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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: a memetic meta-heuristic for discrete optimization

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ENGINEERING OPTIMIZATION 2006年第2期38卷 129-154页

作者： Eusuff, M Lansey, K Pasha, F Univ Arizona Dept Civil Engn & Engn Mech Tucson AZ 85721 USA Dept Water Resource Sacramento CA USA

A memetic meta-heuristic called the shuffled frog-leaping algorithm (SFLA) has been developed for solving combinatorial optimization problems. The SFLA is a population-based cooperative search metaphor inspired by natural memetics. The algorithm contains elements of local search and global information exchange. The SFLA consists of a set of interacting virtual population of frogs partitioned into different memeplexes. The virtual frogs act as hosts or carriers of memes where a meme is a unit of cultural evolution. The algorithm performs simultaneously an independent local search in each memeplex. The local search is completed using a particle swarm optimization-like method adapted for discrete problems but emphasizing a local search. To ensure global exploration, the virtual frogs are periodically shuffled and reorganized into new memplexes in a technique similar to that used in the shuffled complex evolution algorithm. In addition, to provide the opportunity for random generation of improved information, random virtual frogs are generated and substituted in the population. The algorithm has been tested on several test functions that present difficulties common to many global optimization problems. The effectiveness and suitability of this algorithm have also been demonstrated by applying it to a groundwater model calibration problem and a water distribution system design problem. Compared with a genetic algorithm, the experimental results in terms of the likelihood of convergence to a global optimal solution and the solution speed suggest that the SFLA can be an effective tool for solving combinatorial optimization problems.

关键词： combinatorial optimization meta-heuristic memetic algorithm memes genes shuffled frog-leaping algorithm

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shuffled frog-leaping algorithm using elite opposition-based learning

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INTERNATIONAL JOURNAL OF SENSOR NETWORKS 2014年第4期16卷 244-251页

作者： Zhao, Jia Lv, Li Nanchang Inst Technol Sch Informat Engn Nanchang 330099 Peoples R China

shuffled frog-leaping algorithm (SFLA) has been shown that it can yield good performance for solving various optimisation problems. However, it tends to suffer from premature convergent when solving complex problems. This paper presents an effective approach, called SFLA using elite opposition-based learning (EOLSFLA), which employs elite population to overcome the problems through adopting opposition-based learning to generate opposite solution, increasing the capability of neighbourhood search and enhancing the local exploitation ability. At the same time, to improve the learning ability of the worst population in a memeplex, a new frog-leaping rule is proposed, in which the worst population can learn from other population in the memeplex to reinforce the global space-exploration ability. Experiments are conducted on a set of well-known benchmark functions to verify the performance of EOLSFLA, comparing with other standard swarm intelligence algorithms, opposition-based learning algorithms and improved SFLA, the results demonstrate promising performance of the new method EOLSFLA on convergence velocity and precision.

关键词： SFLA shuffled frog-leaping algorithm EOLSFLA elite opposition-based learning frog-leaping rule

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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 new multiobjective fuzzy shuffled frog-leaping algorithm for optimal reconfiguration of radial distribution systems in the presence of reactive power compensators

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TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES 2013年第3期21卷 864-881页

作者： Dehnavi, Hossein Dehghan Esmaeili, Saeid Shahid Bahonar Univ Kerman Dept Elect Engn Kerman Iran

This paper presents a new approach for the optimal reconfiguration of radial distribution systems in the presence of reactive power compensators (RPCs). The proposed method is based on the simultaneous reconfiguration and RPC allocation for the mitigation of losses, equalizing the feeder load balancing as well as improving the voltage profile in power distribution networks. In this paper, the compensators are the capacitor bank and distribution static compensator, which is a type of distribution flexible alternating current transmission system device representative of conventional and modern RPCs. In this regard, the optimal states of the distribution system tie switches are determined taking into consideration the best size and location of the RPCs. In order to facilitate the algorithm for multiobjective search ability, the optimization problem is formulated for minimizing fuzzy performance indices. Since the optimization problem is nonlinear, using intelligent search methods such as the shuffled frog-leaping (SFL) algorithm can overcome the limitations of conventional analytic methods. In addition, to enhance the performance of the standard SFL, the fuzzy frog-leaping rule is used in this paper. This algorithm is more accurate and has an efficient convergence property compared to other intelligent search algorithms. The proposed method is validated using the IEEE 33-bus test system and a Tai-Power 11.4-kV distribution system as a real distribution network. The obtained results indicate that multiobjective simultaneous placement of RPCs along with reconfiguration can be more beneficial than separate single-objective optimization.

关键词： Optimal reconfiguration reactive power compensator network losses voltage profile load balancing shuffled frog-leaping algorithm fuzzy system

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An effective shuffled frog-leaping algorithm for solving the hybrid flow-shop scheduling problem with identical parallel machines

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ENGINEERING OPTIMIZATION 2013年第12期45卷 1409-1430页

作者： Xu, Ye Wang, Ling Wang, Shengyao Liu, Min Tsinghua Univ Dept Automat Tsinghua Natl Lab Informat Sci & Technol Beijing 100084 Peoples R China

In this article, an effective shuffled frog-leaping algorithm (SFLA) is proposed to solve the hybrid flow-shop scheduling problem with identical parallel machines (HFSP-IPM). First, some novel heuristic decoding rules for both job order decision and machine assignment are proposed. Then, three hybrid decoding schemes are designed to decode job order sequences to schedules. A special bi-level crossover and multiple local search operators are incorporated in the searching framework of the SFLA to enrich the memetic searching behaviour and to balance the exploration and exploitation capabilities. Meanwhile, some theoretical analysis for the local search operators is provided for guiding the local search. The parameter setting of the algorithm is also investigated based on the Taguchi method of design of experiments. Finally, numerical testing based on well-known benchmarks and comparisons with some existing algorithms are carried out to demonstrate the effectiveness of the proposed algorithm.

关键词： hybrid flow-shop scheduling problem identical parallel machines shuffled frog-leaping algorithm decoding method design of experiments

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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年第Jul.期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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