Ant-Balanced multiple traveling salesmen: ACO-BmTSP

A new algorithm based on the ant colony optimization (ACO) method for the multiple traveling salesman problem (mTSP) is presented and defined as ACO-BmTSP. This paper addresses the problem of solving the mTSP while considering several salesmen and keeping both the total travel cost at the minimum and the tours balanced. Eleven different problems with several variants were analyzed to validate the method. The 20 variants considered three to twenty salesmen regarding 11 to 783 cities. The results were compared with best-known solutions (BKSs) in the literature. Computational experiments showed that a total of eight final results were better than those of the BKSs, and the others were quite promising, showing that with few adaptations, it will be possible to obtain better results than those of the BKSs. Although the ACO metaheuristic does not guarantee that the best solution will be found, it is essential in problems with non-deterministic polynomial time complexity resolution or when used as an initial bound solution in an integer programming formulation. Computational experiments on a wide range of benchmark problems within an acceptable time limit showed that compared with four existing algorithms, the proposed algorithm presented better results for several problems than the other algorithms did.info:eu-repo/semantics/publishedVersio

Traveling Salesman Problem

This book is a collection of current research in the application of evolutionary algorithms and other optimal algorithms to solving the TSP problem. It brings together researchers with applications in Artificial Immune Systems, Genetic Algorithms, Neural Networks and Differential Evolution Algorithm. Hybrid systems, like Fuzzy Maps, Chaotic Maps and Parallelized TSP are also presented. Most importantly, this book presents both theoretical as well as practical applications of TSP, which will be a vital tool for researchers and graduate entry students in the field of applied Mathematics, Computing Science and Engineering

An Integrated Solution Approach for Flow Shop Scheduling

This study seeks to integrate Random Key Genetic Algorithm (RKGA) and Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) to compute makespan and solve the Flow Shop Scheduling Problem (FSSP). FSSP is considered as a Multi Criteria Decision Making Problem (MCDM) by setting machines as criteria and jobs as alternatives. RKGA is employed to determine the best weights for the criteria that directly affect the robustness of the solution. The proposed methodology is presented with illustrative example and applied to benchmark problems. The solutions are compared to well-known construction heuristics. The proposed methodology provides the best or reasonable solutions in acceptable computational times

Swarm Intelligent in Bio-Inspired Perspective: A Summary

This paper summarizes the research performed in the field of swarm intelligent in recent years. The classification of swarm intelligence based on behavior is introduced.  The principles of each behaviors, i.e. foraging, aggregating, gathering, preying, echolocation, growth, mating, clustering, climbing, brooding, herding, and jumping are described. 3 algorithms commonly used in swarm intelligent are discussed.  At the end of summary, the applications of the SI algorithms are presented

Algoritmos evolutivos para alguns problemas em telecomunicações

Orientadores: Flavio Keidi Miyazawa, Mauricio Guilherme de Carvalho ResendeTese (doutorado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: Nos últimos anos, as redes de telecomunicação tem experienciado um grande aumento no fluxo de dados. Desde a utilização massiva de vídeo sob demanda até o incontável número de dispositivos móveis trocando texto e vídeo, o tráfego alcançou uma escala capaz de superar a capacidade das redes atuais. Portanto, as companhias de telecomunicação ao redor do mundo tem sido forçadas a aumentar a capacidade de suas redes para servir esta crescente demanda. Como o custo de instalar uma infraestrutura de rede é geralmente muito grande, o projeto de redes usa fortemente ferramentas de otimização para manter os custos tão baixos quanto possível. Nesta tese, nós analisamos vários aspectos do projeto e implementação de redes de telecomunicação. Primeiramente, nós apresentamos um novo problema de projeto de redes usado para servir demandas sem fio de dispositivos móveis e rotear tal tráfego para a rede principal. Tais redes de acesso são baseadas em tecnologias sem fio modernos como Wi-Fi, LTE e HSPA. Este problema consideramos várias restrições reais e é difícil de ser resolvido. Nós estudamos casos reais nas vizinhanças de uma grande cidade nos Estados Unidos. Em seguida, nós apresentamos uma variação do problema de localização de hubs usado para modelar as redes principais (backbones ou laços centrais). Este problema também pode ser utilizado para modelar redes de transporte de cargas e passageiros. Nós também estudamos o problema de clusterização correlacionada com sobreposições usado para modelar o comportamento dos usuários quando utilizam seus equipamentos móveis. Neste problema, nós podemos rotular um objeto usando múltiplos rótulos e analisar a conexão entre eles. Este problema é adequado para análise de mobilidade de equipamentos que pode ser usada para estimar o tráfego em uma dada região. E finalmente, nós analisamos o licenciamento de espectro sobre uma perspectiva governamental. Nestes casos, uma agência do governo deseja vender licenças para companhias de telecomunicação para que operem em uma dada faixa de espectro. Este processo usualmente é conduzido usando leilões combinatoriais. Para todos problemas, nós propomos algoritmos genéticos de chaves aleatórias viciadas e modelos de programação linear inteira mista para resolvê-los (exceto para o problema de clusterização correlacionada com sobreposição, devido sua natureza não-linear). Os algoritmos que propusemos foram capazes de superar algoritmos do estado da arte para todos problemasAbstract: Cutting and packing problems are common problems that occur in many industry and business process. Their optimized resolution leads to great profits in several sectors. A common problem, that occur in textil and paper industries, is to cut a strip of some material to obtain several small items, using the minimum length of material. This problem, known by Two Dimensional Strip Packing Problem (2SP), is a hard combinatorial optimization problem. In this work, we present an exact algorithm to 2SP, restricted to two staged cuts (known by Two Dimensional Level Strip Packing, 2LSP). The algorithm uses the branch-and-price technique, and heuristics based on approximation algorithms to obtain upper bounds. The algorithm obtained optimal or almost optimal for small and moderate sized instancesAbstract: In last twenty years, telecommunication networks have experienced a huge increase in data utilization. From massive on-demand video to uncountable mobile devices exchanging text and video, traffic reached scales that overcame the network capacities. Therefore, telecommunication companies around the world have been forced to increase their capacity to serve this increasing demand. As the cost to deploy network infrastructure is usually very large, the design of a network heavily uses optimization tools to keep costs as low as possible. In this thesis, we analyze several aspects of the design and deployment of communication networks. First, we present a new network design problem used to serve wireless demands from mobile devices and route the traffic to the core network. Such access networks are based on modern wireless access technologies such as Wi-Fi, LTE, and HSPA. This problem has several real world constraints and it is hard to solve. We study real cases of the vicinity of a large city in the United States. Following, we present a variation of the hub-location problem used to model these core networks. Such problem is also suitable to model transportation networks. We also study the overlapping correlation clustering problem used to model the user's behavior when using their mobile devices. In such problem, one can label an object with multiple labels and analyzes the connections between them. Although this problem is very generic, it is suitable to analyze device mobility which can be used to estimate traffic in geographical regions. Finally, we analyze spectrum licensing from a governmental perspective. In these cases, a governmental agency wants to sell rights for telecommunication companies to operate over a given spectrum range. This process usually is conducted using combinatorial auctions. For all problems we propose biased random-key genetic algorithms and mixed integer linear programming models (except in the case of the overlapping correlation clustering problem due its non-linear nature). Our algorithms were able to overcome the state of the art algorithms for all problemsDoutoradoCiência da ComputaçãoDoutor em Ciência da Computaçã

Swarm Intelligent in Bio-Inspired Perspective: A Summary

This paper summarizes the research performed in the field of swarm intelligent in recent years. The classification of swarm intelligence based on behavior is introduced. The principles of each behaviors, i.e. foraging, aggregating, gathering, preying, echolocation, growth, mating, clustering, climbing, brooding, herding, and jumping are described. 3 algorithms commonly used in swarm intelligent are discussed. At the end of summary, the applications of the SI algorithms are presented

Entropy-based framework for combinatorial optimization problems and enabling the grid of the future

This thesis is divided into two parts. In the first part, I describe efficient meta-heuristic algorithms for a series of combinatorially complex optimization problems, while the second part is concerned with robust and scalable control architecture for a network of paralleled converter/inverter systems (DC/AC microgrids). Combinatorial optimization problems arise in many applications in various forms in seemingly unrelated areas such as data compression, pattern recognition, image segmentation, resource allocation, routing, and scheduling, graph aggregation, and graph partition problems. These optimization problems are characterized by a combinatorial number of configurations, where a cost value can be assigned to each configuration, and the goal is to find the configuration that minimizes the cost. Moreover, these optimization problems are largely non-convex, computationally complex and suffer from multiple local minima that riddle the cost surface. Most heuristics to these optimization problems are very sensitive to initial guess solutions, and efforts to make them robust to initializations typically come at significant computational costs such that the algorithms lose practicality in many applications. In our work, we are motivated by solutions that are employed by nature to similar combinatorial optimization problems; well described in terms of laws such as maximum entropy principle (MEP) in statistical physics literature. We propose to use MEP in solving a variety of combinatorial optimization problems. Our main current contributions are threefold - (i) First we provide a clustering or resource allocation viewpoint to several combinatorial optimization problems: (a) data clustering, (b) graph partitioning (such as clustering of power networks), (c) traveling salesman problem (TSP) and its variants, and (d) hard problems on graphs, such as multiway $k$-cut. This viewpoint enables a unified approach to handle a broad class of problems, and therefore efficient MEP based heuristics can be leveraged to obtain high-quality solutions. (ii) Second, we explore MEP based ideas to clustering problems specified by pairwise distances. Many problems in graph theory are indeed specified in terms of the corresponding edge-weight matrices (and not in terms of the nodal locational coordinates). (iii) Finally, our framework allows for inclusion of several constraints in the clustering/resource allocation problems. These constraints may correspond to capacity constraints in case of resource allocations where capacity of each resource is limited, or minimum-tour length constraints in case of traveling salesman problems (TSPs) and its variants. In the second part of this thesis, we describe a novel distributed, robust and optimal control architecture for both DC as well as AC microgrids. Microgrids are grid systems that allow integration of local power sources, such as photovoltaics (PVs), wind, battery and other distributed energy resources (DERs) with local loads connected at the DC-link or the point of common coupling (PCC). Microgrids are hypothesized as viable alternatives to the traditional electric grid. In a microgrid, the main goals of the control design are to regulate voltage and frequency at the PCC and ensuring a prescribed sharing of power among different sources; for instance, economic considerations can dictate that power provided by the sources should be in a certain proportion or according to a prescribed priority. The main challenges arise from the uncertainties in the size and the schedules of loads, the complexity of a coupled multi-converter network, the uncertainties in the model parameters at each converter, and the adverse effects of interfacing DC power sources with AC loads, such as the $120$Hz ripple that must be provided by the DC sources. A systematic control design that addresses all the challenges and objectives for the multi-converter/inverter control is still lacking in the existing literature. The main contribution of the control architecture proposed by us is its capability to addresses all the primary objectives - a) voltage and frequency regulation at the PCC with guaranteed robustness margins, b) prescribed time-varying power sharing in a network of parallel converters, c) controlling the tradeoff between 120Hz ripple on the total current provided by the power sources and the ripple on the DC-link voltage. An important contribution of our work is that our control architecture allows for closed-loop analysis and robust control synthesis for the entire grid network. We introduce a structure in the control architecture, whereby, we show that analysis of the entire multi-component microgrid can be simplified to that of an equivalent {\em single-component} system. Besides analysis, this simplification facilitates using robust and optimal control tools for achieving multiple objectives simultaneously; in contrast in existing architectures, closed-loop analysis for entire networks is typically difficult, and posing optimal control and robustness objectives for the entire network practically untenable

Efficient Fuel Consumption Minimization for Green Vehicle Routing Problems using a Hybrid Neural Network-Optimization Algorithm

Efficient routing optimization yields benefits that extend beyond mere financial gains. In this thesis, we present a methodology that utilizes a graph convolutional neural network to facilitate the development of energy-efficient waste collection routes. Our approach focuses on a Waste company in Tromsø, Remiks, and uses real-life datasets, ensuring practicability and ease of implementation. In particular, we extend the dpdp algorithm introduced by Kool et al. (2021) [1] to minimize fuel consumption and devise routes that account for the impact of elevation and real road distance traveled. Our findings shed light on the potential advantages and enhancements these optimized routes can offer Remiks, including improved effectiveness and cost savings. Additionally, we identify key areas for future research and development