María Soto

A mathematical model and a metaheuristic approach for a memory allocation problem

Memory allocation in embedded systems is one of the main challenges that electronic designers have to face. This part, rather difficult to handle is often left to the compiler with which automatic rules are applied. Nevertheless, an optimal allocation of data to memory banks may lead to great savings in terms of running time and energy consumption. This paper introduces an exact approach and a vns-based metaheuristic for addressing a memory allocation problem. Numerical experiments have been conducted on real instances from the electronic community and on dimacs instances expanded for our specific problem.

Multiple neighborhood search, tabu search and ejection chains for the multi-depot open vehicle routing problem

The proposed approach solves the Multi-Depot Open VRP.An uniform view of ejection chains is adopted for all neighborhoods.Multiple Neighborhood Search (MNS) and Tabu Search (TS) are combined.MNS-TS outperforms the state-of-the-art methods. In this paper, we address the Multi-Depot Open Vehicle Routing Problem (MDOVRP), which is a generalization of the Capacitated Vehicle Routing Problem (CVRP) where vehicles start from different depots, visit customers, deliver goods and are not required to return to the depot at the end of their routes. The goal of this paper is twofold. First, we have developed a general Multiple Neighborhood Search hybridized with a Tabu Search (MNS-TS) strategy which is proved to be efficient and second, we have settled an unified view of ejection chains to be able to handle several neighborhoods in a simple manner. The neighborhoods in the proposed MNS-TS are generated from path moves and ejection chains. The numerical and statistical tests carried out over OVRP and MDOVRP problem instances from the literature show that MNS-TS outperforms the state-of-the-art methods.

GRASP with ejection chains for the dynamic memory allocation in embedded systems

In the design of electronic embedded systems, the allocation of data structures to memory banks is a main challenge faced by designers. Indeed, if this optimization problem is solved correctly, a great improvement in terms of efficiency can be obtained. In this paper, we consider the dynamic memory allocation problem, where data structures have to be assigned to memory banks in different time periods during the execution of the application. We propose a GRASP to obtain high quality solutions in short computational time, as required in this type of problem. Moreover, we also explore the adaptation of the ejection chain methodology, originally proposed in the context of tabu search, for improved outcomes. Our experiments with real and randomly generated instances show the superiority of the proposed methods compared to the state-of-the-art method.

Two iterative metaheuristic approaches to dynamic memory allocation for embedded systems

Electronic embedded systems designers aim at finding a tradeoff between cost and power consumption. As cache memory management has been shown to have a significant impact on power consumption, this paper addresses dynamic memory allocation for embedded systems with a special emphasis on time performance. In this work, time is split into time intervals, into which the application to be implemented by the embedded system requires accessing to data structures. The proposed iterative metaheuristics aim at determining which data structure should be stored in cache memory at each time interval in order to minimize reallocation and conflict costs. These approaches take advantage of metaheuristics previously designed for a static memory allocation problem.

Iterative approaches for a dynamic memory allocation problem in embedded systems

Memory allocation has a significant impact on energy consumption in embedded systems. In this paper, we are interested in dynamic memory allocation for embedded systems with a special emphasis on time performance. We propose two mid-term iterative approaches which are compared with existing long-term and short-term approaches, and with an ILP formulation as well. These approaches rely on solving a static version of the allocation problem and they take advantage of previous works for addressing the static problem. A statistic analysis is carried out for showing that the mid-term approach is the best one in terms of solution quality.

A multiple neighborhood search for dynamic memory allocation in embedded systems

Memory allocation has a significant impact on power consumption in embedded systems. We address the dynamic memory allocation problem, in which memory requirements may change at each time interval. This problem has previously been addressed using integer linear programming and iterative approaches which build a solution interval by interval taking into account the requirements of partial time intervals. A GRASP that builds a solution for all time intervals has been proposed as a global approach. Due to the complexity of this problem, the GRASP algorithm solution quality decreases for larger instances. In order to overcome this drawback, we propose a multiple neighborhood search hybridized with a Tabu Search and enhanced by complex ejection chains. The proposed approach outperforms all previously developed methods devised for the dynamic memory allocation problem.

Three new upper bounds on the chromatic number

This paper introduces three new upper bounds on the chromatic number, without making any assumptions on the graph structure. The first one, @x, is based on the number of edges and nodes, and is to be applied to any connected component of the graph, whereas @z and @h are based on the degree of the nodes in the graph. The computation complexity of the three-bound computation is assessed. Theoretical and computational comparisons are also made with five well-known bounds from the literature, which demonstrate the superiority of the new upper bounds.