A greedy randomized adaptive search procedure (GRASP) is a metaheuristic for combinatorial optimization. GRASP heuristics are multistart procedures which apply local search to a set of starting solutions generated with a randomized greedy algorithm or semi-greedy method. The best local optimum found over the iterations is returned as the heuristic solution. Path-relinking is a search … Read more
The Capacitated Arc Routing Problem (CARP) stands among the hardest combinatorial problems to solve or to find high quality solutions. This becomes even more true when dealing with large instances. This paper investigates methods to improve on lower and upper bounds of instances on graphs with over two hundred vertices and three hundred edges, dimensions … Read more
In this paper, we describe brkgaAPI, an efficient and easy-to-use object oriented application programming interface for the algorithmic framework of biased random-key genetic algorithms. Our cross-platform library automatically handles the large portion of problem-independent modules that are part of the framework, including population management and evolutionary dynamics, leaving to the user the task of implementing … Read more
The document describes COIN-OR METSlib, a C++ framework for local search based metaheuristics. METSlib has been used to implement a massively parallel VRP algorithm, a state of the art Vertex Coloring Problem solver, a Timetabling software, and in many other projects. ArticleDownload View PDF
In this paper we propose an adaptation of the GRASP metaheuristic to solve multi-objective combinatorial optimization problems. In particular we describe several alternatives to specialize the construction and improvement components of GRASP when two or more objectives are considered. GRASP has been successfully coupled with path-relinking for single-objective optimization. In this paper, we propose different … Read more
In many telecommunication network architectures a given set of client nodes must be served by different kinds of facility, which provide di fferent services and have diff erent capabilities. Such facilities must be located and dimensioned in the design phase. We tackle a particular location problem in which two sets of facilities, mid level and high level, … Read more
The use of metaheuristics in Multi-objective Combinatorial Optimization, particularly Ant Colony Optimization (ACO), has grown recently. This paper proposes an approach where multi-species ants compete for food resources. Each species has its own search strategy and do not access pheromone information of other species. As in nature, successful ant populations are allowed to grow, whereas … Read more
This paper presents a local search, based on a new neighborhood for the job-shop scheduling problem, and its application within a biased random-key genetic algorithm. Schedules are constructed by decoding the chromosome supplied by the genetic algorithm with a procedure that generates active schedules. After an initial schedule is obtained, a local search heuristic, based … Read more
This paper presents a local search, based on a new neighborhood for the job-shop scheduling problem, and its application within a biased random-key genetic algorithm. Schedules are constructed by decoding the chromosome supplied by the genetic algorithm with a procedure that generates active schedules. After an initial schedule is obtained, a local search heuristic, based … Read more
In this paper we propose the approach to solving several combinatorial optimization problems using local search and genetic algorithm techniques. Initially this approach was developed in purpose to overcome some difficulties inhibiting the application of above-mentioned techniques to the problems of the Questionnaire Theory. But when the algorithms were developed it became clear that them … Read more