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Multi-factor of path planning based on an ant colony optimization algorithm / Mingchang Wang in Annals of GIS, vol 26 n° 2 (April 2020)  

Public [article]  inAnnals of GIS > vol 26 n° 2 (April 2020) Titre : Multi-factor of path planning based on an ant colony optimization algorithm Type de document : Article/Communication Auteurs : Mingchang Wang, Auteur ; Chunyu Zhu, Auteur ; Fengyan Wang, Auteur Année de publication : 2020 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes descripteurs IGN] classification floue [Termes descripteurs IGN] gestion des itinéraires [Termes descripteurs IGN] optimisation par colonie de fourmis [Termes descripteurs IGN] planification [Termes descripteurs IGN] processus d'analyse hiérarchisée floue [Termes descripteurs IGN] robot Résumé : (auteur) We propose an improved ant colony algorithm for avoiding obstacles in complex static environments that addresses the problems of a single evaluation factor and low path quality of the traditional ant colony algorithm in path planning. The improvements are: 1) a fuzzy planner is constructed according to the comprehensive evaluation method of fuzzy mathematics and the analytic hierarchy process to comprehensively evaluate and determine the impact of environmental factors, 2) the probability selection formula of the ant colony algorithm is optimized, 3) the pheromone update formula is optimized, and 4) the corner system mechanism is introduced as a post-processing method of path optimization to further smooth the path. Results from simulation experiments of the traditional ant colony algorithm were analysed and compared with those of the improved ant colony algorithm, showing that the latter has a stronger path planning ability and higher algorithm efficiency, resulting in a smoother path with a lower negative impact by environmental factors. Thus, the proposed algorithm is expected to provide a computational basis for effective multi-factor path planning in realistic environments, thereby saving human and material resources. Numéro de notice : A2020-320 Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/19475683.2020.1755725 date de publication en ligne : 13/05/2020 En ligne : https://doi.org/10.1080/19475683.2020.1755725 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95186 [article]

A restrictive polymorphic ant colony algorithm for the optimal band selection of hyperspectral remote sensing images / Xiaohui Ding in International Journal of Remote Sensing IJRS, vol 41 n° 3 (15 - 22 janvier 2020)  

Public [article]  inInternational Journal of Remote Sensing IJRS > vol 41 n° 3 (15 - 22 janvier 2020) . - pp 1093 - 1117 Titre : A restrictive polymorphic ant colony algorithm for the optimal band selection of hyperspectral remote sensing images Type de document : Article/Communication Auteurs : Xiaohui Ding, Auteur ; Shuqing Zhang, Auteur ; Huapeng Li, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 1093 - 1117 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes descripteurs IGN] bande spectrale [Termes descripteurs IGN] dimensionnalité [Termes descripteurs IGN] image hyperspectrale [Termes descripteurs IGN] jeu de données [Termes descripteurs IGN] optimisation par colonie de fourmis [Termes descripteurs IGN] précision de la classification [Termes descripteurs IGN] test de performance Résumé : (auteur) With hundreds of spectral bands, the rise of the issue of dimensionality in the classification of hyperspectral images is usually inevitable. In this paper, a restrictive polymorphic ant colony algorithm (RPACA) based band selection algorithm (RPACA-BS) was proposed to reduce the dimensionality of hyperspectral images. In the proposed algorithm, both local and global searches were conducted considering band similarity. Moreover, the problem of falling into local optima, due to the selection of similar band subsets although travelling different paths, was solved by varying the pheromone matrix between ants moving in opposite directions. The performance of the proposed RPACA-BS algorithm was evaluated using three public datasets (the Indian Pines, Pavia University and Botswana datasets) based on average overall classification accuracy (OA) and CPU processing time. The experimental results showed that average OA of RPACA-BS was up to 89.80%, 94.96% and 92.17% for the Indian Pines, Pavia University and Botswana dataset, respectively, which was higher than that of the benchmarks, including the ant colony algorithm-based band selection algorithm (ACA-BS), polymorphic ant colony algorithm-based band selection algorithm (PACA-BS) and other band selection methods (e.g. the ant lion optimizer-based band selection algorithm). Meanwhile, the time consumed by RPACA-BS and PACA-BS were slightly lower than that of ACA-BS but obviously lower than that of other benchmarks. The proposed RPACA-BS method is thus able to effectively enhance the search abilities and efficiencies of the ACA-BS and PACA-BS algorithms to handle the complex band selection issue for hyperspectral remotely sensed images. Numéro de notice : A2020-214 Thématique : IMAGERIE Nature : Article DOI : 10.1080/01431161.2019.1655810 date de publication en ligne : 20/08/2019 En ligne : https://doi.org/10.1080/01431161.2019.1655810 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94899 [article]

An artificial bee colony-based algorithm to automatically create colour schemes for geovisualizations / Mingguang Wu in Cartographic journal (the), Vol 56 n° 2 (May 2019)  

Public [article]  inCartographic journal (the) > Vol 56 n° 2 (May 2019) . - pp 161 - 174 Titre : An artificial bee colony-based algorithm to automatically create colour schemes for geovisualizations Type de document : Article/Communication Auteurs : Mingguang Wu, Auteur ; Taisheng Chen, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 161 - 174 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes descripteurs IGN] apprentissage automatique [Termes descripteurs IGN] base de règles [Termes descripteurs IGN] carte sur mesure [Termes descripteurs IGN] couleur à l'écran [Termes descripteurs IGN] optimisation par colonie de fourmis [Vedettes matières IGN] Géovisualisation Résumé : (Auteur) Creating appropriate colour schemes is challenging for both novice and experienced cartographers. This paper introduces an artificial bee colony (ABC) algorithm to automatically create various colour schemes. Colour scheme creation is treated as a constrained search problem in a continuous colour space. We considered the gamut of the target device and a series of cartographic rules, such as convention, discrimination, contrast, perceptual uniformity and brightness mirror, in the ABC algorithm and presented detailed initialization, fitness definition, local exploration, and global exploration methods for creating qualitative, sequential and diverging colour schemes. The proposed method is evaluated with a case study, and the results indicate that compared with the brute force search method, the proposed method can create satisfying colour schemes of similar quality but significantly improved efficiency. Numéro de notice : A2019-240 Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/00087041.2018.1507182 date de publication en ligne : 20/11/2018 En ligne : https://doi.org/10.1080/00087041.2018.1507182 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92933 [article]

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Detecting arbitrarily shaped clusters in origin-destination flows using ant colony optimization / Si Song in International journal of geographical information science IJGIS, Vol 33 n° 1-2 (January - February 2019)  

Public [article]  inInternational journal of geographical information science IJGIS > Vol 33 n° 1-2 (January - February 2019) . - pp 134 - 154 Titre : Detecting arbitrarily shaped clusters in origin-destination flows using ant colony optimization Type de document : Article/Communication Auteurs : Si Song, Auteur ; Tao Pei, Auteur ; Ting Ma, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 134 - 154 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes descripteurs IGN] analyse de groupement [Termes descripteurs IGN] données localisées [Termes descripteurs IGN] migration pendulaire [Termes descripteurs IGN] modèle statistique [Termes descripteurs IGN] optimisation par colonie de fourmis [Termes descripteurs IGN] origine - destination Mots-clés libres : logarithme du rapport de vraisemblance  log likelihood ratio (LLR) Résumé : (auteur) An origin-destination (OD) flow can be defined as the movement of objects between two locations. These movements must be determined for a range of purposes, and strong interactions can be visually represented via clustering of OD flows. Identification of such clusters may be useful in urban planning, traffic planning and logistics management research. However, few methods can identify arbitrarily shaped flow clusters. Here, we present a spatial scan statistical approach based on ant colony optimization (ACO) for detecting arbitrarily shaped clusters of OD flows (AntScan_flow). In this study, an OD flow cluster is defined as a regional pair with significant log likelihood ratio (LLR), and the ACO is employed to detect the clusters with maximum LLRs in the search space. Simulation experiments based on AntScan_flow and SaTScan_flow show that AntScan_flow yields better performance based on accuracy but requires a large computational demand. Finally, a case study of the morning commuting flows of Beijing residents was conducted. The AntScan_flow results show that the regions associated with moderate- and long-distance commuting OD flow clusters are highly consistent with subway lines and highways in the city. Additionally, the regions of short-distance commuting OD flow clusters are more likely to exhibit ‘residential-area to work-area’ patterns. Numéro de notice : A2019-019 Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2018.1516287 date de publication en ligne : 10/09/2018 En ligne : https://doi.org/10.1080/13658816.2018.1516287 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91684 [article]

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Hybrid image noise reduction algorithm based on genetic ant colony and PCNN / Chong Shen in The Visual Computer, vol 33 n° 11 (November 2017)  

Public [article]  inThe Visual Computer > vol 33 n° 11 (November 2017) . - pp 1373 - 1384 Titre : Hybrid image noise reduction algorithm based on genetic ant colony and PCNN Type de document : Article/Communication Auteurs : Chong Shen, Auteur ; Ding Wang, Auteur ; Shuming Tang, Auteur ; Huiliang Cao, Auteur ; Jun Liu, Auteur Année de publication : 2017 Article en page(s) : pp 1373 - 1384 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes descripteurs IGN] algorithme génétique [Termes descripteurs IGN] filtrage du bruit [Termes descripteurs IGN] optimisation par colonie de fourmis [Termes descripteurs IGN] réseau neuronal artificiel Résumé : (Auteur) Pulse Coupled Neural Network (PCNN) has gained widespread attention as a nonlinear filtering technology in reducing the noise while keeping the details of images well, but how to determine the proper parameters for PCNN is a big challenge. In this paper, a method that can optimize the parameters of PCNN by combining the genetic algorithm (GA) and ant colony algorithm is proposed, which named as GACA, and the optimized procedure is named as GACA-PCNN. Firstly, the noisy image is filtered by median filter in the proposed GACA-PCNN method; then, the noisy image is filtered by GACA-PCNN constantly and the median filtering image is used as a reference image; finally, a set of parameters of PCNN can be automatically estimated by GACA, and the pretty effective denoising image will be obtained. Experimental results indicate that GACA-PCNN has a better performance on PSNR (peak signal noise rate) and a stronger capacity of preserving the details than previous denoising techniques. Numéro de notice : A2017-712 Thématique : IMAGERIE/INFORMATIQUE Nature : Article DOI : 10.1007/s00371-016-1325-x En ligne : https://doi.org/10.1007/s00371-016-1325-x Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=88093 [article]

Optimal spatial land-use allocation for limited development ecological zones based on the geographic information system and a genetic ant colony algorithm / Nan Mi in International journal of geographical information science IJGIS, vol 29 n° 12 (December 2015)  

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Modèle intégré de description verbale et de représentation spatiale au sein des SIG / David Brosset in Cartes & Géomatique, n° 215 (mars 2013)  

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JFSMA'11, dix-neuvièmes journées francophones sur les systèmes multi-agents / E. Adam (2011)

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Ant intelligence for solving optimal path-covering problems with multi-objectives / X. Li in International journal of geographical information science IJGIS, vol 23 n° 7-8 (july 2009)

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Intelligent GIS for solving high-dimensional site selection problems using ant colony optimization techniques / X. Li in International journal of geographical information science IJGIS, vol 23 n°3-4 (march - april 2009)

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An innovative method to classify remote-sensing images using Ant Colony Optimization / X. Liu in IEEE Transactions on geoscience and remote sensing, vol 46 n° 12 (December 2008)

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A bottom-up approach to discover transition rules of cellular automata using ant intelligence / X. Liu in International journal of geographical information science IJGIS, vol 22 n°11-12 (november 2008)

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Phase unwrapping for SAR interferometry based on an ant colony optimization algorithm / Z.Q. Wei in International Journal of Remote Sensing IJRS, vol 29 n°3-4 (February 2008)

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Généralisation automatique / Julien Gaffuri (2008)

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Optimisation par colonies de fourmis pour les tournées sur arcs / Philippe Lacomme (23/04/2003) 

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