Greedy heuristic algorithm for solving series of eee components classification problems*

Kazakovtsev, Lev; Антамошкин, А. Н.; Fedosov, V V

doi:10.1088/1757-899x/122/1/012011

Cited by 8 publications

(3 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Since the MCLP was suggested by Church and ReVelle in 1973 [5], many models and methodologies were proposed to achieve MCLP that applied in various sectors to cover area and determine the best location facilities [2]. Such that, [6] used the agglomerative greedy heuristics with genetic algorithms to solve the p-median clustering problem. Moreover, the MCLP was represented as numbering system that has been have ability to represent in computer software [5].…”

Section: An Overview Of Covering Area Problemmentioning

confidence: 99%

“…Dataset representation and collection: the dataset in consistence of the results of [4] and the collected data from different resources (point 3 and 4) will be represented in Arc-GIS database. 6. System description and applying: At this point, the procedures, variables, and facts are ready to apply in Arc-GIS.…”

Section: Linked the Research Decision Effective Factors From [4] Andmentioning

confidence: 99%

See 1 more Smart Citation

A Comprehensive Dynamic Approach for Selecting the Optimal Position of Telecommunication Towers

Asassafeh¹,

Akkaya²,

Al-Tarawneh³

2020

IRJIET

View full text Add to dashboard Cite

The telecommunication industry in Jordan has become highly competitive which adds more pressure on service providers to operate more effectively in the manner of the quality of service without undermining the cost of the service. Selecting the optimal position of the telecommunication tower is one of the most important factors to ensure providing high-quality service at a minimum cost. However, selecting the right position for the tower is complicated and involves different factors such as population density, topology, and reserved area. Unfortunately, the current methods and techniques are not robust enough to ensure selecting the right position effectively, that makes the developing approaches imperative. This paper presents a comprehensive dynamic approach for selecting the optimal position of telecommunication towers; the approach integrates the most common selection factors, rules and the GIS system in a multi-phase process. The accuracy of the proposed approach is relatively high (82%), it is evaluated by comparing the recommended positions with the experts' selection.

show abstract

Section: An Overview Of Covering Area Problemmentioning

confidence: 99%

Section: Linked the Research Decision Effective Factors From [4] Andmentioning

confidence: 99%

A Comprehensive Dynamic Approach for Selecting the Optimal Position of Telecommunication Towers

Asassafeh¹,

Akkaya²,

Al-Tarawneh³

2020

IRJIET

View full text Add to dashboard Cite

show abstract

“…This searching process reduces the time requirement substantially. Problem-specific properties [36] and greedy approaches [29] are often used for subset selection. Meta-heuristic algorithms [13,16,44] are used to overcome heuristic algorithms' inability to circumvent local optima and are applicable to a wide range of problems.…”

Section: Introductionmentioning

confidence: 99%

Binary Genetic Swarm Optimization: A Combination of GA and PSO for Feature Selection

Ghosh

Guha

Alam

et al. 2019

Journal of Intelligent Systems

View full text Add to dashboard Cite

Abstract Feature selection (FS) is a technique which helps to find the most optimal feature subset to develop an efficient pattern recognition model under consideration. The use of genetic algorithm (GA) and particle swarm optimization (PSO) in the field of FS is profound. In this paper, we propose an insightful way to perform FS by amassing information from the candidate solutions produced by GA and PSO. Our aim is to combine the exploitation ability of GA with the exploration capacity of PSO. We name this new model as binary genetic swarm optimization (BGSO). The proposed method initially lets GA and PSO to run independently. To extract sufficient information from the feature subsets obtained by those, BGSO combines their results by an algorithm called average weighted combination method to produce an intermediate solution. Thereafter, a local search called sequential one-point flipping is applied to refine the intermediate solution further in order to generate the final solution. BGSO is applied on 20 popular UCI datasets. The results were obtained by two classifiers, namely, k nearest neighbors (KNN) and multi-layer perceptron (MLP). The overall results and comparisons show that the proposed method outperforms the constituent algorithms in 16 and 14 datasets using KNN and MLP, respectively, whereas among the constituent algorithms, GA is able to achieve the best classification accuracy for 2 and 7 datasets and PSO achieves best accuracy for 2 and 4 datasets, respectively, for the same set of classifiers. This proves the applicability and usefulness of the method in the domain of FS.

show abstract

Classification of T6 Tempered 6XXX Series Aluminum Alloys Based on Machine Learning Principles

Tiwari,

Jalalian,

Mendis

et al. 2023

JOM

View full text Add to dashboard Cite

Aluminum alloys are widely used in each sector of engineering because of their lower density coupled with higher strength compared to many existing alloys of other metals. Due to these unique characteristics, there is acceleration in demand and discovery of new aluminum alloys with targeted properties and compositions. Traditional methods of designing new materials with desired properties, like ‘domain specialists and trial-and-error ' approaches, are laborious and costly. These techniques also lead to the expansion of alloy search area. Also, high demand for recycling of aluminum alloys requires fewer alloy groups. We suggest a machine learning design system to reduce the number of grades in the 6XXX series of aluminum alloys by collecting the features involving chemical composition and tensile properties at T6 tempering state. This work demonstrates the efficiency of grouping the aluminum alloys into a number of clusters by a combined PCA and K-means algorithm. To understand the physics inside the clusters we used an explainable artificial intelligence algorithm and connected the findings with sound metallurgical reasoning. Through machine learning we will narrow down the search space of 6XXX series aluminum alloys to few groups. This work offers a useful method for reducing compositional space of aluminum alloys.

show abstract

Greedy heuristic algorithm for solving series of eee components classification problems*

Cited by 8 publications

References 4 publications

A Comprehensive Dynamic Approach for Selecting the Optimal Position of Telecommunication Towers

A Comprehensive Dynamic Approach for Selecting the Optimal Position of Telecommunication Towers

Binary Genetic Swarm Optimization: A Combination of GA and PSO for Feature Selection

Classification of T6 Tempered 6XXX Series Aluminum Alloys Based on Machine Learning Principles

Contact Info

Product

Resources

About