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An Adaptive Cooperated shuffled frog-leaping algorithm for Parallel Batch Processing Machines Scheduling in Fabric Dyeing Processes

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Computers, Materials & Continua 2025年第5期83卷 1771-1789页

作者： Lianqiang Wu Deming Lei Yutong Cai School of Automation Wuhan University of TechnologyWuhan430070China

Fabric dyeing is a critical production process in the clothing industry and heavily relies on batch processing machines(BPM).In this study,the parallel BPM scheduling problem with machine eligibility in fabric dyeing is considered,and an adaptive cooperated shuffled frog-leaping algorithm(ACSFLA)is proposed to minimize makespan and total tardiness *** determines the search times for each memeplex based on its quality,with more searches in high-quality *** adaptive cooperated and diversified search mechanism is applied,dynamically adjusting search strategies for each memeplex based on their dominance relationships and *** the cooperated search,ACSFLA uses a segmented and dynamic targeted search approach,while in non-cooperated scenarios,the search focuses on local search around superior solutions to improve ***,ACSFLA employs adaptive population division and partial population shuffling *** these strategies,memeplexes with low evolutionary potential are selected for reconstruction in the next generation,while thosewithhighevolutionarypotential are retained to continue their *** the performance of ACSFLA,comparative experiments were conducted using ACSFLA,SFLA,ASFLA,MOABC,and NSGA-CC in 90 *** computational results reveal that ACSFLA outperforms the other algorithms in 78 of the 90 test cases,highlighting its advantages in solving the parallel BPM scheduling problem with machine eligibility.

关键词： Batch processing machine parallel machine scheduling shuffled frog-leaping algorithm fabric dyeing process machine eligibility

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shuffled frog-leaping algorithm for parameter estimation of a double-cage asynchronous machine

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IET ELECTRIC POWER APPLICATIONS 2012年第8期6卷 484-490页

作者： Gomez-Gonzalez, M. Jurado, F. Perez, I. Univ Jaen Dept Elect Engn Jaen 23700 Spain Univ Jaen Dept Elect Engn Jaen 23071 Spain

This study introduces a shuffled frog-leaping algorithm based method for the estimation of induction motor double-cage model parameters from standard manufacturer data: full load torque, full load power factor, full load current, maximum torque, starting torque and starting current. The steady-state equivalent circuit is applied for the simulations. The circuit parameters are found as the result of the error minimisation function between the estimated and maker data. The suggested algorithm solves the parameter estimation problem and surpasses the solutions reached by particle swarm optimisation, genetic algorithms and classical parameter estimation method (a modified Newton method). The algorithm has been tested on three motors.

关键词： Optimisation techniques particle swarm optimisation maker data Power system management, operation and economics estimated data steady-state equivalent circuit modified Newton method full load power factor classical parameter estimation method equivalent circuits induction motor double-cage model parameters estimation electric current Newton method starting torque error minimisation function power factor induction motors double-cage asynchronous machine genetic algorithms torque shuffled frog-leaping algorithm starting current Asynchronous machines full load current standard manufacturer data: circuit parameters full load torque maximum torque parameter estimation

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shuffled frog-leaping algorithm for order acceptance and scheduling in flow shop 35

Shuffled frog-leaping algorithm for order acceptance and sch...

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35th Chinese Control Conference (CCC)

作者： Lei, Deming Tan, Xianfeng Wuhan Univ Technol Sch Automat Wuhan 430070 Peoples R China

ISBN: (纸本)9789881563910

Order acceptance and scheduling problem is considered in a flow shop where the objective is to decide on the orders to accept and schedule the accepted orders so as to maximize total net revenue. An effective shuffled frog-leaping algorithm (SFLA) is proposed, in which population is divided into memeplexes using tournament selection and the search process within each memeplex is done on the best solution of the memeplex. We assess the performance of SFLA using an extensive set of instances. The experimental results show that SFLA can provide the promising results on the considered problems.

关键词： shuffled frog-leaping algorithm flow shop scheduling order acceptance total net revenue

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Estimation of induction motor parameters using shuffled frog-leaping algorithm

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ELECTRICAL ENGINEERING 2013年第3期95卷 267-275页

作者： Perez, I. Gomez-Gonzalez, M. Jurado, F. Univ Jaen Dept Elect Engn EPS Jaen Jaen 23071 Spain Univ Jaen Dept Elect Engn EPS Linares Jaen 23700 Spain

This paper introduces a shuffled frog-leaping algorithm based method to approximate the equivalent circuit parameters of induction machines from the manufacturer data, such as nameplate data and motor performance characteristics. The steady-state equivalent circuit is applied for the simulations. The circuit parameters are found as the result for the error minimization function between the estimated and maker data. The suggested algorithm solves the parameter estimation problem and surpasses the solutions reached by differential evolution, particle swarm optimization and genetic algorithms. Therefore, this algorithm can be employed in motor energy management system for bettering the overall energy savings in industry.

关键词： Induction motor Parameters estimation shuffled frog-leaping algorithm Differential evolution Genetic algorithms Particle swarm optimization

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An improved shuffled frog-leaping algorithm to optimize component pick-and-place sequencing optimization problem

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EXPERT SYSTEMS WITH APPLICATIONS 2014年第15期41卷 6818-6829页

作者： Zhu, Guang-Yu Zhang, Wei-Bo Fuzhou Univ Sch Mech Engn & Automat Fuzhou 35002 Fujian Peoples R China

The component pick-and-place sequence is one of the key factors to affect the working efficiency of the surface mounting machine in the printed circuit board assembly. In this paper, an improved shuffled frog-leaping algorithm was presented by improving the basic shuffled frog-leaping algorithm (SFLA) with the strategy of letting all frogs taking part in memetic evolution and adding the self-variation behavior to the frog. The objective function of component pick-and-place sequence of the gantry multi-head component surface mounting machine was established. Parameters selection is critical for SFLA. In this study, Three-way ANOVA was used in parameters analyzing of the new improved SFLA. The parameters like memeplex numbers m, the frogs' number P and local evolution numbers i(part) were found having notable effects on the mounting time (time spent for components picking and placing), but the interactions among these parameters were not obvious. Multiple comparison procedures were adopted to determine the best parameter settings. In order to test the performance of the new algorithm, several experiments were carried out to compare the performance of improved SFLA with the basic SFLA and the genetic algorithm (GA) in solving the component pick-and-place sequence optimization problems. The experiment results indicate that improved SFLA can solve the optimization problem efficiently and outperforms SFLA and GA in terms of convergence accuracy, although more CPU time is undeniably needed. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Combinatorial optimization Component placement sequence optimization shuffled frog-leaping algorithm A shuffled frog-leaping algorithm with variation behavior

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A shuffled frog-leaping algorithm with Q-learning for unrelated parallel machine scheduling with additional resource and learning effect

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2023年第3期44卷 5357-5375页

作者： Yi, Tian Li, Mingbo Lei, Deming Wuhan Univ Technol Sch Automat Wuhan 430070 Peoples R China

Unrelated parallel machine scheduling problem (UPMSP) with additional resources and UPMSP with learning effect have attracted some attention;however, UPMSP with additional resources and learning effect is seldom studied and meta-heuristics for UPMSP hardly possess reinforcement learning as new optimization mechanism. In this study, a shuffled frog-leaping algorithm with Q-learning (QSFLA) is presented to solve UPMSP with one additional resource and learning effect. A new solution presentation is presented. Two populations are obtained by division. A Q-learning algorithm is constructed to dynamically decide search operator and search times. It has 12 states depicted by population quality evaluation, four actions defined as search operators, a new reward function and a new action selection. Extensive experiments are conducted. Computational results demonstrate that QSFLA has promising advantages for the considered UPMSP.

关键词： parallel machine scheduling additional resource learning effect shuffled frog-leaping algorithm reinforcement learning

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A new training strategy for neural network using shuffled frog-leaping algorithm and application to channel equalization

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AEU-INTERNATIONAL JOURNAL OF ELECTRONICS AND COMMUNICATIONS 2014年第11期68卷 1031-1036页

作者： Panda, Sunita Sarangi, Archana Panigrahi, Siba Prasada Kalam Inst Technol Berhampur Odisha India SOA Univ ITER Bhubaneswar Odisha India CV Raman Coll Engn Bhubaneswar Odisha India

This paper makes use of shuffled frog-leaping algorithm (SFLA) as a training algorithm to train multilayer artificial neural network (ANN). Next, The SFLA ANNs are used for channel equalization. We, in this paper, also introduce SFLA for channel equalization that is formulated as an optimization problem. In short, this paper introduces a novel strategy for training of ANN and also proposes two novel approaches for channel equalization problem using shuffled frog-leaping algorithm (SFLA). The proposed strategies are tested both in time-invariant and time varying channels and interestingly yield better performance than contemporary approaches as evidenced by simulation results. (C) 2014 Elsevier GmbH. All rights reserved.

关键词： Neural network shuffled frog-leaping algorithm Channel equalization

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Opposition based learning ingrained shuffled frog-leaping algorithm

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JOURNAL OF COMPUTATIONAL SCIENCE 2017年 21卷 307-315页

作者： Sharma, Tarun Kumar Pant, Millie Amity Univ Jaipur Rajasthan India IIT Dept Math Roorkee Uttar Pradesh India

shuffled frog-leaping algorithm (SFLA) is a kind of memetic algorithm. Randomicity and determinacy, the two keywords of SFLA ensures flexibility, robustness and exchange of information effectively in SFLA. In the basic structure of SFLA, the frogs are divided into memeplexes based on their fitness values where they forage for food. In this study the opposition based learning concept is embedded into the memeplexes before the frog initiates foraging. The proposal is investigated, analyzed and compared with latest variants of SFLA on benchmark functions (unimodal and multimodal) along with a real life problem. The result analysis shows that the proposed variant performs consistently well for different types of problems considered in this study. (C) 2017 Elsevier B.V. All rights reserved.

关键词： shuffled frog-leaping algorithm OBL Location management cost Paging cost Hand off cost

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Enhanced shuffled frog-leaping algorithm for solving numerical function optimization problems

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JOURNAL OF INTELLIGENT MANUFACTURING 2018年第5期29卷 1133-1153页

作者： Liu, Chao Niu, Peifeng Li, Guoqiang Ma, Yunpeng Zhang, Weiping Chen, Ke Yanshan Univ Key Lab Ind Comp Control Engn Hebei Prov Qinhuangdao 066004 Peoples R China Natl Engn Res Ctr Equipment & Technol Cold Strip Qinhuangdao 066004 Peoples R China Qinhuangdao Inst Technol Qinhuangdao 066100 Peoples R China

The shuffled frog-leaping algorithm (SFLA) is a relatively new meta-heuristic optimization algorithm that can be applied to a wide range of problems. After analyzing the weakness of traditional SFLA, this paper presents an enhanced shuffled frog-leaping algorithm (MS-SFLA) for solving numerical function optimization problems. As the first extension, a new population initialization scheme based on chaotic opposition-based learning is employed to speed up the global convergence. In addition, to maintain efficiently the balance between exploration and exploitation, an adaptive nonlinear inertia weight is introduced into the SFLA algorithm. Further, a perturbation operator strategy based on Gaussian mutation is designed for local evolutionary, so as to help the best frog to jump out of any possible local optima and/or to refine its accuracy. In order to illustrate the efficiency of the proposed method (MS-SFLA), 23 well-known numerical function optimization problems and 25 benchmark functions of CEC2005 are selected as testing functions. The experimental results show that the enhanced SFLA has a faster convergence speed and better search ability than other relevant methods for almost all functions.

关键词： shuffled frog-leaping algorithm Optimization Opposition-based learning Adaptive nonlinear inertia weight Perturbation operator strategy Gaussian mutation

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A shuffled frog-leaping algorithm for flexible job shop scheduling with the consideration of energy consumption

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2017年第11期55卷 3126-3140页

作者： Lei, Deming Zheng, Youlian Guo, Xiuping Wuhan Univ Technol Sch Automat Wuhan Peoples R China Hubei Univ Fac Comp Sci & Informat Engn Wuhan Peoples R China Southwest Jiaotong Univ Sch Econ & Management Chengdu Peoples R China

Flexible job shop scheduling problem (FJSP) has been extensively investigated and objectives are often related to time. Energy-related objective should be considered fully in FJSP with the advent of green manufacturing. In this study, FJSP with the minimisation of workload balance and total energy consumption is considered and the conflicting between two objectives is analysed. A shuffled frog-leaping algorithm (SFLA) is proposed based on a three-string coding approach. Population and a non-dominated set are used to construct memeplexes according to tournament selection and the search process of each memeplex is done on its non-dominated member. Extensive experiments are conducted to test the search performance of SFLA and computational results show the conflicting between two objectives of FJSP and the promising advantages of SFLA on the considered FJSP.

关键词： flexible job shop scheduling total energy consumption workload balance shuffled frog-leaping algorithm

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