Search results for "Metaheuristic"
showing 10 items of 153 documents
Connections with Other Population-Based Approaches
2003
Throughout this book, we have established that scatter search (SS) belongs to the family of population-based metaheuristics. This family also includes the well-known evolutionary algorithms and the approach known as path relinking.
A Primer on Memetic Algorithms
2012
Memetic Algorithms (MAs) are population-based metaheuristics composed of an evolutionary framework and a set of local search algorithms which are activated within the generation cycle of the external framework, see [376]. The earliest MA implementation has been given in [621] in the context of the Travelling Salesman Problem (TSP) while an early systematic definition has been presented in [615]. The concept of meme is borrowed from philosophy and is intended as the unit of cultural transmission. In other words, complex ideas can be decomposed into memes which propagate andmutate within a population.Culture, in this way, constantly undergoes evolution and tends towards progressive improvemen…
3D inter-subject medical image registration by scatter search
2005
Image registration is a very active research area in computer vision, namely it is used to find a transformation between two images taken under different conditions. Point matching is an image registration approach based on searching for the right pairing of points between the two images. From this matching, the registration transformation we are searching, can be inferred by means of numerical methods. In this paper, we propose a scatter search (SS) algorithm to solve the matching problem. SS is a hybrid metaheuristic with a good trade-off between search space diversification and intensification. On the one hand, diversity is basically introduced from a population-based approach where syst…
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.
Metaheuristics meet metamodels : a modeling language and a product line architecture for route optimization systems
2011
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.