Application of Genetic Algorithms to Packing Problems — A Review

Hopper, E. Mitchell; Turton, Brian

doi:10.1007/978-1-4471-0427-8_30

Cited by 23 publications

(13 citation statements)

References 15 publications

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“…However, those heuristics only focus on the minimization of the number of machines, and have not considered the various constraints in the placement. As a result, the solution based on traditional heuristics may be far from our desired optimum because it does not reflect the second objective (5). We need to design new heuristics for the multiple objectives optimization.…”

Section: Application Placementmentioning

confidence: 96%

“…After evaluating the objective function for each individual sample in P (g + 1), the algorithm generates the (g + 1)th population P (g + 1) by selecting the best samples from the union of P (g + 1) and the previous population P (g). It has been shown in [5] that by designing appropriate 'variation' and 'selection' operators, the iterative process in Figure 4 will converge to the optimal solution, i.e., the one that brings the lowest value for the function (6).…”

Section: Application Placementmentioning

confidence: 99%

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VCAE: A Virtualization and Consolidation Analysis Engine for Large Scale Data Centers

Chen¹,

Kang

Jiang³

et al. 2010

2010 Fourth IEEE International Conference on Self-Adaptive and Self-Organizing Systems

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Server consolidation through virtualization is becoming an effective way to save power and space in enterprise data centers. However, it also brings additional operational risks for the consolidated system because the impacts of hardware failures, human errors, and security breaches can be vastly magnified in that densely packed environment. In order to mitigate the above issues, this paper proposes a new virtualization and consolidation analysis engine(VCAE), which exploits and utilizes various constraints in the consolidation process. VCAE provides a comprehensive framework to discover, represent, check, and combine various constraints in server consolidation. It can assist system operators to effectively deal with the large number of constraints in the consolidation planning. In addition, VCAE proposes an evolution based method to discover the optimal consolidation scheme under multiple constraints. As a consequence, the consolidation solution generated by VCAE can not only maximize the utilization of system resources but also keep the hidden risks as low as possible in the consolidated system. The experimental results from an real enterprise system have demonstrated the advantages of our analysis engine.

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Section: Application Placementmentioning

confidence: 96%

Section: Application Placementmentioning

confidence: 99%

VCAE: A Virtualization and Consolidation Analysis Engine for Large Scale Data Centers

Chen¹,

Kang

Jiang³

et al. 2010

2010 Fourth IEEE International Conference on Self-Adaptive and Self-Organizing Systems

View full text Add to dashboard Cite

show abstract

“…There were also several approaches by incorporating the bound information of the branches of search trees into GAs [40]. Hopper and Turton [41] had reviewed applications of GAs to packing problems. They also conducted an empirical study of meta-heuristic and heuristic algorithms for 2D packing problems [42].…”

Section: Cutting and Packing Problemsmentioning

confidence: 99%

Moving Block Sequence and Organizational Evolutionary Algorithm for General Floorplanning With Arbitrarily Shaped Rectilinear Blocks

Jing

Zhong

Jiao

et al. 2008

IEEE Trans. Evol. Computat.

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Abstract⎯A new nonslicing floorplan representation, the moving block sequence (MBS), is proposed in this paper. Our idea of the MBS originates from the observation that placing blocks on a chip has some similarities to playing the game, Tetris ® . Because no extra constraints are exerted on solution spaces, the MBS is not only useful for evolutionary algorithms, but also for dealing with rectangular, convex rectilinear, and concave rectilinear blocks, similarly and simultaneously, without partitioning rectilinear blocks into sub-blocks. This is owed to a special structure designed for recording the information of both convex and concave rectilinear blocks To test the performance of the MBS-OEA, benchmarks with hard rectangular, soft rectangular, and hard rectilinear blocks are used. The number of blocks in these benchmarks varies from 9 to 2 300. Also, the MBS-OEA and several well-designed existing algorithms are compared. The results show that the MBS-OEA can find high quality solutions for various problems.Additionally, the MBS-OEA shows a good performance in solving the problems with 300 hard rectangular blocks, 100 soft rectangular blocks, and 100 hybrid blocks, including both soft rectangular and hard rectilinear blocks. This illustrates that the MBS-OEA is not only suitable for solving a wide range of problems, but also competent for solving large-scale problems.Finally, a set of specific experiments is designed to identify the key component that is mainly responsible for the good performance of the MBS-OEA.

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“…The scale of the community is between 30 and 200. An inadequate scale of the community will influence on comprehensive performance of genetic algorithm [17] .…”

Section: Initialization Populationmentioning

confidence: 99%

Application of genetic algorithm for rectangular object layout optimization

Zhao

et al. 2008

2008 12th International Conference on Computer Supported Cooperative Work in Design

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Rectangular Object Layout is not a simple NP (nondeterministic polynomial) optimization problem because of the practical production rules, such as guillotine. Under some situations, the guillotine is even more important than the pure using ratio of metal sheets. To improve the using ratio of stock sheets and to reduce the production costs, the genetic algorithm is adopted to optimize rectangular object layout on stock sheets. The purpose of this paper is to construct the model of genetic algorithm and design the genetic operators. Combined with the lowest horizontal linesearch algorithm, Genetic Algorithm model is applied into rectangular object layout optimization. Results show that the model in the paper can satisfy not only the practical production requirements of guillotine, but also the requirement for production convenience. In this way, user can get optimal layout results effectively and quickly and a higher material using ratio at the same time for normal production practice.

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Application of Genetic Algorithms to Packing Problems — A Review

Cited by 23 publications

References 15 publications

VCAE: A Virtualization and Consolidation Analysis Engine for Large Scale Data Centers

VCAE: A Virtualization and Consolidation Analysis Engine for Large Scale Data Centers

Moving Block Sequence and Organizational Evolutionary Algorithm for General Floorplanning With Arbitrarily Shaped Rectilinear Blocks

Application of genetic algorithm for rectangular object layout optimization

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