Search results for "Metaheuristics"

An ILS-Based Metaheuristic for the Stacker Crane Problem

2012

[EN] In this paper we propose a metaheuristic algorithm for the Stacker Crane Problem. This is an NP-hard arc routing problem whose name derives from the practical problem of operating a crane. Here we present a formulation and a lower bound for this problem and propose a metaheuristic algorithm based on the combination of a Multi-start and an Iterated Local Search procedures. Computational results on a large set of instances are presented.

Solving dynamic memory allocation problems in embedded systems with parallel variable neighborhood search strategies

2015

International audience; Embedded systems have become an essential part of our lives, thanks to their evolution in the recent years, but the main drawback is their power consumption. This paper is focused on improving the memory allocation of embedded systems to reduce their power consumption. We propose a parallel variable neighborhood search algorithm for the dynamic memory allocation problem, and compare it with the state of the art. Computational results and statistical tests applied show that the proposed algorithm produces significantly better outcomes than the previous algorithm in shorter computing time.

Minimizing weighted earliness-tardiness on parallel machines using hybrid metaheuristics

2015

We consider the problem of scheduling a set of jobs on a set of identical parallel machines where the objective is to minimize the total weighted earliness and tardiness penalties with respect to a common due date. We propose a hybrid heuristic algorithm for constructing good solutions, combining priority rules for assigning jobs to machines and a local search with exact procedures for solving the one-machine subproblems. These solutions are then used in two metaheuristic frameworks, Path Relinking and Scatter Search, to obtain high quality solutions for the problem. The algorithms are tested on a large number of test instances to assess the efficiency of the proposed strategies. The result…

Max–min dispersion with capacity and cost for a practical location problem

2022

Diversity and dispersion problems deal with selecting a subset of elements from a given set in such a way that their diversity is maximized. This study considers a practical location problem recently proposed in the context of max–min dispersion models. It is called the generalized dispersion problem, and it models realistic applications by introducing capacity and cost constraints. We propose two effective linear formulations for this problem, and develop a hybrid metaheuristic algorithm based on the variable neighborhood search methodology, to solve real instances. Extensive numerical computational experiments are performed to compare our hybrid metaheuristic with the state-of-art heurist…

Herramientas matemáticas para la valoración de la ampliación de una infraestructura portuaria

2003

infraestructura portuaria ya consolidada, que conlleva unas inversiones a largo plazo. Para ello hay que recurrir a medios de an´alisis capaces de recoger, en la medida de lo posible, la incertidumbre sobre la futura evoluci´on de los tr´aficos de mercanc´?as, sobre el efecto de la competencia entre puertos, etc., y que los m´etodos tradicionales no aproximan en toda su dimensi´on. Existe, adem´as, un problema de decisi´on de pol´?tica ´optima de gesti´on del proyecto que depende de variables de decisi´on que modelizan las opciones presentes en el mismo. Las oportunidades de inversi´on han sido tratadas como una colecci´on de opciones americanas sobre activos reales. Nosotros hemos optado p…

Parallel global optimization : structuring populations in differential evolution

2010

On automatic algorithm configuration of vehicle routing problem solvers

2019

Many of the algorithms for solving vehicle routing problems expose parameters that strongly influence the quality of obtained solutions and the performance of the algorithm. Finding good values for these parameters is a tedious task that requires experimentation and experience. Therefore, methods that automate the process of algorithm configuration have received growing attention. In this paper, we present a comprehensive study to critically evaluate and compare the capabilities and suitability of seven state-of-the-art methods in configuring vehicle routing metaheuristics. The configuration target is the solution quality of eight metaheuristics solving two vehicle routing problem variants.…

Context-Aware Adaptive System For M- Learning Personalization

2014

International audience; Context-aware mobile learning is becoming important because of the dynamic and continually changing learning settings in learner's mobile environment, giving rise to many different learning contexts that are difficult to apprehend. To provide personalization of learning content, we aim to develop a recommender system based on semantic modeling of learning contents and learning context. This modeling is complemented by a behavioral part made up of rules and metaheuristics used to optimize the combination of pieces of learning contents according to learner's context. All these elements form a new approach to mobile learning.

SEMANTIC AND CONTEXTUAL APPROACH FOR THE RECOMMENDATION OF LEARNING MODULES IN MOBILITY

2012

International audience; Many researchers argue that mobile learning is just an adaptation of e-learning on mobile technology, but far from a simple extension of e-learning, m-learning raises original issues in technological and pedagogical terms. M-learning is usually based on the consideration of a context rich on information and interactions. The challenge of m-learning is therefore, not simply to transfer on mobile content designed primarily for e-learning. This concept implies that we must rethink the entire process of the learning experience in mobility to maximize its efficiency.

Recommender system for combination of learning elements in mobile environment

2012

5 pages; International audience; The paper presents an ongoing research about the development of a new recommender system dedicated to m-learning. This system is an extension of content based recommender system proposals. It's made of three levels architecture: 1/ a domain model describing the knowledge of teaching, 2/ a user model defining learner's profile and learning's context, 3/ an adaptation model containing rules and metaheuristics, which aims at combining learning modules. Our system takes into account the spatio-temporal context of the learners, the evolution of learner's profile and the dynamic adaptation of modules during the learning process in a mobile environment. The result …