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2015 IEEE 17th International Conference on High Performance Computing and Communications (HPCC)

作者： Wu, Quanwang Ishikawa, Fuyuki Natl Inst Informat Tokyo Japan

ISBN: (纸本)9781479989379

Virtual machine (VM) consolidation is a promising approach for improving energy efficiency of the datacenter by increasing the resource utilization of physical machines. However, the live migration technology that VM consolidation relies on is costly in itself, and this migration cost is usually heterogeneous as well as the datacenter. This paper focuses on how to pay limited migration costs to save as much energy as possible via VM consolidation in a heterogeneous cloud environment. That is, how to minimize the energy consumption while keeping most of the VMs in the datacenter unmoved. To capture these two conflicting objectives, a migration cost estimation method is first proposed and then a consolidation score function is defined for overall evaluation. To maximize the consolidation score, an improved grouping genetic algorithm (IGGA) based on a greedy heuristic and a swap operation is proposed for VM consolidation. Experiments show that IGGA performs better than existing consolidation methods.

关键词： virtual machine consolidation energy migration cost heterogeneity grouping genetic algorithm

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Using grouping genetic algorithm to Mine Diverse Group Stock Portfolio

Using Grouping Genetic Algorithm to Mine Diverse Group Stock...

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IEEE Congress on Evolutionary Computation (CEC) held as part of IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： Chen, Chun-Hao Lu, Cheng-Yu Hong, Tzung-Pei Su, Ja-Hwung Tamkang Univ Dept Comp Sci & Informat Engn Taipei Taiwan Natl Univ Kaohsiung Dept Comp Sci & Informat Engn Kaohsiung Taiwan Natl Sun Yat Sen Univ Dept Comp Sci & Engn Kaohsiung 80424 Taiwan Cheng Shiu Univ Dept Informat Management Kaohsiung Taiwan Kainan Univ Dept Informat Management Taoyuan Taiwan

ISBN: (纸本)9781509006229

In this paper, to increase the diversity of stock portfolios, the diverse group stock portfolio mining algorithm is proposed based on the grouping genetic algorithm. Each chromosome is represented by grouping part, stock part and stock portfolio part. The fitness function that consists of portfolio satisfaction, group balance and diversity factor is designed to evaluate quality of chromosomes. The diversity factor is used to make the numbers of stock categories in groups as similar as possible. The genetic operations are then executed on population to generate offspring for finding a near-optimal group stock portfolio. Finally, experiments on a real financial data were made to show the effectiveness of the proposed approach.

关键词： grouping genetic algorithm group stock portfolio maximally diverse grouping problem portfolio optimization

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Hybrid grouping genetic algorithm for Large-Scale Two-Level Resource Allocation of Containers in the Cloud 34th

Hybrid Grouping Genetic Algorithm for Large-Scale Two-Level ...

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34th Australasian Joint Conference on Artificial Intelligence (AI)

作者： Akindele, Taiwo Tan, Boxiong Mei, Yi Ma, Hui Victoria Univ Wellington Wellington New Zealand

ISBN: (纸本)9783030975463;9783030975456

Cloud container resource allocation aims to find container placements in cloud Virtual Machines (VM) and Physical Machines (PM) such that overall energy consumption is minimised A resource allocation architecture where application containers are consolidated into cloud VMs in a container-VM-PM model is common practise in data centers. The VM layer may provide additional administrative or security features, but adds complexity to the optimization problem when deploying containers initially on a large scale. Research addressing this two-level resource allocation is limited, some of the recent work try to optimise consolidation of containers to VM layer separately from consolidation of VMs to PMs, which results in large portions of the search space remaining unexplored. A grouping genetic algorithm (GGA) framework that can simultaneously optimize consolidation on both levels is promising. However, for large instances of the two-level optimisation, it may suffer from premature convergence and limited population diversity. In this work, we propose a new fixed-length crossover operator that is designed to improve population diversity and exploration in GGA for container resource allocation optimisation. We also propose problem-specific Best-Fit and Largest VM heuristic operators to aid local search by rearranging containers from the lower fitness PMs at the chromosome tail into existing VMs and PMs with better utilization when possible. We demonstrate that with the newly developed operators, the proposed GGA can significantly reduce energy consumption in large-scale test cases.

关键词： grouping genetic algorithm Container resource allocation Energy consumption optimisation

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A grouping genetic algorithm FOR THE ASSEMBLY LINE BALANCING PROBLEM OF SEWING LINES IN GARMENT INDUSTRY

A GROUPING GENETIC ALGORITHM FOR THE ASSEMBLY LINE BALANCING...

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International Conference on Machine Learning and Cybernetics

作者： Chen, James C. Hsaio, Mabel H. Chen, Chun-Cheh Sun, Cheng-Ju Chung Yuan Christian Univ Dept Ind & Syst Engn Chungli 320 Taiwan

ISBN: (纸本)9781424447053

The garment manufacturing is a traditional and fashion industry, that is globally competitive and customer centric. The most critical operation process is sewing, as it generally involves a great number of operations. The aim of assembly line balancing planning in sewing lines is to assign task to the workstation in order that the machines of the workstation can perform the assigned tasks with a balanced loading. Assembly line balancing problem (ALBP) is known as an NP-hard problem. Thus, the heuristic methodology could be a better way to plan the sewing lines in a reasonable time. This paper presents a grouping genetic algorithm (GGA) for assembly line balancing problem of sewing lines in garment industry. GGA was first developed by Falkenauer in 1992 as a type of GA which exploits the special structure of grouping problem, and overcomes the drawbacks of GA. GGA allocates workload among machines as evenly as possible, so the minimum mean absolute deviations (MAD) can be minimized. The performance is verified through solving two real problems in garment industry. The computational results reveal that GGA outperforms GA in both simple and complex problems by 13.81% and 8.81%, respectively. This shows GGA's effectiveness in solving ALBP.

关键词： Assembly Line Balancing Problem grouping genetic algorithm Garment Industry

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A grouping genetic algorithm Based on the GES Local Search for Pickup and Delivery Problem with Time Windows and LIFO Loading 14th

A Grouping Genetic Algorithm Based on the GES Local Search f...

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14th International Conference on Intelligent Computing (ICIC)

作者： Zhang, Feng Li, Bin Qian, Kun Univ Sci & Technol China Sch Informat Sci & Technol Hefei 230027 Anhui Peoples R China

ISBN: (纸本)9783319959320;9783319959337

This paper investigates the pickup and delivery problem with time windows and last-in-first-out (LIFO) loading (PDPTWL). In this problem, the compartment of a vehicle is modeled as a linear LIFO stack. The last picked up goods are placed on the top of the stack, and the goods can be delivered only when they are on the top of the stack. The LIFO constraint makes the feasible solution space more tightly constrained and the design of an effective algorithm more difficult. A grouping genetic algorithm combined with the guided ejection search is proposed to solve the PDPTWL problem of large-size, in which, an evaluation function is defined to guide the selection of genes for crossover and mutation, and a local search based on the guided ejection search is embedded into the genetic algorithm to improve the quality of the solutions. Then, a population-based metaheuristic is ready for the PDPTWL problem. It can solve instances with 50-300 requests in the Li and Lim's benchmarks. Compared with the existing state-of-the-art algorithms, the experimental results confirm that the proposed algorithm works more efficiently. It improves 164 best-known solutions out of 236 instances and reduces 424 vehicles.

关键词： Vehicle routing problem with pickup and delivery Last-in-first-out loading grouping genetic algorithm Guided ejection search

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Improving grouping genetic algorithm for Virtual Machine Placement in Cloud Data Centers 4

Improving Grouping Genetic Algorithm for Virtual Machine Pla...

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4th International Conference on Computer and Knowledge Engineering (ICCKE)

作者： Jamali, Shahram Malektaji, Sepideh Univ Moliagliegh Ardebili Dept Elect & Comp Engn Ardebil Iran

ISBN: (纸本)9781479954865

cloud computing the newly emerged service oriented paradigm, has changed IT industry significantly. Virtualization is the main technique to empower cloud computing by separating compute environments from the actual physical infrastructure and creating virtual machines (VMs). Mapping of these virtual machines to the physical servers is called virtual machine placement problem and known to be NP hard. On the other hand, the grouping genetic algorithm which generally used for this problem does not perform efficiently in many cases. In the current work, we improve this algorithm by introducing a unique and efficient method for encoding and generating new solutions. Using vector packing problem, we model the problem of virtual machine placement and try to reduce power consumption by minimizing the number of used servers and also maximizing resource usage efficiency. The algorithm is tested over varying VM placement scenarios which show encouraging results.

关键词： cloud computing virtualization grouping genetic algorithm virtual machine placement

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Multi-Strategy grouping genetic algorithm for the Pickup and Delivery Problem with Time Windows 09

Multi-Strategy Grouping Genetic Algorithm for the Pickup and...

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World Summit on genetic and Evolutionary Computation (GEC 09)

作者： Ding Genhong Li Linye Ju Yao Hohai Univ Nanjing 210098 Peoples R China

ISBN: (纸本)9781605583266

The Pickup and Delivery Problem with Time Windows (PDPTW) is a generalization of the well studied Vehicle Routing Problem with Time Windows (VRPTW). This paper studies a grouping genetic algorithm for solving the PDPTW. The insertion-searching heuristics (in GGA) which can generate feasible solutions was improved, new data structures were built, and then three routing adjustment strategies were added to come up with the Multi-Strategy grouping genetic algorithm (MSGGA). The PDPTW benchmark problems with 100 customers are calculated with MSGGA, and the comparison between the result and that of the reference shows that the new algorithm shortens the calculating time with its astringency, better solutions of four cases are obtained and stability is improved.

关键词： Vehicle Routing Problem Pickup and Delivery Problem with Time Windows genetic algorithm grouping genetic algorithm Multi-Strategy grouping genetic algorithm

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A Hybrid grouping genetic algorithm for Multiprocessor Scheduling

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2nd International Conference on Contemporary Computing

作者： Singh, Alok Sevaux, Marc Rossi, Andre Univ Hyderabad Sch Math & Comp Informat Sci Dept Comp & Informat Sci Hyderabad 500046 Andhra Pradesh India Univ Bretagne Sud UEB Lab STICC Ctr Recherche F-56321 Lorient France

ISBN: (纸本)9783642035463

This paper describes a hybrid grouping genetic algorithm for a multiprocessor scheduling problem, where a list of tasks has to be scheduled oil identical parallel processors. Each task in the list is defined by a release date, a due date and a processing time. The objective is to minimize the number of processors used while respecting the constraints imposed by release dates and due dates. We have compared Our hybrid approach with two heuristic methods reported in the literature. Computational results show the Superiority Of Our hybrid approach over these two approaches. Our hybrid approach obtained better quality Solutions in shorter time.

关键词： Combinatorial optimization grouping genetic algorithm heuristic multiprocessor scheduling

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An Effective Approach for the Diverse Group Stock Portfolio Optimization Using grouping genetic algorithm

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IEEE ACCESS 2019年 7卷 155871-155884页

作者： Chen, Chun-Hao Lu, Cheng-Yu Hong, Tzung-Pei Lin, Jerry Chun-Wei Gaeta, Matteo Tamkang Univ Dept Comp Sci & Informat Engn Taipei 251 Taiwan Natl Univ Kaohsiung Dept Comp Sci & Informat Engn Kaohsiung 811 Taiwan Natl Sun Yat Sen Univ Dept Comp Sci & Engn Kaohsiung 804 Taiwan Western Norway Univ Appl Sci Dept Comp Math & Phys N-5063 Bergen Norway Univ Salerno Dipartimento Ingn Informaz & Elettr Matemat Appli I-84084 Fisciano Italy

Finding useful portfolios that could be a portfolio of trading strategy or a stock portfolio from financial datasets is always an attractive research topic due to the nature of financial markets. Because investors always want an approach that can continually provide various portfolios, the issue of group stock portfolio optimization (GSPO) has been raised and the algorithms to obtain a group stock portfolio (GSP) have also been described in the past. A GSP divides the whole set of stocks into several stock groups, and the stocks in a group are exchangeable in investment. Thus, when investors are not satisfied with a suggested stock, they can select another stock from the same group to replace the original one. However, the industry diversity of stocks within a group is not regarded in the existing literatures. In this paper, an algorithm for dealing with the diverse group stock portfolio optimization (DGSPO) is proposed to obtain a diverse group stock portfolio (DGSP). The proposed algorithm is based on the group genetic algorithm with the chromosome representation and the fitness function designed for the purpose of finding a good DGSP. Especially, a factor called group diversity is designed to diversify stocks from different industries and is considered in the fitness evaluation. Another factor considers cash dividend is also applied to keep companies with good quality in the portfolio for increasing the profit of a DGSP. Two real financial datasets are used in the experiments to verify the effectiveness of the proposed approach.

关键词： Portfolios Optimization genetic algorithms Biological cells Industries Automobiles Investment Stock portfolio group stock portfolio grouping genetic algorithm grouping problem group diversity

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A grouping genetic algorithm FOR THE ASSEMBLY LINE BALANCING PROBLEM OF SEWING LINES IN GARMENT INDUSTRY

A GROUPING GENETIC ALGORITHM FOR THE ASSEMBLY LINE BALANCING...

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2009 International Conference on Machine Learning and Cybernetics(2009机器学习与控制论国际会议)

作者： JAMES C.CHEN MABEL H.HSAIO CHUN-CHIEH CHEN CHENG-JU SUN Department of Industrial and Systems Engineering Chung Yuan Christian University Chung Li Taiwan 320 R.O.C

The garment manufacturing is a traditional and fashion industry, that is globally competitive and customer centric. The most critical operation process is sewing, as it generally involves a great number of operations. The aim of assembly line balancing planning in sewing lines is to assign task to the workstation in order that the machines of the workstation can perform the assigned tasks with a balanced loading. Assembly line balancing problem (ALBP) is known as an NP-hard problem. Thus, the heuristic methodology could be a better way to plan the sewing lines in a reasonable *** paper presents a grouping genetic algorithm (GGA) for assembly line balancing problem of sewing lines in garment industry. GGA was first developed by Falkenauer in 1992 as a type of GA which exploits the special structure of grouping problem, and overcomes the drawbacks of GA. GGA allocates workload among machines as evenly as possible, so the minimum mean absolute deviations (MAD) can be minimized. The performance is verified through solving two real problems in garment industry. The computational results reveal that GGA outperforms GA in both simple and complex problems by 13.81％ and 8.81％, respectively. This shows GGA's effectiveness in solving ALBP.

关键词： Assembly Line Balancing Problem grouping genetic algorithm Garment Industry

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