Hybrid Algorithm Based on Ant Colony Optimization and Simulated Annealing Applied to the Dynamic Traveling Salesman Problem

Stodola, Petr; Michenka, Karel; Nohel, Jan; Rybanský, Marian

doi:10.3390/e22080884

Cited by 44 publications

(24 citation statements)

References 44 publications

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“…The results of comparison of CCM analysis are displayed in maps in Figure 5 in the form of raster analysis. Raster methods of vehicle mobility analysis in the terrain are also studied in References [19,[39][40][41].…”

Section: Overall Results Of Ccm Analysismentioning

confidence: 99%

“…Other possible approaches for analysis of traversability, not directly utilized in this article, are described in the following papers. Studies dealing with the automatic creation of cross-country movement maps are published by Pokonieczny [39,40] or Pokonieczny and Rybansky [41]. Heštera and Pahernik described the physical-geographic factors of terrain trafficability of military vehicles according to Western World methodologies in Reference [42].…”

Section: Introductionmentioning

confidence: 99%

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The Impact of the Accuracy of Terrain Surface Data on the Navigation of Off-Road Vehicles

Rada

Rybanský

Dohnal

2021

IJGI

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Current soil and surface data are not detailed enough to obtain accurate analyses of cross-country movement. The reason for the research presented in this article was the absence of a methodology for the synthetic assessment of the influence of the terrain surface on the movement of military vehicles. The study is based on analyses of data and information sources of soils and surface conditions primarily with the aim to determine their reliability, availability and precision when used for analyses of terrain traversability by off-road vehicles. The key method to achieve the set objective is the employment of tractive charts of military vehicles and utilized coefficients, the coefficient of rolling resistance and the coefficient of adhesion. Input data and information is tested with a comparative method of cross-country movement analyses. Conversion of soil and surface types to tractive chart coefficients is currently not optimal. For the most part, evaluation of soil type is very inaccurate with a wide range of possible values. Results of the analysis propose developing a methodology of evaluating surface and soils for vehicle traversability.

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Section: Overall Results Of Ccm Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The Impact of the Accuracy of Terrain Surface Data on the Navigation of Off-Road Vehicles

Rada

Rybanský

Dohnal

2021

IJGI

Self Cite

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“…The integration of local search operators with metaheuristics showed significant improvements in the solution quality for static optimization problems [28][29][30][31] and, more recently, for DOPs [8,10]. The Unstringing and Stringing (US) [32] and Lin-Kernighan (LK) [33,34] local search operators have demonstrated promising performance in ACObased memetic algorithms for DOPs [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Hyper-Heuristic Based on ACO and Local Search for Dynamic Optimization Problems

Müller

Bonilha

2021

Algorithms

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Hyper-heuristics comprise a set of approaches that are motivated (at least in part) by the objective of intelligently combining heuristic methods to solve hard optimization problems. Ant colony optimization (ACO) algorithms have been proven to deal with Dynamic Optimization Problems (DOPs) properly. Despite the good results obtained by the integration of local search operators with ACO, little has been done to tackle DOPs. In this research, one of the most reliable ACO schemes, the MAX-MIN Ant System (MMAS), has been integrated with advanced and effective local search operators, resulting in an innovative hyper-heuristic. The local search operators are the Lin–Kernighan (LK) and the Unstringing and Stringing (US) heuristics, and they were intelligently chosen to improve the solution obtained by ACO. The proposed method aims to combine the adaptation capabilities of ACO for DOPs and the good performance of the local search operators chosen in an adaptive way and based on their performance, creating in this way a hyper-heuristic. The travelling salesman problem (TSP) was the base problem to generate both symmetric and asymmetric dynamic test cases. Experiments have shown that the MMAS provides good initial solutions to the local search operators and the hyper-heuristic creates a robust and effective method for the vast majority of test cases.

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“…However, the medical community does not have the same general idea about the pathogenesis of breast cancer due to the uncertainty of its pathogenic factors and the unclear dominance of early clinical features. erefore, most of the diagnosed patients have reached the middle and advanced stage, thus missing the best treatment opportunity for the disease [2,3]. So far, breast cancer has had a considerable impact on women's physical and mental health, and the World Health Organization has paid great attention to it and paid close attention to its development trend [4].…”

Section: Introductionmentioning

confidence: 99%

Value of Magnetic Resonance Imaging Features in Diagnosis and Treatment of Breast Cancer under Intelligent Algorithms

Liu

Tang

Ruan

et al. 2021

Scientific Programming

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This study was to analyze the clinical application value of magnetic resonance imaging (MRI) image features based on intelligent algorithms in the diagnosis and treatment of breast cancer and to provide an effective reference assessment for breast cancer diagnosis. The MRI diagnosis model (ACO-MRI) based on the ant colony algorithm (ACO) was proposed, which was compared with the diagnosis methods based on support vector machine (SVM) and proximity (KNN) algorithm, and the proposed algorithm was applied to MRI images to diagnose breast cancer. The results showed that the accuracy, sensitivity, and specificity of the ACO-MRI model were greater than those of the KNN and SVM algorithm. Moreover, the specificity was statistically considerable compared with the two algorithms of KNN and SVM ( P < 0.05 ). By comparing 1/5 number of ants and the average gray path of the ACO-MRI model under 1/8 number of ants, it was found that the average gray path value of 1/8 number of ants was greatly higher than the average gray path value of 1/5 number of ants ( P < 0.05 ). The differences in the overall distribution of breast MRI imaging features among Luminal A, Luminal B, HER-2 overexpression, and TN were compared. There were considerable differences in the overall distribution of the three breast MRI imaging features of the boundaries, morphology, and enhancement methods among the four groups ( P < 0.05 ). In short, MRI image based on the intelligent algorithm ACO-MRI diagnosis model can effectively improve the diagnosis effect of breast cancer. Its image feature boundaries, morphology, and enhancement methods had good imaging features in the diagnosis of breast cancer.

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Hybrid Algorithm Based on Ant Colony Optimization and Simulated Annealing Applied to the Dynamic Traveling Salesman Problem

Cited by 44 publications

References 44 publications

The Impact of the Accuracy of Terrain Surface Data on the Navigation of Off-Road Vehicles

The Impact of the Accuracy of Terrain Surface Data on the Navigation of Off-Road Vehicles

Hyper-Heuristic Based on ACO and Local Search for Dynamic Optimization Problems

Value of Magnetic Resonance Imaging Features in Diagnosis and Treatment of Breast Cancer under Intelligent Algorithms

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