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World Summit on genetic and Evolutionary Computation (GEC 09)

作者： Agrawal, Shubham Bose, Sumit Kumar Sundarrajan, Srikanth Univ Texas Austin Dept Mech Engn Austin TX 78712 USA

ISBN: (纸本)9781605583266

The advent of virtualization technologies encourages organizations to undertake server consolidation exercises for improving the overall server utilization and for minimizing the capacity redundancy within data-centers. Identifying complimentary workload patterns is a key to the success of server consolidation exercises and for enabling multi-tenancy within data-centers. Existing works either do not consider incompatibility constraints or performs poorly on the disjointed conflict graphs. The algorithm proposed in the current work overcomes the limitations posed by the existing solutions. The current work models the server consolidation problem as a vector packing problem with conflicts (VPC) and tries to minimize the number of servers used for hosting applications within data-centers and maximizes the packing efficiency of the servers utilized. This paper solves the problem using techniques inspired from grouping genetic algorithm (GGA) - a variant of the traditional genetic algorithm (GA). The algorithm is tested over varying scenarios which show encouraging results.

关键词： Vector Packing Problem with Conflicts (VPC) Enterprise Grid Virtualization grouping genetic algorithm Incompatibility Constraints

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grouping genetic algorithm for the Capacitated p-median Problem

Grouping Genetic Algorithm for the Capacitated <i>p</i>-medi...

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International Conference on Information and Digital Technologies (IDT)

作者： Janosikova, Ludmila Vasilovsky, Patrik Univ Zilina Fac Management Sci & Informat Univ 1 Zilina 01026 Slovakia

ISBN: (纸本)9781509056897

The paper suggests an adaptation of a grouping genetic algorithm for solving the capacitated p-median problem. We propose a new encoding of the individual solutions that enables an efficient implementation of the crossover operation. A hybrid metaheuristic that combines the grouping genetic algorithm with the post-processing solver is proposed as well. Numerical experiments performed on benchmark instances proved the predominance of the grouping encoding over the standard encoding.

关键词： facility location capacitated p-median problem grouping genetic algorithm linear programming

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grouping genetic algorithm for Data Clustering

Grouping Genetic Algorithm for Data Clustering

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2nd Swarm, Evolutionary and Memetic Computing Conference (SEMCCO 2011)

作者： Peddi, Santhosh Singh, Alok Univ Hyderabad Dept Comp & Informat Sci Hyderabad 500046 Andhra Pradesh India

ISBN: (纸本)9783642271717

Clustering can be visualized as a grouping problem as it consists of identifying finite set of groups in a dataset. grouping genetic algorithms are specially designed to handle grouping problems. As the clustering criteria such as minimizing the with-in cluster distance is high-dimensional, non-linear and multi-modal, many standard algorithms available in the literature for clustering tend to converge to a locally optimal solution and/or have slow convergence. Even genetic guided clustering algorithms which are capable of identifying better quality solutions in general are also not totally immune to these shortcomings because of their ad hoc approach towards clustering invalidity and context insensitivity. To remove these shortcomings we have proposed a hybrid steady-state grouping genetic algorithm. Computational results show the effectiveness of our approach.

关键词： grouping genetic algorithm Data Clustering Heuristic

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grouping genetic algorithm with efficient data structures for the university course timetabling problem

Grouping genetic algorithm with efficient data structures fo...

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8th International Conference on the Practice and Theory of Automated Timetabling, PATAT 2010

作者： Santos, Felipe Arenales Delbem, Alexandre C. B. University of Sao Paulo Sao Carlos SP Brazil

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A multiple objective grouping genetic algorithm for assembly line design

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JOURNAL OF INTELLIGENT MANUFACTURING 2001年第5-6期12卷 467-485页

作者： Rekiek, B De Lit, P Pellichero, F L'Eglise, T Fouda, P Falkenauer, E Delchambre, A Free Univ Brussels CAD CAM Dept B-1050 Brussels Belgium Optimal Design B-1400 Nivelles Belgium Univ Catholique Louvain Dept Mech Engn B-1348 Louvain Belgium

The purpose of this paper is to describe some of the main problems concerning assembly line design. The focus will be on the following steps: (1) the input data preparation, (2) the elaboration of the logical layout of the line, which consists in the distribution of operations among stations along the line and an assignment of resources to the different stations, (3) finally the mapping phase using a simulation package to check the obtained results. This work presents a new method to tackle the hybrid assembly line design, dealing with multiple objectives. The goal is to minimize the total cost of the line by integrating design (station space, cost, etc.) and operation issues (cycle time, precedence constraints, availability, etc.). This paper also presents in detail a very promising approach to solve multiple objective problems. It is a multiple objective grouping genetic algorithm hybridized with the multicriteria decision-aid method PROMETHEE II. An approach to deal with user's preferences in design problems is also introduced. The essential concepts adopted by the method are described and its application to an industrial case study is presented.

关键词： assembly line design multiple objective problems grouping genetic algorithm multicriteria decision-aid

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Modular product design with grouping genetic algorithm - a case study

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COMPUTERS & INDUSTRIAL ENGINEERING 2004年第3期46卷 443-460页

作者： Kreng, VB Lee, TP Natl Cheng Kung Univ Sch Ind Management Tainan 701 Taiwan

Modular products are products that fulfill various functions through the combination of distinct modules. These detachable modules are constructed both according to the maximum physical and functional relations among components and maximizing the similarity of specifically modular driving forces. Accordingly, a non-linear programming is proposed to identify separable modules and simultaneously optimize the number of modules. This paper presents a systematic approach to accomplish modular product design in four major phases. Phase 1 is by means of functional and physical interaction analysis to format a component-to-component correlation matrix. Phase 2 is the exploration of design requirements to evaluate the relative importance of each modular driver. In phase 3, non-linear programming is used to formulate the objective function. In the final phase, a heuristic grouping genetic algorithm is adopted to search for the optimal or near-optimal modular architecture. This process and its application are illustrated by a real case of an electrical consumer product provided by an Original Design Manufacturer. The results demonstrate that the designer could direct a new approach to establish product modules according to the relative importance of modular drivers and the interaction among components. (C) 2004 Elsevier Ltd. All rights reserved.

关键词： modular product design non-linear programming grouping genetic algorithm

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Cooperative parallel grouping genetic algorithm for the one-dimensional bin packing problem

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COMPUTERS & INDUSTRIAL ENGINEERING 2018年 125卷 157-170页

作者： Kucukyilmaz, Tayfun Kiziloz, Hakan Ezgi TED Univ Ankara Turkey Univ Turkish Aeronaut Assoc Ankara Turkey

Evolutionary algorithms have been reported to be efficient metaheuristics for the optimization of several NP Hard combinatorial optimization problems. In addition to their ability to solve difficult and complex problems in reasonable execution times, parallelized versions of evolutionary algorithms are reported to explore and exploit the problem search space more effectively than their sequential counterparts. The Island Model, where the population of a given run is divided into semi isolated subpopulations, is a popular parallelization approach for evolutionary algorithms such as grouping genetic algorithms (GGA). Although the nature of GGAs is very suitable for coarse-grained parallel processing, designing an Island-parallel model for them is not a straightforward task. Selecting the communication topology, deciding migration and assimilation strategies, adjusting the migration rate and frequency, and using efficient diversification techniques are some of the important issues that needs to be covered in a successful Island-parallel Model. In this study, we propose a novel, scalable Island parallel GGA (IPGGA) for the well-known combinatorial optimization Problem 1D Bin-Packing (1DBPP). We provide a thorough experimental evaluation of the parallel model and report significant improvements on the Hard28 problem instances by outperforming the state-of-the-art genetic algorithms. Additionally, we analyze and evaluate the parallelization parameters of IPGGA with an emphasis on problem search-space diversity and report several interesting results.

关键词： 1D bin-packing grouping genetic algorithm Parallel evolutionary algorithms Search space diversity Migration topology Assimilation strategy

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A hybrid grouping genetic algorithm for the cell formation problem

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COMPUTERS & OPERATIONS RESEARCH 2007年第7期34卷 2059-2079页

作者： James, Tabitha L. Brown, Evelyn C. Keeling, Kellie B. Virginia Polytech Inst & State Univ RB Pamplin Coll Business Dept Business Informat Technol Blacksburg VA 24061 USA

The machine-part cell formation problem consists of constructing a set of machine cells and their corresponding product families with the objective of minimizing the inter-cell movement of the products while maximizing machine utilization. This paper presents a hybrid grouping genetic algorithm for the cell formation problem that combines a local search with a standard grouping genetic algorithm to form machine-part cells. Computational results using the grouping efficacy measure for a set of cell formation problems from the literature are presented. The hybrid grouping genetic algorithm is shown to outperform the standard grouping genetic algorithm by exceeding the solution quality on all test problems and by reducing the variability among the solutions found. The algorithm developed performs well on all test problems, exceeding or matching the solution quality of the results presented in previous literature for most problems. (c) 2005 Elsevier Ltd. All rights reserved.

关键词： machine-part cell formation grouping genetic algorithm heuristics

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A new grouping genetic algorithm approach to the multiple traveling salesperson problem

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SOFT COMPUTING 2009年第1期13卷 95-101页

作者： Singh, Alok Baghel, Anurag Singh Univ Hyderabad Dept Comp & Informat Sci Hyderabad 500046 Andhra Pradesh India Dept Elect Commun Jaipur 302001 Rajasthan India

The multiple traveling salesperson problem (MTSP) is an extension of the well known traveling salesperson problem (TSP). Given m > 1 salespersons and n > m cities to visit, the MTSP seeks a partition of cities into m groups as well as an ordering among cities in each group so that each group of cities is visited by exactly one salesperson in their specified order in such a way that each city is visited exactly once and sum of total distance traveled by all the salespersons is minimized. Apart from the objective of minimizing the total distance traveled by all the salespersons, we have also considered an alternate objective of minimizing the maximum distance traveled by any one salesperson, which is related with balancing the workload among salespersons. In this paper, we have proposed a new grouping genetic algorithm based approach for the MTSP and compared our results with other approaches available in the literature. Our approach outperformed the other approaches on both the objectives.

关键词： constrained optimization heuristic grouping genetic algorithm multiple traveling salesperson problem

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A grouping genetic algorithm for the microcell sectorization problem

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2004年第6期17卷 589-598页

作者： Brown, EC Vroblefski, M Virginia Tech Dept Business Informat Technol Blacksburg VA 24061 USA

The number of wireless users has steadily increased over the last decade, leading to the need for methods that efficiently use the limited bandwidth available. Reducing the size of the cells in a cellular network increases the rate of frequency reuse or channel reuse, thus increasing the network capacity. The drawback of this approach is increased costs associated with installation and coordination of the additional base stations. A code-division multiple-access network where the base stations are connected to the central station by fiber has been proposed to reduce the installation costs. To reduce the coordination costs and the number of handoffs, sectorization (grouping) of the cells is suggested. We propose a dynamic sectorization of the cells, depending on the current sectorization and the time-varying traffic. A grouping genetic algorithm is proposed to find a solution which minimizes costs. The computational results demonstrate the effectiveness of the algorithm across a wide range of problems. The GGA is shown to be a useful tool to efficiently allocate the limited number of channels available. (C) 2004 Elsevier Ltd. All rights reserved.

关键词： code-division multiple-access dynamic channel allocation genetic algorithm grouping genetic algorithm microcell sectorization wireless communication networks

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