Browsing by Keyword "Ant colony optimization"
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Item Implementable hybrid quantum ant colony optimization algorithm(Springer Science and Business Media Deutschland GmbH, 2022-06-17) de Andoin, M. Garcia; Echanobe, J.We propose a new hybrid quantum algorithm based on the classical Ant Colony Optimization algorithm to produce approximate solutions for NP-hard problems, in particular optimization problems. First, we discuss some previously proposed Quantum Ant Colony Optimization algorithms, and based on them, we develop an improved algorithm that can be truly implemented on near-term quantum computers. Our iterative algorithm codifies only the information about the pheromones and the exploration parameter in the quantum state, while subrogating the calculation of the numerical result to a classical computer. A new guided exploration strategy is used in order to take advantage of the quantum computation power and generate new possible solutions as a superposition of states. This approach is specially useful to solve constrained optimization problems, where we can implement efficiently the exploration of new paths without having to check the correspondence of a path to a solution before the measurement of the state. As an example of a NP-hard problem, we choose to solve the Quadratic Assignment Problem. The benchmarks made by simulating the noiseless quantum circuit and the experiments made on IBM quantum computers show the validity of the algorithmItem Learning semantically-annotated routes for context-aware recommendations on map navigation systems(2012-09) Mocholi, Jose A.; Jaen, Javier; Krynicki, Kamil; Catala, Alejandro; Picón, Artzai; Cadenas, Alejandro; COMPUTER_VISIONModern technology has brought many changes to our everyday lives. Our need to be in constant touch with others has been met with the cellphone, which has become our companion and the convergence point of many technological advances. The combination of capabilities such as browsing the Internet and GPS reception has multiplied the services and applications based on the current location of the user. However, providing the user with these services has certain drawbacks. Although map navigation systems are the most meaningful way of displaying this information, the user still has to manually set up the filter in order to obtain a non-bloated visualization of the map and the available services. To tackle this problem, we present here a semantic multicriteria ant colony algorithm capable of learning the user's routes, including associated context information, and then predicting the most likely route a user is following, given his current location and context data. This knowledge could then be used as the basis for offering services related to his current (or most likely future) context data close to the path he is following. Our experimental results show that our algorithm is capable of obtaining consistent solutions sets even when multiple objective ontological terms are included in the process.Item On the Applicability of Ant Colony Optimization to Non-Intrusive Load Monitoring in Smart Grids(Springer Berlin Heidelberg, 2015-11) Gonzalez-Pardo, Antonio; Del Ser, Javier; Camacho, David; Puerta, José M.; Gámez, José A.; Dorronsoro, Bernabé; Baruque, Bruno; Troncoso, Alicia; Barrenechea, Edurne; Galar, Mikel; IAAlong with the proliferation of the Smart Grid, power load disaggregation is a research area that is lately gaining a lot of popularity due to the interest of energy distribution companies and customers in identifying consumption patterns towards improving the way the energy is produced and consumed (via e.g. demand side management strategies). Such data can be extracted by using smartmeters, but the expensive cost of incorporating a monitoring device for each appliance jeopardizes significantly the massive implementation of any straightforward approach. When resorting to a single meter to monitor the global consumption of the house at hand, the identification of the different appliances giving rise to the recorded consumption profile renders a particular instance of the so-called source separation problem, for which a number of algorithmic proposals have been reported in the literature. This paper gravitates on the applicability of the Ant Colony Optimization (ACO) algorithm to perform this power disaggregation treating the problem as a Constraint Satisfaction Problem (CSP). The discussed experimental results utilize data contained in the REDD dataset, which corresponds to real power consumption traces of different households. Although the experiments carried out in this work reveal that the ACO solver can be successfully applied to the Non-Intrusive Load Monitoring problems, further work is needed towards assessing its performance when tackling more diversea ppliance models and noisy power load traces.