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MILP Modeling and Optimization of Energy- Efficient Distributed Flexible Job Shop Scheduling Problem

IEEE ACCESS 2020年 8卷 191191-191203页

作者： Meng, Leilei Ren, Yaping Zhang, Biao Li, Jun-Qing Sang, Hongyan Zhang, Chaoyong Liaocheng Univ Sch Comp Sci Liaocheng 252059 Shandong Peoples R China Jinan Univ Sch Intelligent Syst Sci & Engn Zhuhai Campus Zhuhai 519070 Peoples R China Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Peoples R China

With the global warming problem and increasing energy cost, manufacturing firms are paying more and more attention to reducing energy consumption. This paper addresses the distributed flexible job shop scheduling problem (DFJSP) with minimizing energy consumption. To solve the problem, firstly, a novel mixed integer linear programming (MILP) model is developed to solve small-scaled problems to optimality. Due to the NP-hardness of DFJSP, we then propose an efficient hybrid shuffled frog-leaping algorithm (HSFLA) for solving DFJSP, particularly for large-sized problems. HSFLA combines the shuffled frog-leaping algorithm (SFLA) with powerful global search ability and variable neighborhood search (VNS) with good local search ability. Moreover, in HSFLA, the encoding method, the decoding method, the initialization method and the memetic evolution process are specifically designed. Finally, numerical experiments are conducted to evaluate the performance of the proposed MILP model and HFSLA.

关键词： Distributed flexible job shop scheduling problem mixed integer linear programming shuffled frog-leaping algorithm variable neighborhood search energy consumption

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Resources Allocation at the Physical Layer for Network Function Virtualization Deployment

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2020年第3期69卷 2771-2784页

作者： Xie, Ning Luo, Jianping Shenzhen Univ Coll Elect & Informat Engn Guangdong Key Lab Intelligent Informat Proc Shenzhen 518060 Peoples R China Shenzhen Univ Coll Elect & Informat Engn Shenzhen Key Lab Media Secur Shenzhen 518060 Peoples R China

Resource Allocation (RA) is one of the important factors in network function virtualization (NFV) deployment. As physical (PHY) layer resources are limited, e.g., transmitted energy and channel uses, the RA problem at the PHY layer for NFV deployment has become a fast-growing problem, especially for supporting ultra-reliable and low-latency communications (URLLC). Moreover, different nodes in NFV have different requirements for end-to-end communication, e.g., a controller has more stringent reliability requirements than does a logical node. There is a need for efficient and robust RA algorithms at the PHY layer for NFV deployment. To illustrate these challenges, we consider an up-link (UL) transmission protocol for NFV deployment, in which wireless transmissions with short packets are considered, and both the packet length and the transmission power are adjustable. Then, for three NFV deployment scenarios, we formulate three RA problems as three optimization problems to obtain the optimal parameters. Since these optimization problems are highly non-convex and they include excessive constraint conditions, the global optimal solutions are hard to obtain and are even infeasible for the conventional heuristic algorithms due to their low convergence efficiency. To address these problems, in this paper, the intelligent scheme of the modified shuffled frog-leaping algorithm (MSFLA) based on improved extremal optimization (EO) is applied to design RA algorithms. Three RA algorithms are designed for three NFV deployment scenarios to evaluate the quality of the solutions produced by the MSFLA-EO scheme. We perform simulations of three proposed RA algorithms in terms of various performance parameters. The experimental results are encouraging and demonstrate the efficiency of the proposed RA algorithms.

关键词： Network function virtualization resource allocation physical layer URLLC shuffled frog-leaping algorithm

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shuffled artificial bee colony algorithm

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SOFT COMPUTING 2017年第20期21卷 6085-6104页

作者： Sharma, Tarun Kumar Pant, Millie Amity Univ Rajasthan Jaipur Rajasthan India Indian Inst Technol Roorkee Roorkee Uttar Pradesh India

In this study, we have introduced a hybrid version of artificial bee colony (ABC) and shuffled frog-leaping algorithm (SFLA). The hybrid version is a two-phase modification process. In the first phase to increase the global convergence, the initial population is produced using randomly generated and chaotic system, and then in the second phase to balance two antagonist factors, i.e., exploration and exploitation capabilities, population is portioned into two groups (superior and inferior) based on their fitness values. ABC is applied to the first group, whereas SFLA is applied to the second group of population. The proposed version is named as shuffled-ABC. The proposal is implemented and tested on constrained benchmark consulted from CEC 2006 and five chemical engineering problems where constraints are handled using penalty function methods. The simulated results illustrate the efficacy of the proposal.

关键词： Computational intelligence Optimization Artificial bee colony shuffled frog-leaping algorithm Chemical engineering problems

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Delayed dynamic step shuffling frog-leaping algorithm for optimal design of photovoltaic models

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ENERGY REPORTS 2021年 7卷 228-246页

作者： Fan, Yi Wang, Pengjun Heidari, Ali Asghar Zhao, Xuehua Turabieh, Hamza Chen, Huiling Wenzhou Univ Coll Elect & Elect Engn Wenzhou 325035 Peoples R China Univ Tehran Coll Engn Sch Surveying & Geospatial Engn Tehran *** Iran Natl Univ Singapore Sch Comp Dept Comp Sci Singapore 117417 Singapore Shenzhen Inst Informat Technol Sch Digital Media Shenzhen 518172 Peoples R China Taif Univ Coll Comp & Informat Technol Dept Informat Technol POB 11099 At Taif 21944 Saudi Arabia Wenzhou Univ Coll Comp Sci & Artificial Intelligence Wenzhou 325035 Zhejiang Peoples R China

Developing an accurate mathematical model is an essential tool for studying and optimizing the performance of the photovoltaic cell system (PV). Transforming the PV problem into an optimization problem in which meta-heuristic algorithms excel provides an alternative approach to identifying the PV model's parameters. The memetic evolution mechanism and the shuffling strategy included in the shuffling frog-leaping algorithm (SFLA) provide safeguards for solving nonlinear, multimodal, and high-dimensional problems. However, the low convergence accuracy is an essential drawback of the SFLA algorithm to solve the PV problem. This paper proposes a delayed dynamic step mechanism based on the SFLA algorithm's characteristics to overcome this disadvantage, called the DDSFLA algorithm. The results of testing 23 benchmark functions and extracting the single diode model, the double-diode model, and the PV module show that the DDSFLA algorithm has a faster convergence speed and higher convergence accuracy and exhibits strong optimization stability under the special conditions of different temperatures or light intensities. The results suggest that the proposed algorithm can be used as an effective method to handle PV models' parameter extraction. The extra resources and online user guidance for this research will be provided at https://***. (C) 2020 The Author(s). Published by Elsevier Ltd.

关键词： Swarm-intelligence shuffled frog-leaping algorithm Delayed dynamic step mechanism PV models Parameter extraction

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Optimal power flow incorporating wind power and optimally allocated energy storage

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WIND ENGINEERING 2020年第3期44卷 227-238页

作者： Meyyappan, Ulagammai Saveetha Engn Coll Chennai 602105 Tamil Nadu India

The rapid growth of wind power poses new challenges for power system operators and electricity marketers. The benefits of using wind power are emission reduction and decrease in consumption of conventional resources. But the inherent nature of wind energy poses challenges in the power system operation and planning. In order to consider the wind power as firm power, auxiliary energy storage is added as a backup to encounter the variations in wind power. In this article, optimal power flow with wind and energy storage is developed. The energy storage systems are installed as a standby of wind generators to meet the demand consistently. The objective is to minimize the loss by optimal location and sizing of energy storage systems. With the optimally located energy storage systems, optimal power flow is carried out using shuffled frog-leaping algorithm technique and tested on IEEE 30 bus system.

关键词： Wavelet neural network shuffled frog-leaping algorithm optimal power flow energy storage systems wind speed forecasting

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Supervised meta-heuristic extreme learning machine for multiple sclerosis detection based on multiple feature descriptors in MR images

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SN APPLIED SCIENCES 2020年第5期2卷 1-19页

作者： Rezaee, Adele Rezaee, Khosro Haddadnia, Javad Gorji, Flamed Taheri Islamic Azad Univ Dept Biomed Engn Tabriz Branch Tabriz Iran Meybod Univ Dept Biomed Engn Meybod Yazd Iran Hakim Sabzevari Univ Dept Biomed Engn Sabzevar Iran Univ North Dakota Dept Elect Engn Grand Forks ND 58202 USA

In this study, we propose a hybrid approach involving feature extraction, feature selection, and optimized learning for the diagnosis of multiple sclerosis (MS), which can detect the lesion caused by MS plaques in the brain using magnetic resonance imaging analysis. A major challenge associated with lesion diagnosis by neurologists is that it is a time-consuming process and demands high expertise;therefore, researchers have been stimulated to find an auto-diagnose method of the disease. Given the high resemblance of MS plaque-induced lesions and other lesions such as Alzheimer's or dementia, scant research has explored the diagnosis of MS-induced lesions, most of which suffering from the lack of an efficient and accurate method. Informed by the need for a precise hybrid model for the classification of MS plaques and other comparable lesions, a solution is proposed that utilizes an efficient model. In this method, after image preprocessing, the feature vector is created by applying fractal and Pseudo-Zernike Moments descriptors. Feature selection using the Difference Evolution) algorithm to select the minimum subset of features will reduce the number of Extreme Learning Machine (ELM) inputs for classification. To improve the classification effect, the ELM wavelet kernel parameters are also regulated by the shuffled frog-leaping algorithm. By applying the proposed model to a set of brain MR images obtained from healthy subjects and MS patients during different experimental iterations, an average accuracy of 97% was obtained. The results of the method were estimated under specific conditions, and finally the proposed model yielded desirable outputs compared to similar methods.

关键词： Multiple sclerosis Extreme learning machine shuffled frog-leaping algorithm Differential evolution Fractal descriptor

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A Genetic-frog leaping algorithm for Large Dataset Document Clustering 16

A Genetic-Frog Leaping Algorithm for Large Dataset Document ...

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16th IEEE/ACS International Conference on Computer Systems and Applications (AICCSA)

作者： Alhenaki, Lubna Hosny, Manar King Saud Univ Comp Sci Dept Riyadh Saudi Arabia

ISBN: (纸本)9781728150529

This study describes the development of a text document clustering optimization model using a novel Genetic algorithm-shuffled frog leaping algorithm (GA-SFLA), which clusters text documents based on selected features in an efficient way. In the approach proposed in this study, a Genetic algorithm (GA) handles the feature selection task, while a shuffled frog-leaping algorithm (SFLA) handles the clustering task. The effectiveness of the proposed approach was evaluated by testing it on the popular "20Newsgroup" text document dataset. After multiple experiments, it was found that using GA-SFLA on the 20Newsgroup dataset considerably helped to enhance the text document clustering task, compared to classical K-means clustering. In addition, the feature selection stage greatly contributed to improving the results of clustering. Nevertheless, this improvement comes at the expense of longer computational time.

关键词： Text Documents Clustering shuffled frog-leaping algorithm Genetic algorithm Feature Selection

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Research on Flexible Flow Shop Scheduling Problem with Variable Processing Times 10

Research on Flexible Flow Shop Scheduling Problem with Varia...

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10th International Conference on Modelling, Identification and Control (ICMIC)

作者： Gao, Zhijun Jia, Meiqi Han, Zhonghua Zhu, Boqiu Shenyang Jianzhu Univ Fac Informat & Control Engn Shenyang 110168 Peoples R China Chinese Acad Sci Dept Digital Factory Shenyang Inst Automat Shenyang 110016 Peoples R China

ISBN: (纸本)9781538654163

In order to solve the flexible flow shop scheduling problem with variable processing times (FFSP-VPT), this paper analyzes the choice of processing stages with variable processing times, the quality inspection of unqualified products, and the occurrence of rework. As such, the FFSP-VPT mathematical model is established, adopting the fine scheduling process, which changes the traditional rough scheduling to guide the workshop production process. In addition, it not only provides the workshop with some adjustable stages, namely, the stages with variable processing times, but also searches for the balance between processing time and rework rate so as to ensure the processing quality while minimizing the maximum completion time. Based on the traditional shuffled frog-leaping algorithm(SFLA), we introduce the self-adaptive factor into the maximum update distance of SFLA algorithm, proposing a self-adaptive shuffled frog-leaping algorithm as the global optimization algorithm. What's more, through simulation test, the test result of the proposed algorithm is compared with those of other algorithms, which verifies the effectiveness of the SFLA algorithm in solving practical FFSP-VPT problems.

关键词： Flexible Flow Shop shuffled frog-leaping algorithm Variable Processing Times Self-Adaptive Maximum Update Distance

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Cooperative Task Assignment of Uninhabited Combat Air Vehicles Based on Improved MOSFLA algorithm 3

Cooperative Task Assignment of Uninhabited Combat Air Vehicl...

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3rd International Conference on Systems and Informatics (ICSAI)

作者： Wang, Shixiong He, Jianhua Huang, Yan Wang, Anlong Northwestern Polytech Univ Sch Elect & Informat Xian 710129 Peoples R China China Elect Technol Grp Corp Inst 20 Xian 710068 Peoples R China

ISBN: (纸本)9781509055210

The multiple uninhabited combat air vehicles (UCAV) cooperative task assignment problem is a multi-constrained combinatorial optimization problem. In order to solve this problem, a task assignment strategy of multi-objective shuffled frog-leaping algorithm (MOSFLA) based on matrix binary encoding is proposed. Firstly a multi-objective optimization model of task assignment is established using the target residual value in the three-dimensional space, the damage of UCAV and the voyage costs. Then the individual position update strategy of MOSFLA is optimized in order to ensure the feasibility of solutions and the global convergence capability. Finally, the strategy proposed in this article is applied in the air-to-ground attack mission assignment problem to obtain the Pareto optimal solutions. Simulation results show that the improved algorithm can solve the multi-UCAVs cooperative task assignment problem, and increase flexibility of decision making.

关键词： cooperative task assignment shuffled frog-leaping algorithm multi-objective matrix binary encoding Pareto optimal solutions

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Multi-population meta-heuristics for production scheduling: A survey

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SWARM AND EVOLUTIONARY COMPUTATION 2020年 58卷

作者： Lei, Deming Cai, Jingcao Wuhan Univ Technol Sch Automat Wuhan Peoples R China

Production scheduling is one of the most critical issues in manufacturing and production systems and has markedly positive impact on the performances of manufacturing. In the past decades, various scheduling problems have been extensively solved by multi-population meta-heuristics, which are artificial bee colony (ABC), imperialist competitive algorithm (ICA) and shuffled frog-leaping algorithm (SFLA);however, the related works of multi-population meta-heuristics to scheduling are hardly reviewed. In this paper, we provide an extensive recall on solving production scheduling based on ABC, ICA or SFLA. A new classification on scheduling problems is first given, then three algorithms are described and their applications to scheduling are summarized in a systematic way;finally, the main conclusions are drawn and some future research directions are presented.

关键词： Production scheduling Artificial bee colony Imperialist competitive algorithm shuffled frog-leaping algorithm

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