A hybrid data mining heuristic to solve the point‐feature cartographic label placement problem

Guerine, Marcos; Rosseti, Isabel; Plastino, Alexandre

doi:10.1111/itor.12666

Cited by 14 publications

(5 citation statements)

References 35 publications

(81 reference statements)

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“…The sensor data set used in Experiment 2 is related to the RMAX UAV experimental platform, which is used to test the coordination control quality of UAVs cluster. In the experiments, the attributes, including sback, sright, throttleStick, rudderStick, sleft, kdelay, timedelay, time, delm[0], delm [1], delm [2], pos[0], pos [1], pos [2] and so force were used. The dataset of the experiments has 7,000,000 records and 48 attributes, as shown in Fig.…”

Section: ) Experiments 2: Uav Flight Control Datamentioning

confidence: 99%

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Dynamic Data Mining of Sensor Data

2020

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The research of data mining has aroused widespread concern in academia and industry. However, an important mark of the Internet of Things era is that sensor data replaces artificially compiled data. How to extract valuable knowledge and patterns from a large amount of data generated by sensors is a meaningful research topic. This paper proposes a dynamic data mining framework for processing sensor data. A sensor data mining model which can be used in the process of dynamic change is constructed. In this model, different sensor network environments are considered as different physical systems. The physical system and its parameters are trained by collecting and mining historical changes in sensor data; the associations between different sensor network environments are discovered by mining the associations between the parameters of different physical systems. In our limited experimental environment, the physical quantities considered included transmission distance, transmission delay, sensor data, data changes, and so on. Experiments were carried out on the designated experimental platform, and the results showed that the model could mine the dynamic data and find stable patterns. Through the analysis of the experimental results, it was found that the model had reference value for the dynamic mining of sensor data, and was expected to construct new methods for industrial big data analysis. INDEX TERMS Clustering, dynamic characteristics, dynamic data mining, IoT, sensors.

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Section: ) Experiments 2: Uav Flight Control Datamentioning

confidence: 99%

“…Traditional data mining locates associations between commodities [1]- [3], so it is impossible to mine associations between one physical system and another physical system. Therefore, directly copying traditional data mining methods cannot mine the relationship between physical systems and different physical systems.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Data Mining of Sensor Data

2020

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“…Araujo et al [17] improved Rabello's clustering search algorithm by proposing a density clustering search using three methods: density-based clustering (DBSCAN), natural group identification (NGI), and label propagation (LP) to detect promising solutions. Guerine [18] combined data mining techniques and clustering search to achieve faster convergence and better label results than the previous clustering search. Cravo et al [19] applied the greedy adaptive random algorithm to the point-feature label placement for solving.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Discrete Artificial Bee Colony Algorithm Based on Label Similarity for Solving Point-Feature Label Placement Problem

Cao,

Xu,

Zhang

et al. 2023

IJGI

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The artificial bee colony algorithm (ABC) is a promising metaheuristic algorithm for continuous optimization problems, but it performs poorly in solving discrete problems. To address this issue, this paper proposes a hybrid discrete artificial bee colony (HDABC) algorithm based on label similarity for the point-feature label placement (PFLP) problem. Firstly, to better adapt to PFLP, we have modified the update mechanism for employed bees and onlooker bees. Employed bees learn the label position of the better individuals, while onlooker bees perform dynamic probability searches using two neighborhood operators. Additionally, the onlooker bees’ selection method selects the most promising solutions based on label similarity, which improves the algorithm’s search capabilities. Finally, the Metropolis acceptance strategy is replaced by the original greedy acceptance strategy to avoid the premature convergence problem. Systematic experiments are conducted to verify the effectiveness of the neighborhood solution generation method, the selection operation based on label similarity, and the Metropolis acceptance strategy in this paper. In addition, experimental comparisons were made at different instances and label densities. The experimental results show that the algorithm proposed in this paper is better or more competitive with the compared algorithm.

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“…In the field of combinatorial optimization, research can follow two complementary directions: on the one hand, problem‐oriented approaches that exploit specificities of the studied problem (Moura, 2019; Guerine et al., 2020), and on the other hand, generic approaches that try to provide guidelines to establish solvers that can handle optimization problems in general (Bianchi et al., 2009; Hao, 2012). This observation can be done for exact algorithms as well as for heuristic search algorithms.…”

Section: Introductionmentioning

confidence: 99%

Partial neighborhood local searches

Tari

Basseur

Goëffon

2021

Int Trans Operational Res

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In this work, we study partial neighborhood local search (PNLS) techniques, which consist of adaptive walks where moves are chosen in a random subset of the current solution neighborhood. PNLSs balance between intensification and diversification is mainly determined by its single parameter λ designing the subset size. We analyze and discuss three PNLSs variants, using the abstraction of several combinatorial optimization problems into fitness landscapes: NK landscapes, Unconstrained Binary Quadratic Programming, Flow-shop scheduling, and Quadratic Assignment. Our empirical study first analyses the structure of these landscapes through indicators. Then, we perform a parameter study of PNLSs for two computational budgets to study the impact of the sample size on the balance between intensification and diversification on different landscapes. Moreover, these experiments allow us to set an appropriate parameter value to compare the ability of PNLSs to reach good-quality solutions accurately. Finally, we compare PNLS variants with two classical metaheuristics, identifying links between landscape characteristics and algorithms behavior.

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A hybrid data mining heuristic to solve the point‐feature cartographic label placement problem

Cited by 14 publications

References 35 publications

Dynamic Data Mining of Sensor Data

Dynamic Data Mining of Sensor Data

A Hybrid Discrete Artificial Bee Colony Algorithm Based on Label Similarity for Solving Point-Feature Label Placement Problem

Partial neighborhood local searches

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