A biased random-key genetic algorithm for the capacitated minimum spanning tree problem

This paper focuses on the capacitated minimum spanning tree(CMST)problem.Given a central processor and a set of remote terminals with specified demands for traffic that must flow between the central processor and terminals,the goal is to design a minimum cost network to carry this demand. Potential links exist between any pair of terminals and between the central processor and the terminals. Each potential link can be included in the design at a given cost.The CMST problem is to design a minimum-cost network connecting the terminals with the central processor so that the flow on any arc of the network is at most Q. A biased random-keygenetic algorithm(BRKGA)is a metaheuristic for combinatorial optimization which evolves a population of random vectors that encode solutions to the combinatorial optimization problem.This paper explores several solution encodings as well as different strategies for some steps of the algorithm and finally proposes a BRKGA heuristic for the CMST problem. Computational experiments are presented showing the effectivenes sof the approach:Seven newbest- known solutions are presented for the set of benchmark instances used in the experiments.Peer ReviewedPostprint (author’s final draft

A new selection operator for genetic algorithms that balances between premature convergence and population diversity

The research objective is to find a balance between premature convergence and population diversity with respect to genetic algorithms (GAs). We propose a new selection scheme, namely, split-based selection (SBS) for GAs that ensures a fine balance between two extremes, i.e. exploration and exploitation. The proposed selection operator is further compared with five commonly used existing selection operators. A rigorous simulation-based investigation is conducted to explore the statistical characteristics of the proposed procedure. Furthermore, performance evaluation of the proposed scheme with respect to competing methodologies is carried out by considering 14 diverse benchmarks from the library of the traveling salesman problem (TSPLIB). Based on t-test statistic and performance index (PI), this study demonstrates a superior performance of the proposed scheme while maintaining the desirable statistical characteristics

Multilayer, locality aware, telecommunication network deployment algorithm

Purpose ”“ In this paper we propose an iterative approach for the deployment of rural telecommunication networks. Methodology/approach/design ”“ This approach relies heavily on the concept of locality, prioritizing small ‘cells’ with a considerable population density, and exploits the natural nesting of the distribution of rural communities, focusing in communities which are populous enough to justify the investment required to provide them with connectivity, and whose sheer size promotes the formation of ‘satellite’ communities that could be benefited from the initial investment at a marginal expense. For this approach, the concept of ‘cells’ is paramount, which are constructed iteratively based on the contour of a Voronoi tessellation centered on the community of interest. Once the focal community has been ‘connected’ with network of the previous layer, the process is repeated with less populous communities at each stage until a coverage threshold has been reached. One of the main contributions of this methodology is that it makes every calculation based on ‘street distance’ instead of Euclidean, giving a more realistic approximate of the length of the network and hence the amount of the investment. To test our results, we ran our experiments on two segregated communities in one of the most complicated terrains, due to the mountain chains, in the state of Chiapas, Mexico. Findings ”“ The results suggest that the use of ‘street distance’ and a local approach leads to the deployment of a remarkably different network than the standard methodology would imply. Practical implications ”“ The results of this paper might lead to a significant reduction in the costs associated with these kinds of projects and therefore make the democratization of connectivity a reality. In order to make our results reproducible, we make all our code open and publicly available on GitHub.Purpose ”“ In this paper we propose an iterative approach for the deployment of rural telecommunication networks. Methodology/approach/design ”“ This approach relies heavily on the concept of locality, prioritizing small ‘cells’ with a considerable population density, and exploits the natural nesting of the distribution of rural communities, focusing in communities which are populous enough to justify the investment required to provide them with connectivity, and whose sheer size promotes the formation of ‘satellite’ communities that could be benefited from the initial investment at a marginal expense. For this approach, the concept of ‘cells’ is paramount, which are constructed iteratively based on the contour of a Voronoi tessellation centered on the community of interest. Once the focal community has been ‘connected’ with network of the previous layer, the process is repeated with less populous communities at each stage until a coverage threshold has been reached. One of the main contributions of this methodology is that it makes every calculation based on ‘street distance’ instead of Euclidean, giving a more realistic approximate of the length of the network and hence the amount of the investment. To test our results, we ran our experiments on two segregated communities in one of the most complicated terrains, due to the mountain chains, in the state of Chiapas, Mexico. Findings ”“ The results suggest that the use of ‘street distance’ and a local approach leads to the deployment of a remarkably different network than the standard methodology would imply. Practical implications ”“ The results of this paper might lead to a significant reduction in the costs associated with these kinds of projects and therefore make the democratization of connectivity a reality. In order to make our results reproducible, we make all our code open and publicly available on GitHub.Purpose ”“ In this paper we propose an iterative approach for the deployment of rural telecommunication networks. Methodology/approach/design ”“ This approach relies heavily on the concept of locality, prioritizing small ‘cells’ with a considerable population density, and exploits the natural nesting of the distribution of rural communities, focusing in communities which are populous enough to justify the investment required to provide them with connectivity, and whose sheer size promotes the formation of ‘satellite’ communities that could be benefited from the initial investment at a marginal expense. For this approach, the concept of ‘cells’ is paramount, which are constructed iteratively based on the contour of a Voronoi tessellation centered on the community of interest. Once the focal community has been ‘connected’ with network of the previous layer, the process is repeated with less populous communities at each stage until a coverage threshold has been reached. One of the main contributions of this methodology is that it makes every calculation based on ‘street distance’ instead of Euclidean, giving a more realistic approximate of the length of the network and hence the amount of the investment. To test our results, we ran our experiments on two segregated communities in one of the most complicated terrains, due to the mountain chains, in the state of Chiapas, Mexico. Findings ”“ The results suggest that the use of ‘street distance’ and a local approach leads to the deployment of a remarkably different network than the standard methodology would imply. Practical implications ”“ The results of this paper might lead to a significant reduction in the costs associated with these kinds of projects and therefore make the democratization of connectivity a reality. In order to make our results reproducible, we make all our code open and publicly available on GitHub

Articles indexats publicats per investigadors del Campus de Terrassa: 2015

Aquest informe recull els 284 treballs publicats per 218 investigadors/es del Campus de Terrassa en revistes indexades al Journal Citation Report durant el 2015Postprint (published version

A BIASED RANDOM-KEY GENETIC ALGORITHM FOR THE CAPACITATED MINIMUM SPANNING TREE PROBLEM

Abstract. This paper focuses on the capacitated minimum spanning tree (CMST) problem. Given a central processor and a set of remote terminals with specified demands for traffic that must flow between the central processor and terminals, the goal is to design a minimum cost network to carry this demand. Potential links exist between any pair of terminals and between the central processor and the terminals. Each potential link can be included in the design at a given cost. The CMST problem is to design a minimum-cost network connecting the terminals with the central processor so that the flow on any arc of the network is at most Q. A biased random-key genetic algorithm (BRKGA) is a metaheuristic for combinatorial optimization which evolves a population of random vectors that encode solutions to the combinatorial optimization problem. This paper explores several solution encodings as well as different strategies for some steps of the algorithm and finally proposes a BRKGA heuristic for the CMST problem. Computational experiments are presented showing the effectiveness of the approach: Seven new best-known solutions are presented for the set of benchmark instances used in the experiments. 1

A biased random-key genetic algorithm for the capacitated minimum spanning tree problem

This paper focuses on the capacitated minimum spanning tree(CMST)problem.Given a central processor and a set of remote terminals with specified demands for traffic that must flow between the central processor and terminals,the goal is to design a minimum cost network to carry this demand. Potential links exist between any pair of terminals and between the central processor and the terminals. Each potential link can be included in the design at a given cost.The CMST problem is to design a minimum-cost network connecting the terminals with the central processor so that the flow on any arc of the network is at most Q. A biased random-keygenetic algorithm(BRKGA)is a metaheuristic for combinatorial optimization which evolves a population of random vectors that encode solutions to the combinatorial optimization problem.This paper explores several solution encodings as well as different strategies for some steps of the algorithm and finally proposes a BRKGA heuristic for the CMST problem. Computational experiments are presented showing the effectivenes sof the approach:Seven newbest- known solutions are presented for the set of benchmark instances used in the experiments.Peer Reviewe

On the Role of Genetic Algorithms in the Pattern Recognition Task of Classification

In this dissertation we ask, formulate an apparatus for answering, and answer the following three questions: Where do Genetic Algorithms fit in the greater scheme of pattern recognition? Given primitive mechanics, can Genetic Algorithms match or exceed the performance of theoretically-based methods? Can we build a generic universal Genetic Algorithm for classification? To answer these questions, we develop a genetic algorithm which optimizes MATLAB classifiers and a variable length genetic algorithm which does classification based entirely on boolean logic. We test these algorithms on disparate datasets rooted in cellular biology, music theory, and medicine. We then get results from these and compare their confusion matrices. For those unfamiliar with Genetic Algorithms, we include a primer on the subject in chapter 1, and include a literature review and our motivations. In Chapter 2, we discuss the development of the algorithms necessary as well as explore other features necessitated by their existence. In Chapter 3, we share and discuss our results and conclusions. Finally, in Chapter 4, we discuss future directions for the corpus we have developed

Esnek atölye tipi hücre çizelgeleme problemleri için çok amaçlı matematiksel model ve genetik algoritma ile çözüm önerisi

06.03.2018 tarihli ve 30352 sayılı Resmi Gazetede yayımlanan “Yükseköğretim Kanunu İle Bazı Kanun Ve Kanun Hükmünde Kararnamelerde Değişiklik Yapılması Hakkında Kanun” ile 18.06.2018 tarihli “Lisansüstü Tezlerin Elektronik Ortamda Toplanması, Düzenlenmesi ve Erişime Açılmasına İlişkin Yönerge” gereğince tam metin erişime açılmıştır.Günümüz rekabetçi iş ortamında, müşteriler daha düşük maliyetle daha yüksek kalitede çeşitli ürünleri satın almak istemektedir. İmalat firmaları, talep çeşitliliğini karşılamak için yüksek derecede ürün çeşitliliğine ve küçük imalat parti büyüklüğüne ihtiyaç duymaktadır. Üretimdeki ürün çeşitlilikleri uzun hazırlık ve taşıma süreleri, karmaşık çizelgeleme problemleri gibi birçok probleme neden olmaktadır. Geleneksel imalat sistemleri, bu tip değişikliklere cevap vermede yeterince esnek değilken Hücresel Üretim Sistemleri üreticilerin bu ihtiyaçlarına cevap verebilecek özelliklere sahiptir. Ayrıca gerçek hayat problemlerinin çoğunda, bir parçanın bazı ya da bütün operasyonları birden fazla makinede işlem görebilmekte ve bazen de bu operasyonlar bir makineyi ya da iş merkezini birden fazla kez ziyaret etmektedir. Bu seçenek sisteme esneklik kazandırırken bu kadar karmaşık bir üretim sisteminin başarılı ve doğru bir şekilde işletilebilmesi kaynakların etkin kullanılmasını da gerektirmektedir. Bu çalışma, istisnai parçaları, hücrelerarası hareketleri, hücrelerarası taşıma sürelerini, sıra bağımlı parça ailesi hazırlık sürelerini ve yeniden işlem gören parçaları dikkate alarak hücresel imalat ortamında esnek atölye tipi çizelgeleme probleminin çözümüne dair bir matematiksel model ve çözüm yöntemi sunmaktadır. Mevcut bilgilerimiz ışığında yapılan bu çalışma Esnek Atölye Tipi Hücre Çizelgeleme Probleminde (EATHÇP) çok amaçlı matematiksel model ve meta-sezgiselinin kullanımı için ilk girişimdir. Bununla birlikte gerçek hayat uygulamaları için EATHÇP süreci, birçok çelişen amacı dikkate almayı gerektirdiği için ele alınan skalerleştirme metodu pratik uygulama ve teorik araştırma açısından oldukça önemlidir. Önerilen karma tamsayılı doğrusal olmayan matematiksel modelle küçük ve orta boyutlu problemler çözülebilmektedir. Büyük boyutlu problemlerin çözümü, doğrusal olmayan modellerle makul zamanlarda olamayacağı ya da çok uzun süreceği için konik skalerleştirmeli çok amaçlı matematiksel modeli kullanan bir Genetik Algoritma (GA) meta-sezgisel çözüm yöntemi önerilmiştir. GA yaklaşımının en iyi veya en iyiye yakın çözüme ulaşmasına etki eden parametrelerin en iyi kombinasyonu belirlemek amacı ile bir deney tasarımı gerçekleştirilmiştir. Bu tez çalışması için Eskişehir Tülomsaş Motor Fabrikası'nda bir uygulama çalışması yürütülmüştür. Yürütülen bu çalışma, altı farklı amaç ağırlık değerleri kullanılarak hem konik skalerleştirmeli GA yaklaşımı ile hem de ağırlıklı toplam skalerleştirmeli GA yaklaşımı ile çözülmüştür. Amaç ağırlıklarının beşinde çok amaçlı konik skalerleştirme GA yaklaşımının daha baskın sonuçlara ulaşabildiği vurgulanmıştır. Ayrıca, önerilen çok amaçlı modelin gerçek hayat problemleri için de makul zamanda uygun çözümler üretebildiği gösterilmiştir.In today's highly competitive business environment, customers desire to buy various products with higher quality at lower costs. Manufacturing firms require a high degree of product variety and small manufacturing lot sizes to meet the demand variability. The product variations in manufacturing cause many problems such as lengthy setup and transportation times, complex scheduling. Cellular Manufacturing Systems contain the characteristics, which will respond to the needs of manufacturers, even though Conventional Manufacturing Systems are not flexible enough to respond to changes. In addition, in most real life manufacturing problems, some or all operations of a part can be processed on more than one machine, and sometimes operations may visit a machine or work center more than once. It is necessary to use resources effectively in order to run such a complex production system successfully. In this study, a mathematical model and a solution approach that deals with a flexible job shop scheduling problem in cellular manufacturing environment is proposed by taking into consideration exceptional parts, intercellular moves, intercellular transportation times, sequence-dependent family setup times, and recirculation. To the best of our knowledge, this is the first attempt to use multi-objective mathematical model and meta-heuristic approach for a Flexible Job Shop Cell Scheduling Problem (FJCSP). However, in the real-life applications, the scalarization method considered is highly important in terms of theoretical research and practical application because the FJCSP process is not easy because of many conflicting objectives. The proposed mixed integer non-linear model can be used for solving small and middle scaled problems. Solution of large scaled problems is not possible in reasonable time or takes too long time, so a Genetic Algorithm (GA) meta-heuristic approach that uses a multi-objective mathematical model with conic scalarization has been presented. An experimental design was used to determine the best combination of parameters which are affected performance of genetic algorithm to achieve optimum or sub-optimum solution. In this thesis study, a case study was conducted in Tülomsaş Locomotive and Engine Factory in Eskişehir. This study was solved by using both conic scalarization GA approach and weighted sum scalarization GA approach with six different weights of objective. It is emphasized that the multi-objective conic scalarization GA approach has better quality than other approach for five different weights of objective. In addition, it has been shown that the multi-objective model could also obtain optimum results in reasonable time for the real-world problems