Algoritmo evolutivo paralelo para o problema de atribuição de localidades a anéis em redes sonet/sdh

The telecommunications play a fundamental role in the contemporary society, having as one of its main roles to give people the possibility to connect them and integrate them into society in which they operate and, therewith, accelerate development through knowledge. But as new technologies are introduced on the market, increases the demand for new products and services that depend on the infrastructure offered, making the problems of planning of telecommunication networks become increasingly large and complex. Many of these problems, however, can be formulated as combinatorial optimization models, and the use of heuristic algorithms can help solve these issues in the planning phase. This paper proposes the development of a Parallel Evolutionary Algorithm to be applied to telecommunications problem known in the literature as SONET Ring Assignment Problem SRAP. This problem is the class NP-hard and arises during the physical planning of a telecommunication network and consists of determining the connections between locations (customers), satisfying a series of constrains of the lowest possible cost. Experimental results illustrate the effectiveness of the Evolutionary Algorithm parallel, over other methods, to obtain solutions that are either optimal or very close to it

Um algoritmo híbrido para o problema de roteamento de veículos com frotas heterogêneas

This paper aims to propose a hybrid meta-heuristics for the Heterogeneous Fleet Vehicle Routing Problem (HVRP), which is a combinatorial optimization problem NP-hard, and is characterized by the use of a limited fleet consists of different vehicles with different capacities. The hybrid method developed makes use of a memetic algorithm associated with the component optimizer Vocabulary Building. The resulting hybrid meta-heuristic was implemented in the programming language C + + and computational experiments generated good results in relation to meta-heuristic applied in isolation, proving the efficiency of the proposed method.

Metaheurísticas evolutivas para o problema de roteamento de unidades móveis de pistoneio

This paper presents metaheuristic strategies based on the framework of evolutionary algorithms (Genetic and Memetic) with the addition of Technical Vocabulary Building for solving the Problem of Optimizing the Use of Multiple Mobile Units Recovery of Oil (MRO units). Because it is an NP-hard problem, a mathematical model is formulated for the problem, allowing the construction of test instances that are used to validate the evolutionary metaheuristics developed

Algoritmo memético com infecção viral: uma aplicação ao problema do caixeiro viajante assimétrico

The Combinatorial Optimization is a basic area to companies who look for competitive advantages in the diverse productive sectors and the Assimetric Travelling Salesman Problem, which one classifies as one of the most important problems of this area, for being a problem of the NP-hard class and for possessing diverse practical applications, has increased interest of researchers in the development of metaheuristics each more efficient to assist in its resolution, as it is the case of Memetic Algorithms, which is a evolutionary algorithms that it is used of the genetic operation in combination with a local search procedure. This work explores the technique of Viral Infection in one Memetic Algorithms where the infection substitutes the mutation operator for obtaining a fast evolution or extinguishing of species (KANOH et al, 1996) providing a form of acceleration and improvement of the solution . For this it developed four variants of Viral Infection applied in the Memetic Algorithms for resolution of the Assimetric Travelling Salesman Problem where the agent and the virus pass for a symbiosis process which favored the attainment of a hybrid evolutionary algorithms and computational viable

The open capacitated arc routing problem

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Multi-population genetic algorithm to solve the synchronized and integrated two-level lot sizing and scheduling problem

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

A Beam Search Method to Solve the Problem of Assignment Cells to Switches in a Cellular Mobile Network

Assigning cells to switches in a cellular mobile network is known as an NP-hard optimization problem. This means that the alternative for the solution of this type of problem is the use of heuristic methods, because they allow the discovery of a good solution in a very satisfactory computational time. This paper proposes a Beam Search method to solve the problem of assignment cell in cellular mobile networks. Some modifications in this algorithm are also presented, which allows its parallel application. Computational results obtained from several tests confirm the effectiveness of this approach and provide good solutions for large scale problems.

The minimization of open stacks problem: A review of some properties and their use in pre-processing operations

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Beam Search Algorithms for Minimizing Tool Switches on a Flexible Manufacturing System

In the minimization of tool switches problem we seek a sequence to process a set of jobs so that the number of tool switches required is minimized. In this work different variations of a heuristic based on partial ordered job sequences are implemented and evaluated. All variations adopt a depth first strategy of the enumeration tree. The computational test results indicate that good results can be obtained by a variation which keeps the best three branches at each node of the enumeration tree, and randomly choose, among all active nodes, the next node to branch when backtracking.

Algoritmos evolutivos dedicados à reconfiguração de redes radiais de distribuição sob demandas fixas e variáveis: estudo dos operadores genéticos e parâmetros de controle

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

A Strategy to Solve the Multistage Transmission Expansion Planning Problem

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Arquitetura multiagente baseada em nuvem de partículas para hibridização de metaheurísticas

This thesis proposes an architecture of a new multiagent system framework for hybridization of metaheuristics inspired on the general Particle Swarm Optimization framework (PSO). The main contribution is to propose an effective approach to solve hard combinatory optimization problems. The choice of PSO as inspiration was given because it is inherently multiagent, allowing explore the features of multiagent systems, such as learning and cooperation techniques. In the proposed architecture, particles are autonomous agents with memory and methods for learning and making decisions, using search strategies to move in the solution space. The concepts of position and velocity originally defined in PSO are redefined for this approach. The proposed architecture was applied to the Traveling Salesman Problem and to the Quadratic Assignment Problem, and computational experiments were performed for testing its effectiveness. The experimental results were promising, with satisfactory performance, whereas the potential of the proposed architecture has not been fully explored. For further researches, the proposed approach will be also applied to multiobjective combinatorial optimization problems, which are closer to real-world problems. In the context of applied research, we intend to work with both students at the undergraduate level and a technical level in the implementation of the proposed architecture in real-world problems

Uma análise experimental de abordagens heurísticas aplicadas ao problema do caixeiro viajante

Due to great difficulty of accurate solution of Combinatorial Optimization Problems, some heuristic methods have been developed and during many years, the analysis of performance of these approaches was not carried through in a systematic way. The proposal of this work is to make a statistical analysis of heuristic approaches to the Traveling Salesman Problem (TSP). The focus of the analysis is to evaluate the performance of each approach in relation to the necessary computational time until the attainment of the optimal solution for one determined instance of the TSP. Survival Analysis, assisted by methods for the hypothesis test of the equality between survival functions was used. The evaluated approaches were divided in three classes: Lin-Kernighan Algorithms, Evolutionary Algorithms and Particle Swarm Optimization. Beyond those approaches, it was enclosed in the analysis, a memetic algorithm (for symmetric and asymmetric TSP instances) that utilizes the Lin-Kernighan heuristics as its local search procedure

Uma abordagem por nuvem de partículas para problemas de otimização combinatória

Combinatorial optimization problems have the goal of maximize or minimize functions defined over a finite domain. Metaheuristics are methods designed to find good solutions in this finite domain, sometimes the optimum solution, using a subordinated heuristic, which is modeled for each particular problem. This work presents algorithms based on particle swarm optimization (metaheuristic) applied to combinatorial optimization problems: the Traveling Salesman Problem and the Multicriteria Degree Constrained Minimum Spanning Tree Problem. The first problem optimizes only one objective, while the other problem deals with many objectives. In order to evaluate the performance of the algorithms proposed, they are compared, in terms of the quality of the solutions found, to other approaches

Algoritomos transgenéticos aplicados ao problema da árvore geradora biobjetivo

The Multiobjective Spanning Tree is a NP-hard Combinatorial Optimization problem whose application arises in several areas, especially networks design. In this work, we propose a solution to the biobjective version of the problem through a Transgenetic Algorithm named ATIS-NP. The Computational Transgenetic is a metaheuristic technique from Evolutionary Computation whose inspiration relies in the conception of cooperation (and not competition) as the factor of main influence to evolution. The algorithm outlined is the evolution of a work that has already yielded two other transgenetic algorithms. In this sense, the algorithms previously developed are also presented. This research also comprises an experimental analysis with the aim of obtaining information related to the performance of ATIS-NP when compared to other approaches. Thus, ATIS-NP is compared to the algorithms previously implemented and to other transgenetic already presented for the problem under consideration. The computational experiments also address the comparison to two recent approaches from literature that present good results, a GRASP and a genetic algorithms. The efficiency of the method described is evaluated with basis in metrics of solution quality and computational time spent. Considering the problem is within the context of Multiobjective Optimization, quality indicators are adopted to infer the criteria of solution quality. Statistical tests evaluate the significance of results obtained from computational experiments