Discrete space location-allocation solutions from genetic algorithms

Hosage, Catherine Marya; Goodchild, M. F.

doi:10.1007/bf02027381

Cited by 119 publications

(52 citation statements)

References 1 publication

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“…Furthermore, to the best of our knowledge, a comprehensive study on the comparative performance of GAs on replicas placements models has not been attempted before. This is particularly significant in view of the fact that GAs have proven to be very effective on non-convex optimization problems for which it is relatively easy to assess the quality of a given feasible solution but difficult to systematically improve solutions by deterministic iterative methods [33][34]. Motivated by these considerations, this paper will present the GAbased heuristics for data sets replicas placements strategy from cost view.…”

Section: Ga-based Heuristics For Replicas Placements Strategy In Cloumentioning

confidence: 99%

Combination Replicas Placements Strategy for Data sets from Cost-effective View in the Cloud

Wu¹

2017

IJCIS

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In the cloud storage system, data sets replicas technology can efficiently enhance data availability and thereby increase the system reliability by replicating commonly used data sets in geographically different data centers. Most current approaches largely focus on system performance improvement by placing replicas for an independent data set, omitting the generation relationship among data sets. Furthermore, cost is an important element in deciding replicas number and their stored places, which can cause great financial burden for cloud clients because the cost for replicas storage and consistency maintenance may lead to high overhead with the number of new replicas increased in a pay-as-you-go paradigm. In this paper, we propose a combination strategy of real-replicas and pseudo-replicas (by computation from its provenance) from cost-effective view in order to achieve the minimum data set management cost, not only for the independent data sets but also for related data sets with generation relationships. We first define cost models that fit into the cloud computing paradigm, including data sets storage, computation and transfer costs, and then develop a new data set management cost model, helping to achieve a multi-criteria optimization of data set management. After that, a minimum cost benchmarking approach for the best trade-off between real-replicas and pseudo-replicas is proposed once decision to add a replica has been made. Then, a more practical and reasonable genetic algorithm as an alternative procedure for generating optimal or nearoptimal solution is given in order to identify the suitable replicas storage places. Finally, we present simulations setups and results that provide a first validation of our strategy. Both the theoretical analysis and simulations conducted on general (random) data sets as well as specific real world applications have shown efficiency and effectiveness of the improved system brought by the proposed strategy in cloud computing environment.

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Section: Ga-based Heuristics For Replicas Placements Strategy In Cloumentioning

confidence: 99%

Combination Replicas Placements Strategy for Data sets from Cost-effective View in the Cloud

Wu¹

2017

IJCIS

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“…Several genetic search heuristics have been suggested. Hosage and Goodchild (1986) encoded a solutions as a string of m binary digits (genes). In order to reach feasibility (p open facilities), the authors penalized the number of open facilities.…”

Section: Metaheuristicsmentioning

confidence: 99%

The p-median problem: A survey of metaheuristic approaches

Mladenović

Brimberg

Hansen

et al. 2007

European Journal of Operational Research

368

182

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Abstract. The p-median problem, like most location problems, is classified as N P -hard, and so, heuristic methods are usually used for solving it. The pmedian problem is a basic discrete location problem with real application that have been widely used to test heuristics. Metaheuristics are frameworks for building heuristics. In this survey, we examine the p-median, with the aim of providing an overview on advances in solving it using recent procedures based on metaheuristic rules.

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“…The approach presented here is in the spirit of the work of Hosage and Goodchild (1986) in that it attempts to use a GGA to select an optimal subset of locations from a discrete set of points, where the subset has a fixed size. Standard GA approaches generate strings consisting of 0's and 1's which may be used to divide the locations into 'used' and 'not used' categories.…”

Section: Introduction To Gas and Ggasmentioning

confidence: 99%

A modified grouping genetic algorithm to select ambulance site locations

Comber

Sasaki

Sano

et al. 2011

International Journal of Geographical Information Science

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This paper describes the development and application of a modified Grouping Genetic Algorithm (GGA) used to identify sets of optimal ambulance locations. The GGA was modified to consider a special case with only two groups, and the reproduction and mutation schemes were modified to operate more efficiently. It was applied to a case study locating ambulances from a fixed set of alternative locations. The sites were evaluated using data of emergency medical services (EMS) calls summarised over census areas and weighted by network distance. Census areas serviced by the same selected location defined ambulance catchments. The results indicated alternative sites for ambulances to be located, with average EMS response times improved by 1 minute 14 seconds and showed the impacts of having different numbers of ambulances in current locations and in new locations. The algorithmic developments associated with the modified GGA and the advantages of using census areas as spatial units to summarise data are discussed.

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Discrete space location-allocation solutions from genetic algorithms

Cited by 119 publications

References 1 publication

Combination Replicas Placements Strategy for Data sets from Cost-effective View in the Cloud

Combination Replicas Placements Strategy for Data sets from Cost-effective View in the Cloud

The p-median problem: A survey of metaheuristic approaches

A modified grouping genetic algorithm to select ambulance site locations

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