Ling Wang, Jing-jing Tsinghua Univ Dept Automat Beijing 100084 Peoples R China

With consideration of uncertainty in the distributed manufacturing systems, this paper addresses a multi-objective fuzzy distributed hybrid flow shop scheduling problem with fuzzy processing times and fuzzy due dates. To optimize the fuzzy total tardiness and robustness simultaneously, a cooperative coevolution algorithm with problem-specific strategies is proposed by reasonably combining the estimation of distribution algorithm (EDA) and the iterated greedy (IG) search. In the EDA-mode search, a problem-specific probability model is established to reduce the solution space and a sample mechanism is proposed to generate new individuals. To enhance exploitation, a specific local search is designed to improve performance of non-dominated solutions. Moreover, destruction and reconstruction methods in the IG-mode search are employed for further exploiting better solutions. To balance exploration and exploitation capabilities, a cooperation scheme for mode switching is designed based on the information entropy and the diversity of elite solutions. The effect of the key parameters on the performances of the proposed algorithm is investigated by Taguchi design of experiment method. Comparative results and statistical analysis demonstrate the effectiveness of the proposed algorithm in solving the problem. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Multi-objective fuzzy distributed hybrid flow shop Fuzzy processing times and due dates Robustness cooperative coevolution algorithm Estimation of distribution algorithm Iterated greedy search

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A Hybrid cooperative coevolution algorithm for Fuzzy Flexible Job Shop Scheduling

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IEEE TRANSACTIONS ON FUZZY SYSTEMS 2019年第5期27卷 1008-1022页

作者： Sun, Lu Lin, Lin Gen, Mitsuo Li, Haojie Dalian Univ Technol Sch Software Dalian 116620 Peoples R China Dalian Univ Technol Int Sch Informat Sci Engn Dalian 116620 Peoples R China Fuzzy Log Syst Inst Iizuka Fukuoka 68041 Japan Dalian Univ Technol Key Lab Ubiquitous Network & Serv Software Liaoni Dalian 116023 Peoples R China Tokyo Univ Sci Tokyo 1628601 Japan

Flexible scheduling is one of the most significant core techniques for intelligent manufacturing systems. Realization of an optimized schedule through flexible resources assignment is critical to the application and popularization of flexible scheduling, especially in uncertain manufacturing environments. In this paper, we consider flexible job shop scheduling with uncertain processing time represented by fuzzy numbers, which is named fuzzy flexible job shop scheduling. We propose an effective hybrid cooperative coevolution algorithm (hCEA) for the minimization of fuzzy makespan. The hCEA combines particle swarm optimization with the genetic algorithm to improve the convergence ability. A parameter self-adaptive strategy is applied to the problems with different scale effectively as well. Five benchmarks and three large-scale problems with fuzzy processing time are adopted to test the hCEA. Computational results show that the hCEA performs better than the existing methods from the literature.

关键词： cooperative coevolution algorithm fuzzy scheduling flexible job shop scheduling

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A Formation Reconfiguration algorithm for Multi-UAVs Based on Distributed cooperative coevolutionary with an Adaptive Grouping Strategy

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Chinese Journal of Electronics 2020年第5期29卷 841-851页

作者： LIU Huaxian LIU Feng ZHANG Xuejun GUAN Xiangmin CHEN Jun School of Electronic and Information Engineering Beihang University General Aviation Institute of Zhejiang JianDe Department of General Aviation Civil Aviation Management Institute of China School of Engineering and Materials Science Queen Mary University of London

The Formation reconfiguration problem plays a crucial role in the implementation of complex tasks for multiple unmanned aerial vehicles, which attracted increasing attention in the past decade. Taking into consideration the control parameters and time discretization of the multi-UAVs in the 3 Dimensional(3-D) space, the formation reconfiguration problem can be formulated as a large-scale combinatorial optimization problem with complex constraints and tight couplings between variables. The problem results in the reduction in efficiency and effectiveness using classic bio-inspired algorithms. In this paper, a formation reconfiguration method based on cooperative coevolutionary algorithm is proposed along with a new decomposition strategy to improve the optimization capability and prevent premature convergence. In the proposed approach, variables of multi-UAV are divided into several sub-groups based on an adaptive grouping strategy. The proposed strategy groups the variables in order to better deal with the tight coupling among them, taking into account the variables’ variance and multi-UAVs characteristics of the formation reconfiguration problem. Therefore, each subgroup can adopt the Self-adaptive differential evolution strategy with neighborhood search(SaNSDE) with the aim to optimize the UAV’s control inputs using multithreaded programming. SaNSDE contributes to calculating the results in a fully distributed and paralleled *** solution is then obtained through cooperation and coordination with all subcomponents. Simulation results based on extreme scenarios adopted by previous researches demonstrate that the proposed algorithm outperformed the existing approaches including Particle swarm optimization(PSO), Differential evolution(DE),and the cooperative coevolution algorithms with different well-known grouping strategies.

关键词： coevolutionary algorithm formation reconfiguration problem self-adaptive differential evolution strategy evolutionary computation multiUAV Grouping strategy particle swarm optimisation cooperative coevolutionary decomposition strategy position control Formation reconfiguration Distributed differential evolution particle swarm optimization Multi-UAV adaptive grouping strategy large-scale combinatorial optimization problem cooperative coevolution algorithm formation reconfiguration algorithm autonomous aerial vehicles multiple unmanned aerial vehicles formation reconfiguration method search problems

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A Knowledge-Driven cooperative coevolutionary algorithm for Integrated Distributed Production and Transportation Scheduling Problem

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2024年 22卷 7435-7448页

作者： Wang, Jingjing Wang, Ling Han, Honggui Beijing Univ Technol Engn Res Ctr Digital Community Sch Informat Sci & Technol Minist Educ Beijing 100084 Peoples R China Tsinghua Univ Dept Automat Beijing 100084 Peoples R China

With increasing market competition, integration between production and transportation in supply chain has been valued to improve the operational performance. Meanwhile, the distributed manufacturing has emerged as a modern paradigm, fostering flexible and intelligent development. In this paper, we address an integrated distributed production and transportation scheduling problem (IDPTSP), taking into account the production plan in distributed heterogeneous hybrid flow-shops, as well as the transportation decisions made by third-party logistics provider. First, the objective calculation is presented to minimize total costs and energy consumption during production and transportation process, and problem property is analyzed to obtain an optimal pickup time strategy. Second, a knowledge-driven cooperative coevolutionary algorithm (KCCA) is proposed, incorporating multiple problem-specific heuristics and operators based on the characteristics of the subproblems in IDPTSP. Third, Q learning assisted cooperative coevolutionary search is proposed via analyzing the interconnections among different subproblems to effectively and efficiently explore their search space. Fourth, local intensification search with multiple operators fusing prior knowledge is incorporated for low-density regions in objective space to enhance exploitation ability. Extensive experiments are carried out to test performances of the KCCA and the numerical comparisons demonstrate effectiveness of the specific designs and superiority of the KCCA over state-of-the-art algorithms in solving the IDPTSP. Note to Practitioners-Drawing inspiration from a real-life case in electronics supply, the focus on distributed manufacturing and the integration of manufacturing and logistics has garnered significant attention from managers due to the enterprises' need to reduce intermediate stocks and enhance operational performance. The primary focus of enterprises is on reducing costs during production and transportatio

关键词： Distributed scheduling hybrid flow-shop transportation energy-efficient knowledge cooperative coevolution algorithm Distributed scheduling hybrid flow-shop transportation energy-efficient knowledge cooperative coevolution algorithm

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An adaptive decentralized under voltage load shedding in distribution networks

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INTERNATIONAL TRANSACTIONS ON ELECTRICAL ENERGY SYSTEMS 2020年第11期30卷 1-22页

作者： Nemouchi, Hichem Tiguercha, Ahmed Ladjici, Ahmed Amine Univ Sci & Technol Houari Boumediene USTHB Dept Elect Engn LSEI Lab BP 32 Algiers 16111 Algeria

Blackout around the world generally manifests due to the voltage instability on distribution networks following major overloads, therefore the activation of the protection devices via a load shedding strategy is necessary to avoid severe contingencies. This article aims is to develop a new approach for under voltage load shedding (UVLS) ensuring two primordial parameters of shedding strategy, its amount and its load priority. The main contribution of this work lies in the application of a neuroevolution algorithm to enhance the UVLS relays decisions in the distribution network, under a decentralized process. The proposed algorithm adapts the amount of the shed loads in each bus, in a cooperative manner, in order to maintain the voltage stability of the system, and in where the priority of UVLS is defined by two voltage stability indices (SI): voltage SI and voltage electrical distance in addition to the measured voltage deviation. The efficiency of the proposed approach is validated for several overload scenarios performed in 33-bus IEEE distribution system, in where the performance of the proposed adaptive Load shedding outperforms the conventional scheme.

关键词： cooperative coevolution algorithm interruption cost function under voltage load shedding voltage electrical distance voltage stability index

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Integration of semi-fuzzy SVDD and CC-Rule method for supplier selection

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EXPERT SYSTEMS WITH APPLICATIONS 2014年第4期41卷 2083-2097页

作者： Guo, Xuesong Zhu, Zhengwei Shi, Jia Xi An Jiao Tong Univ Sch Publ Policy & Adm Xian 710049 Peoples R China

A model based on semi-fuzzy support vector domain description (semi-fuzzy SVDD) is put forward to address multi-classification problem involved in supplier selection. By preprocessing using semi-fuzzy kernel clustering algorithm, original samples are divided into two subsets: deterministic samples and fuzzy samples. Only the fuzzy samples, rather than all original ones, require expert judgment to decide their categories and are selected as training samples to accomplish SVDD specification. Therefore, the samples preprocessing method can not only decrease experts working strength, but also achieve less computational consumption and better performance of the classifier. Nevertheless, in order to accomplish practical decision making, another condition has to be met: good explanations to the decision. A rule extraction method based on cooperative coevolution algorithm (CCEA), is introduced to achieve the target. To validate the proposed methodology, samples from real world were employed for experiments, with results compared with conventional multi-classification support vector machine approaches and other artificial intelligence techniques. Moreover, in terms of rule extraction, experiments on key parameters, different methods including decompositional and pedagogical ones etc. were also conducted. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： Supplier selection Semi-fuzzy kernel clustering algorithm Support vector domain description cooperative coevolution algorithm

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cooperative Co-evolution and Data Mining for Planning Disassemly Sequence and Estimating Time

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Procedia CIRP 2021年 104卷 1855-1860页

作者： Yu-Yao Guo Lei Wang Ze-Lin Zhang Xu-H Xia Hui-Xian Zhu Key Laboratory of Metallurgical Equipment and Control Technology Ministry of Education Wuhan University of Science and Technology Wuhan 430081 China

Few existing methods can simultaneously confirm sequence shortest and operation time minimum of target parts disassembly for retired products. Aiming to filling this research gap, this paper proposes a cooperative co-evolution (CC) and data mining (DM) based method for planning disassembly sequence and estimating operation time, in which CC is used to search the shortest disassembly sequence (SDS). Modular Arrangement of Pre-determined Time Standard (MOD) is employed to estimate the operation time of SDS, then DM is used to further adjust this operation time. A real-world use case verified the effectiveness of the proposed method.

关键词： Remanufacturing Multi-objective disassembly sequence planning Disassembly time calculation cooperative coevolution algorithm Data mining

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