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Solving distributed two-stage hybrid flowshop scheduling using a shuffled frog-leaping algorithm with memeplex grouping

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ENGINEERING OPTIMIZATION 2020年第9期52卷 1461-1474页

作者： Lei, Deming Wang, Tian Wuhan Univ Technol Sch Automat Wuhan Hubei Peoples R China

Two-stage hybrid flowshop scheduling in a single factory has been considered fully;however, the distributed two-stage hybrid flowshop scheduling problem (DTHFSP) is seldom studied in a multi-factory environment. In this study, a DTHFSP with sequence-dependent setup times is investigated and a shuffled frog-leaping algorithm with memeplex grouping (MGSFLA) is proposed to minimize makespan and the number of tardy jobs. After an initial population is generated by an heuristic, two phases are executed sequentially with a new population division. In the second phase, all memeplexes are categorized into three groups, the different search processes are implemented in the different groups and the best memeplex is excluded from population division. A number of experiments are conducted on many instances and computational results validate the effectiveness of the main strategies and the promising advantages of an MGSFLA.

关键词： shuffled frog-leaping algorithm distributed scheduling two-stage hybrid flowshop scheduling problem sequence-dependent setup times

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A novel shuffled frog-leaping algorithm with reinforcement learning for distributed assembly hybrid flow shop scheduling

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2023年第4期61卷 1233-1251页

作者： Cai, Jingcao Lei, Deming Wang, Jing Wang, Lei Wuhan Univ Technol Sch Automat Wuhan Peoples R China Anhui Polytech Univ Sch Mech Engn Wuhu Peoples R China

Distributed hybrid flow shop scheduling (DHFS) problem has attracted much attention in recent years;however, DHFS with actual processing constraints like assembly is seldom considered and reinforcement learning is hardly embedded into meta-heuristic for DHFS. In this study, a distributed assembly hybrid flow shop scheduling (DAHFS) problem with fabrication, transportation and assembly is considered and a mathematic model is constructed. A new shuffled frog-learning algorithm with Q-learning (QSFLA) is proposed to minimise makespan. A three-string representation is used. A newly defined Q-learning process is embedded into QSFLA to select a search strategy dynamically for memeplex search. It is composed of four actions based on the combination of global search, neighbourhood search and solution acceptance rule, six states depicted by population evaluation on elite solution and diversity, and a newly defined reward function. A number of experiments are conducted. The computational results demonstrate that QSFLA can provide promising results on the considered DAHFS.

关键词： Scheduling hybrid flow shop assembly shuffled frog-leaping algorithm reinforcement learning

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An Adaptive shuffled frog-leaping algorithm for Hybrid-Flow Shop Scheduling with No Precedence Between Some Stages

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Complex System Modeling and Simulation 2024年第3期4卷 292-302页

作者： Zhenghui Yin Deming Lei Bo Yang School of Automation Wuhan University of TechnologyWuhan 430070China

Hybrid flow shop scheduling problem(HFSP)has been extensively considered,however,some reallife conditions are seldom *** this study,HFsP with no precedence between some stages is solved and an adaptive shuffled frog-leaping algorithm(ASFLA)is developed to optimize makespan.A new solution representation and a decoding procedure are presented,an adaptive memeplex search and dynamical population shuffling are implemented *** computational experiments are *** results prove that the new strategies of ASFLA are effective and ASFLA is very competitive in solving HFSP with no precedence between some stages.

关键词： hybrid-flow shop scheduling shuffled frog-leaping algorithm precedence

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Optimal inverse magnetorheological damper modeling using shuffled frog-leaping algorithm-based adaptive neuro-fuzzy inference system approach

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ADVANCES IN MECHANICAL ENGINEERING 2016年第8期8卷 1-18页

作者： Lin, Xiufang Chen, Shumei Fuzhou Univ Coll Mech Engn Fuzhou 350116 Peoples R China Fujian Agr & Forestry Univ Dept Informat & Mechatron Engn Jianshan Coll Fuzhou Peoples R China

Magnetorheological dampers have become prominent semi-active control devices for vibration mitigation of structures which are subjected to severe loads. However, the damping force cannot be controlled directly due to the inherent nonlinear characteristics of the magnetorheological dampers. Therefore, for fully exploiting the capabilities of the magnetorheological dampers, one of the challenging aspects is to develop an accurate inverse model which can appropriately predict the input voltage to control the damping force. In this article, a hybrid modeling strategy combining shuffled frogleaping algorithm and adaptive-network-based fuzzy inference system is proposed to model the inverse dynamic characteristics of the magnetorheological dampers for improving the modeling accuracy. The shuffled frog-leaping algorithm is employed to optimize the premise parameters of the adaptive-network-based fuzzy inference system while the consequent parameters are tuned by a least square estimation method, here known as shuffled frog-leaping algorithm-based adaptive-network-based fuzzy inference system approach. To evaluate the effectiveness of the proposed approach, the inverse modeling results based on the shuffled frog-leaping algorithm-based adaptive-network-based fuzzy inference system approach are compared with those based on the adaptive-network-based fuzzy inference system and genetic algorithm-based adaptive-network-based fuzzy inference system approaches. Analysis of variance test is carried out to statistically compare the performance of the proposed methods and the results demonstrate that the shuffled frogleaping algorithm-based adaptive-network-based fuzzy inference system strategy outperforms the other two methods in terms of modeling (training) accuracy and checking accuracy.

关键词： Magnetorheological damper inverse model adaptive-network-based fuzzy inference system shuffled frog-leaping algorithm genetic algorithm

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An Integration of Neural Network and shuffled frog-leaping algorithm for CNC Machining Monitoring

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FOUNDATIONS OF COMPUTING AND DECISION SCIENCES 2021年第1期46卷 27-42页

作者： Goli, Alireza Tirkolaee, Erfan Babaee Weber, Gerhard-Wilhelm Univ Isfahan Dept Ind Engn Esfahan Iran Istinye Univ Dept Ind Engn Istanbul Turkey Poznan Univ Tech Fac Engn Management Poznan Poland Middle East Tech Univ Inst Appl Math Ankara Turkey

This paper addresses Acoustic Emission (AE) from Computer Numerical Control (CNC) machining operations. Experimental measurements are performed on the CNC lathe sensors to provide the power consumption data. To this end, a hybrid methodology based on the integration of an Artificial Neural Network (ANN) and a shuffled frog-leaping algorithm (SFLA) is applied to the data resulting from these measurements for data fusion from the sensors which is called SFLA-ANN. The initial weights of ANN are selected using SFLA. The goal is to assess the potency of the signal periodic component among these sensors. The efficiency of the proposed SFLA-ANN method is analyzed compared to hybrid methodologies of Simulated Annealing (SA) algorithm and ANN (SA-ANN) and Genetic algorithm (GA) and ANN (GA-ANN).

关键词： Artificial Neural Network shuffled frog-leaping algorithm Simulated Annealing Genetic algorithm CNC machining Multi-sensor data fusion

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New mathematical model for software quality prediction of component-based software using shuffled frog-leaping algorithm

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INTERNATIONAL JOURNAL OF COMPUTER APPLICATIONS IN TECHNOLOGY 2017年第4期55卷 266-275页

作者： Panwar, Deepak Tomar, Pradeep Amity Univ Rajasthan Amity Sch Engn & Technol Jaipur 303002 Rajasthan India Gautam Buddha Univ Dept Comp Sci & Engn Sch Informat & Commun Technol Greater Noida 201308 India

Customer satisfaction and profit making are the two motives that define software quality;therefore, software industry uses new technologies like component-based software engineering, reengineering, etc., to make their software production more profitable. The proposed mathematical model is executed under ISO/IEC 9126 quality assurance model and justifies the definition of software quality given by IEEE 1061(1998). The model calculates the degree of stakeholder satisfaction (Q) by combining the quality attributes and it is validated using shuffled frog-leaping algorithm (SFLA) which improved the result by 2.46%.

关键词： software quality prediction ISO/IEC 9126 shuffled frog-leaping algorithm component-based software development

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The construction of a support vector machine using the shuffled frog-leaping algorithm

The construction of a support vector machine using the shuff...

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2014 International Conference on Computer Science and Systems Engineering（CSSE2014）

作者： M.H.Horng C.F.Chao H.Z.Chai Department of Computer Science and Information Engineering “National PingTung University”

ISBN: (纸本)9781784660543

The parameter setting of a support vector machine is a very important factor to its accuracy and efficiency. In this paper, we employ the shuffled frog-leaping algorithm(SFLA) to simultaneously train all parameters of the support vector machine including the penalty parameter, smoothness parameter and Lagrangian multiplier. The proposed method is called the SFLA-based support vector machine(SFLA-SVM). In experiments, binary and multi-class classifications are explored. In experiments, 10 of the benchmark data sets of UCI Machine Learning Repository are used. The classification performance of SFLA-SVM is compared to the original LIBSVM method associated with the grid search method and the PSO-SVM. The experimental results advocate that the use of SFLA-SVM for classifying the pattern classifications has better classification accuracy.

关键词： support vector machine shuffled frog-leaping algorithm UCI machine learning repository particle swarm optimization

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A shuffled frog-leaping algorithm with three memeplexes for hybrid flowshop scheduling problem with two batch processing machines stages

A shuffled frog-leaping algorithm with three memeplexes for ...

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第43届中国控制会议

作者： Deming Lei Shihan Wen School of Automation Wuhan University of Technology

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

Hybrid flowshop scheduling problem(HFSP) with batch processing machines(BPM) has attracted some attention by using various meta-heuristics. In this study, HFSP with two BPM stages is solved and a shuffled frog-leaping algorithm with three memeplexes(TMSFLA) is presented to minimize makespan and totardiness. In TMSFLA, the number of memeplexes is not used as parameter and fixed to be 3. A new way is given to divide population into three memeplexes. Lexicographical method is adopted in memeplex 2 for makespan and memeplex 3 for total tardiness. The differentiated search strategies are used in three memeplexes and reinforcement search is added. Extensive experiments are conducted. The effectiveness of new strategies and the search advantages are tested for TMSFLA in solving HFSP with two BPM stages.

关键词： Hybrid flowshop scheduling problem shuffled frog-leaping algorithm batch processing machines

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UAV Path Planning Based on shuffled frog-leaping algorithm and Dubins Path

UAV Path Planning Based on Shuffled Frog-Leaping Algorithm a...

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第三十九届中国控制会议

作者： Xinfang Li Yangwang Fang Wenxing Fu Northwestern Polytechnical University

In this paper, a novel path planning algorithm for the fixed-wing UAV based on shuffled frog-leaping algorithm（SFLA） and Dubins path is proposed. First, the Dubins path planning is transformed into a discrete combinatorial optimization problem. Then the SFLA is used to search the optimal Dubins path, so that the generated flight path directly satisfies the minimum turning radius constraint of UAV. In order to perfectly combine the SFLA and the Dubins path, a novel coding method and the worst frog update strategy are presented. Finally, the simulation results prove that the algorithm is effective for path planning of single UAV and multiple UAVs.

关键词： path planning Dubins path shuffled frog-leaping algorithm obstacle avoidance

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Identification of noise in multi noise plant using enhanced version of shuffled frog leaping algorithm

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INTERNATIONAL JOURNAL OF SYSTEM ASSURANCE ENGINEERING AND MANAGEMENT 2018年第1期9卷 43-51页

作者： Sharma, Tarun Kumar Pant, Millie Amity Univ Rajasthan Jaipur Rajasthan India IIT Roorkee Dept Appl Sci & Engn Roorkee Uttar Pradesh India

In any factory or industry the high level noise can be very harmful to the employees. As investigated by Occupational Safety and Health Act of 1970, the high level noise not only causes physiological ailments in employees but also causes harmful environment in the neighborhood. Therefore it becomes essential to control the noise levels in any manufacturing plant or industry. This can be achieved by optimal allocation of noise equipment which is quite not easy to recognize the exact location. In this study a shuffled frog-leaping algorithm (SFLA) with modification is applied to identify optimal locations for equipment in order to reduce noise level in multi noise plant. Comparatively, SFLA is a recent addition to the family of nontraditional population based search methods that mimics the social and natural behavior of species (frogs). SFLA merges the advantages of particle swarm optimization and genetic algorithm (GA). Though SFLA has been successfully applied to solve many benchmark and real time problems but it limits in convergence speed. In order to improve its performance, the frog with best position in each memeplexes is allowed to slightly modify its position using random walk. This process improves the local search around the best position. The proposal is named as improved local search in SFLA. The simulated results defend the efficacy of the proposal when compared with the differential evolution, GA and SFL algorithms.

关键词： shuffled frog-leaping algorithm SFLA Global optimization Noise identification

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