The Generate-and-Solve Framework Revisited: Generating by Simulated Annealing

Saraiva, Rommel Dias; Nepomuceno, Napoleão; Pinheiro, Plácido Rogério

doi:10.1007/978-3-642-37198-1_23

Cited by 5 publications

(6 citation statements)

References 19 publications

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“…Even though most existing applications of the GS framework are in the context of cutting, packing and loading problems-see, for example, [7,8,9,10,11]-other successful applications include the ones to configuration problems arising in wireless networks [12,13,14]. Moreover, it is interesting to note that the applications of GS published to date generate sub-instances in the GRI component using either evolutionary algorithms [10,14] or simulated annealing [11,13]. Finally, note that in [10] the authors introduced a so-called density control operator in order to control the size of the generated sub-instances.…”

Section: Related Workmentioning

confidence: 99%

Construct, Merge, Solve & Adapt A new general algorithm for combinatorial optimization

Blum

Pinacho

López-Ibáñez

et al. 2016

Computers & Operations Research

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This paper describes a general hybrid metaheuristic for combinatorial optimization labelled Construct, Merge, Solve & Adapt. The proposed algorithm is a specific instantiation of a framework known from the literature as Generate-And-Solve, which is based on the following general idea. First, generate a reduced sub-instance of the original problem instance, in a way such that a solution to the sub-instance is also a solution to the original problem instance. Second, apply an exact solver to the reduced sub-instance in order to obtain a (possibly) high quality solution to the original problem instance. And third, make use of the results of the exact solver as feedback for the next algorithm iteration. The minimum common string partition problem and the minimum covering arborescence problem are chosen as test cases in order to demonstrate the application of the proposed algorithm. The obtained results show that the algorithm is competitive with the exact solver for small to medium size problem instances, while it significantly outperforms the exact solver for larger problem instances.

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Section: Related Workmentioning

confidence: 99%

Construct, Merge, Solve & Adapt A new general algorithm for combinatorial optimization

Blum

Pinacho

López-Ibáñez

et al. 2016

Computers & Operations Research

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“…As future works, we investigate other applications in order to verify the efficiency of the methods on structured problems and extend the decomposition to nonlinear problems and integer nonlinear, using the Lagrangian heuristic process along with the regularization. It is expected that other hybrid methodologies [71][72][73][74] can be applied in the solution of the problem (P) [1].…”

Section: Discussionmentioning

confidence: 99%

A Hybrid Approach of Bundle and Benders Applied Large Mixed Linear Integer Problem

Pinheiro

Oliveira

2013

Journal of Applied Mathematics

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Consider a large mixed integer linear problem where structure of the constraint matrix is sparse, with independent blocks, and coupling constraints and variables. There is one of the groups of constraints to make difficult the application of Benders scheme decomposition. In this work, we propose the following algorithm; a Lagrangian relaxation is made on the mentioned set of constraints; we presented a process heuristic for the calculation of the multiplier through the resolution of the dual problem, structured starting from the bundle methods. According to the methodology proposed, for each iteration of the algorithm, we propose Benders decomposition where quotas are provided for the value function andε-subgradient.

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“…The use of insular genetic algorithms [25][26][27][28] can bring more diversity and possibilities, resulting in effectiveness enhancement. Moreover, other metaheuristics such as particle swarm could be experimented replacing or working cooperatively with genetic algorithms.…”

Section: Discussionmentioning

confidence: 99%

“…Another promising line of investigation involves the design and implementation of parallel/distributed versions of the framework, by means of which several GRI (Generator of reduced instances) and SRI (solver of reduced instances) instances could run concurrently, each one configured to explore different aspects of the optimization problem at hand. The use of insular genetic algorithms [25][26][27][28] can bring more diversity and possibilities, resulting in effectiveness enhancement. Moreover, other metaheuristics such as particle swarm could be experimented replacing or working cooperatively with genetic algorithms.…”

Section: Discussionmentioning

confidence: 99%

Handing Optimization Energy Consumption in Heterogeneous Wireless Sensor Networks

Pinheiro

Neto

Aguiar

2013

International Journal of Distributed Sensor Networks

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This paper presents an integer linear programming model devoted to optimize the energy consumption efficiency in heterogeneous wireless sensor networks. This model is based upon a schedule of sensor allocation plans in multiple time intervals subject to coverage and connectivity constraints. By turning off specifics sets of redundant sensors in each time interval, it is possible to reduce the total energy consumption in the network and, at the same time, avoid partitioning the whole network by losing some strategic sensors too prematurely. Since the network is heterogeneous, sensors can sense different phenomena from different demand points, with different sample rates. By resorting to this model, it is possible to provide extra lifetime to heterogeneous wireless sensor networks, reducing their setup and maintenance costs. This is an important issue to be considered when deploying sensor devices in hostile and inaccessible environments.

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The Generate-and-Solve Framework Revisited: Generating by Simulated Annealing

Cited by 5 publications

References 19 publications

Construct, Merge, Solve & Adapt A new general algorithm for combinatorial optimization

Construct, Merge, Solve & Adapt A new general algorithm for combinatorial optimization

A Hybrid Approach of Bundle and Benders Applied Large Mixed Linear Integer Problem

Handing Optimization Energy Consumption in Heterogeneous Wireless Sensor Networks

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