Using differential evolution for combinatorial optimization: A general approach

Prado, Ricardo S.; Silva, Rodrigo C. P.; Guimarães, Frederico Gadelha; Neto, Oriane M.

doi:10.1109/icsmc.2010.5642193

Cited by 37 publications

(20 citation statements)

References 11 publications

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“…In [5], it is suggested to set the value of in order to control the size of by using one of the strategies proposed in [10]. However, in the experimentations reported in the next section, is set to , which means that it has no impact on the size of , whatever the strategy chosen.…”

Section: B Set-based De For Combinatorial Optimizationmentioning

confidence: 99%

Parallel Preprocessing for the Optimal Camera Placement Problem

Brevilliers¹,

Lepagnot²,

Kritter³

et al. 2018

IJMO

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Abstract-This paper deals with the preprocessing needed for the optimal camera placement problem, which is stated as a unicost set covering problem (USCP). Distributed and massively parallel computations with graphics processing unit (GPU) are proposed in order to perform the reduction and visibility preprocessing respectively. An experimental study reports that a significant speedup can be achieved, and we give a general heterogeneous parallel approach that brings together these parallel computations. In addition to that, a set-based differential evolution (DE) method is applied to solve 10 instances of the considered problem, and promising results are reported.Index Terms-Distributed computing, graphics processing unit (GPU), optimal camera placement problem, preprocessing, set-based differential evolution (DE) algorithm, unicost set covering problem (USCP). I. INTRODUCTIONIntelligent video surveillance systems aim at monitoring areas of interest by using appropriate networks of cameras: the placement of these cameras is thus of great importance because of the requested quality of service and the deployment costs. Such an optimal camera placement problem can be stated as a decision problem in a discrete search space [1]: given the set of possible camera locations, find the optimal subset that can meet the operational requirements. In this work, the problem is modelled as a unicost set covering problem (USCP) together with a three-dimensional discretization of the monitored area.Firstly, the optimization process needs the following input data: the list of points to be covered, the list of possible camera locations, and the lists of points covered by each possible camera location. This so-called visibility preprocessing is performed according to the practical context, where the 3D setting avoids blind spot (with regard to a 2D model), but at the cost of a much larger computational effort [2]. For this reason, it is of high interest to design parallel approaches that can compute these input data within a reasonable time, so that larger problems can be optimized.Secondly, these input data can be reduced in order to speed up the optimizing process. The main reduction strategy is a variant of the so-called column domination test for non-unicost set covering problems [3] (sets that cover fewer points at the same cost can be removed, instead of those that cover the same points at a higher cost). It consists in Manuscript received October 27, 2017; revised December 12, 2017. This work was supported by the French Agence Nationale de la Recherche (ANR) as part of the OPMoPS project .The authors are with the LMIA Research Laboratory, Université de Haute-Alsace, Mulhouse, France (e-mail: mathieu.brevilliers@uha.fr, julien.lepagnot@uha.fr, julien.kritter@uha.fr, lhassane.idoumghar@uha.fr).discarding the so-called dominated camera locations: for each possible camera location , if another one can cover the same points as and some additional points, then is said to be dominated by and it can be removed from the set of availabl...

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Section: B Set-based De For Combinatorial Optimizationmentioning

confidence: 99%

Parallel Preprocessing for the Optimal Camera Placement Problem

Brevilliers¹,

Lepagnot²,

Kritter³

et al. 2018

IJMO

View full text Add to dashboard Cite

show abstract

“…Since then efforts have been made to adapt the original algorithm so that it can be applied to discrete (combinatorial) problems (Onwubolu, 2009;Prado et al, 2010). These efforts have been successful in some cases, however the applicability of DE to combinatorial optimization problems remains debatable.…”

Section: Differential Evolution (De)mentioning

confidence: 99%

“…The other way is to transform the solution vectors from the discrete domain to the real domain and to leave the differential operator as it is. Different options are outlined in (Onwubolu, 2009) and (Prado et al, 2010). Here it was opted to transform the solution vectors to the real domain.…”

Section: Differential Evolution (De)mentioning

confidence: 99%

Associating optical measurements of MEO and GEO objects using Population-Based Meta-Heuristic methods

Zittersteijn

Vananti

Schildknecht

et al. 2016

Advances in Space Research

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Currently several thousands of objects are being tracked in the MEO and GEO regions through optical means. The problem faced in this framework is that of Multiple Target Tracking (MTT). The MTT problem quickly becomes an NP-hard combinatorial optimization problem. This means that the effort required to solve the MTT problem increases exponentially with the number of tracked objects. In an attempt to find an approximate solution of sufficient quality, several Population-Based Meta-Heuristic (PBMH) algorithms are implemented and tested on simulated optical measurements. These first results show that one of the tested algorithms, namely the Elitist Genetic Algorithm (EGA), consistently displays the desired behavior of finding good approximate solutions before reaching the optimum. The results further suggest that the algorithm possesses a polynomial time complexity, as the computation times are consistent with a polynomial model. With the advent of improved sensors and a heightened interest in the problem of space debris, it is expected that the number of tracked objects will grow by an order of magnitude in the near future. This research aims to provide a method that can treat the association and orbit determination problems simultaneously, and is able to efficiently process large data sets with minimal manual intervention.

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“…The original DE algorithm, which is introduced by Price and Storn [16,17], conducts optimization in continuous search spaces. Recently, Prado et al [18] proposed a general discrete DE algorithm for combinatorial optimization, and Kashan et al [19] introduced a binary version of the DE algorithm. We propose a binary DE algorithm that is suitable to our channel selection problem, by defining novel binary mutation and crossover operations.…”

Section: Introductionmentioning

confidence: 99%

Channel selection for multispectral color imaging using binary differential evolution

Shen

Yao

et al. 2014

Appl. Opt.

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In multispectral color imaging, there is a demand to select a reduced number of optimal imaging channels to simultaneously speed up the image acquisition process and keep reflectance reconstruction accuracy. In this paper, the channel selection problem is cast as the binary optimization problem, and is consequently solved using a novel binary differential evolution (DE) algorithm. In the proposed algorithm, we define the mutation operation using a differential table of swapping pairs, and deduce the trial solutions using neighboring self-crossover. In this manner, the binary DE algorithm can well adapt to the channel selection problem. The proposed algorithm is evaluated on the multispectral color imaging system on both synthetic and real data sets. It is verified that high color accuracy is achievable by only using a reduced number of channels using the proposed method. In addition, as binary DE is a global optimization algorithm in nature, it performs better than the traditional sequential channel selection algorithm.

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Using differential evolution for combinatorial optimization: A general approach

Cited by 37 publications

References 11 publications

Parallel Preprocessing for the Optimal Camera Placement Problem

Parallel Preprocessing for the Optimal Camera Placement Problem

Associating optical measurements of MEO and GEO objects using Population-Based Meta-Heuristic methods

Channel selection for multispectral color imaging using binary differential evolution

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