Search results for "Guided Local Search"
showing 6 items of 16 documents
Using penalties instead of rewards: Solving OCST problems with guided local search
2012
Abstract This paper considers the optimal communication spanning tree (OCST) problem. Previous work analyzed features of high-quality solutions and found that edges in optimal solutions have low weight and point towards the center of a tree. Consequently, integrating this problem-specific knowledge into a metaheuristic increases its performance for the OCST problem. In this paper, we present a guided local search (GLS) approach which dynamically changes the objective function to guide the search process into promising areas. In contrast to traditional approaches which reward promising solution features by favoring edges with low weights pointing towards the tree’s center, GLS penalizes low-…
Resource-constrained project scheduling: A critical activity reordering heuristic
2003
Abstract In this paper, we present a new metaheuristic algorithm for the resource-constrained project-scheduling problem. The procedure is a non-standard implementation of fundamental concepts of tabu search without explicitly using memory structures embedded in a population-based framework. The procedure makes use of a fan search strategy to intensify the search, whereas a strategic oscillation mechanism loosely related to the forward/backward technique provides the necessary diversification. Our implementation employs the topological order (TO) representation of schedules. To explore the TO vector space we introduce three types of moves, two of them based on the concept of relative critic…
Measuring the Spatial Dispersion of Evolutionary Search Processes: Application to Walksat
2002
In this paper, we propose a simple and efficient method for measuring the spatial dispersion of a set of points in a metric space. This method allows the quantifying of the population diversity in genetic algorithms. It can also be used to measure the spatial dispersion of any local search process during a specified time interval. We then use this method to study the way Walksat explores its search space, showing that the search for a solution often includes several stages of intensification and diversification.
Parallel Random Search and Tabu Search for the Minimal Consistent Subset Selection Problem
1998
The Minimal Consistent Subset Selection (MCSS) problem is a discrete optimization problem whose resolution for large scale instances requires a prohibitive processing time. Prior algorithms addressing this problem are presented. Randomization and approximation techniques are suitable to face the problem, then random search and meta-heuristics are proposed and consequently Tabu Search strategies are applied and evaluated. Parallel computing helps to reduce processing time and/or produce better results; different approaches for designing parallel tabu search are analyzed.
A tabu search algorithm for assigning teachers to courses
2002
In this paper we deal with the problem of assigning teachers to courses in a secondary school. The problem appears when a timetable is to be built and the teaching assignments are not fixed. We have developed a tabu search algorithm to solve the problem. The parameters involved in the algorithm have been estimated by using multiple regression techniques. The computational results, obtained on a set of Spanish secondary schools, show that the solutions obtained by this automatic procedure can be favourably compared with the solutions proposed by the experts.
A hybrid evolution strategy for the open vehicle routing problem
2010
This paper presents a hybrid evolution strategy (ES) for solving the open vehicle routing problem (OVRP), which is a well-known combinatorial optimization problem that addresses the service of a set of customers using a homogeneous fleet of non-depot returning capacitated vehicles. The objective is to minimize the fleet size and the distance traveled. The proposed solution method manipulates a population of @m individuals using a (@m+@l)-ES; at each generation, a new intermediate population of @l offspring is produced via mutation, using arcs extracted from parent individuals. The selection and combination of arcs is dictated by a vector of strategy parameters. A multi-parent recombination …