An ant colony-based semi-supervised approach for learning classification rules

Albinati, Julio; Oliveira, Samuel; Otero, Fernando E. B.; Pappa, Gisele L.

doi:10.1007/s11721-015-0116-8

Cited by 14 publications

(12 citation statements)

References 27 publications

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“…(1) and No. (24) tour routes. The two routes' generation tree weight function value L(•) is the minimum value, and thus it relates to the maximum sub-unit motive function I(•) iteration value, that is, the tourist can get the best motive benefits from the two optimal routes.…”

Section: Optimal Tour Route Searching Resultsmentioning

confidence: 99%

“…While considering that the trip in the whole tour route will be influenced by factors, like geographic information services, traffic information services, physical qualifications, tourist site attraction indexes, etc., a tour route planning algorithm should combine with these factors, as they are indispensable objective conditions in the trip process, and they conform to the tour reality [24][25][26]. The tour routes output by the smart machine have the following features: (1) all tourist site classifications and specific tourist sites conform to the tourist's needs and interests; (2) all tourist sites are nearest to temporary accommodation, which demands the lowest expenditure; (3) temporary accommodations are the both starting point and terminal point of the whole trip, which conforms to the schedule; (4) the algorithm combines with factors that influence the motive benefits of the trip, which conforms to the tour reality; and, (5) the smart machine not only outputs optimal tour routes, but also outputs sub-optimal ones, guide maps, and decision supports.…”

Section: Introductionmentioning

confidence: 99%

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Smart Tour Route Planning Algorithm Based on Naïve Bayes Interest Data Mining Machine Learning

Zhou

Liu

et al. 2020

IJGI

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A smart tour route planning algorithm based on a Naïve Bayes interest data mining machine learning is brought forward in the paper, according to the problems of current tour route planning methods. A machine learning model of Naïve Bayes interest data mining is set up by learning a mass of training data on tourists’ interests and needs. Through the recommended interest tourist site classifications from the machine learning module, the optimal tourist site mining algorithm based on the membership degree searching propagating tree of a tourist’s temporary accommodation is set up, which mines and outputs the optimal tourist sites. The mined optimal tourist sites are taken as seed points to set up a tour route planning algorithm based on the optimal propagating tree of a closed-loop structure. Through the proposed algorithm, an experiment is designed and performed to output optimal tour routes conforming to tourists’ needs and interests, including the propagating tree closed-loop structures, a minimum heap of propagating tree weight function value, and a weight function value complete binary tree. We prove that the proposed algorithm has the features of intelligence and accuracy, and it can learn tourists’ needs and interests to output optimal tourist sites and tour routes and ensure that tourists can get the best motive benefits and travel experience in the tour process, by analyzing the experiment data and results.

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Section: Optimal Tour Route Searching Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Smart Tour Route Planning Algorithm Based on Naïve Bayes Interest Data Mining Machine Learning

Zhou

Liu

et al. 2020

IJGI

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show abstract

“…The pheromone model and the probability model are the core of this algorithm [31]. ACO algorithms have been widely used for pattern classification [32], cloud computing [33], network coding [34], robot path planning [35] and in other fields. We propose some optimizations to improve the ACO algorithm for the study of the virtual globe platform and penetration route planning.…”

Section: Route Planning Methodsmentioning

confidence: 99%

Unmanned Aerial Vehicle Route Planning in the Presence of a Threat Environment Based on a Virtual Globe Platform

Zhang

Liu

et al. 2016

IJGI

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Route planning is a key technology for an unmanned aerial vehicle (UAV) to fly reliably and safely in the presence of a threat environment. Existing route planning methods are mainly based on the simulation scene, whereas approaches based on the virtual globe platform have rarely been reported. In this paper, a new planning space for the virtual globe and the planner is proposed and a common threat model is constructed for threats including a no-fly zone, hazardous weather, radar coverage area, missile killing zone and dynamic threats. Additionally, an improved ant colony optimization (ACO) algorithm is developed to enhance route planning efficiency and terrain masking ability. Our route planning methods are optimized on the virtual globe platform for practicability. A route planning system and six types of planners were developed and implemented on the virtual globe platform. Finally, our evaluation results demonstrate that our optimum planner has better performance in terms of fuel consumption, terrain masking, and risk avoidance. Experiments also demonstrate that the method and system described in this paper can be used to perform global route planning and mission operations.

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“…5. 4 The joint angle values of the particles are updated according to the speed (eq. 5) and position equation (eq.…”

Section: The Particle (P) Loop Is Startedmentioning

confidence: 99%

A Comprehensive Research on The Use Of Swarm Algorithms in The Inverse Kinematics Solution

Dereli¹,

Köker²,

Öylek³

et al. 2018

Journal of Polytechnic

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Bu makaleye şu şekilde atıfta bulunabilirsiniz(To cite to this article): Dereli S., Köker R., Öylek İ. ve Ay M., "A comprehensive research on the use of swarm algorithms in the ınverse kinematics solution", Politeknik Dergisi, 22(1): 75-79, (2019).Erişim linki (To link to this article): http://dergipark.gov.tr/politeknik/archive DOI: 10.2339/politeknik.374830 Politeknik Dergisi, 2019; 22(1) : 75-79 Journal of Polytechnic, 2019; 22 (1) ABSTRACTA comprehensive study was presented on swarm algorithms used in the inverse kinematic solution which is the basis of robot control in this paper. Because it is a complex and difficult problem group, the inverse kinematic solution is an important problem especially in robot arms with a lot of joints. So, swarm optimization techniques which were inspired by the animals in the nature, are often used by researchers, because these techniques find the best solution in a particular solution space. Artificial bee colony, firefly algorithm and particle swarm algorithm are the swarm techniques mentioned in this study. Since, these algorithms are frequently used in inverse kinematic solution in the literature. Keywords: Robotic, inverse kinematics, swarm algorithms. ÖZBu çalışmada, robot kontrolünün temelinde bulunan ters kinematik çözümün bulunması için kullanılan sürü algoritmalarının kapsamlı bir araştırması sunulmuştur. Ters kinematik çözüm karmaşık ve zor problem grupları arasında olduğundan önemli bir problemdir. Özellikle bu durum robot kolunun çok fazla sayıda eklemi bulunduğunda çok daha zorlaşmaktadır. Bu nedenle doğadaki hayvanların davranışlarından esinlenilen sürü optimizasyonu teknikleri araştırmacılar tarafından sık sık kullanılmaktadır. Çünkü bu teknikler parçacık çözüm uzayındaki en iyi değeri elde etmektedirler. Yapay arı kolonisi, ateş böceği algoritması ve parçacık sürü algoritması bu çalışmada bahsi geçen tekniklerdir ve literatürde sıklıkla tercih edilmişlerdir.

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An ant colony-based semi-supervised approach for learning classification rules

Abstract: The version in the Kent Academic Repository may differ from the final published version. Users are advised to check http://kar.kent.ac.uk for the status of the paper. Users should always cite the published version of record.

Cited by 14 publications

References 27 publications

Smart Tour Route Planning Algorithm Based on Naïve Bayes Interest Data Mining Machine Learning

Smart Tour Route Planning Algorithm Based on Naïve Bayes Interest Data Mining Machine Learning

Unmanned Aerial Vehicle Route Planning in the Presence of a Threat Environment Based on a Virtual Globe Platform

A Comprehensive Research on The Use Of Swarm Algorithms in The Inverse Kinematics Solution

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