Browsing by Keyword "Harmony Search"
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Item Centralized and distributed spectrum channel assignment in cognitive wireless networks: A Harmony Search approach(2012-02) Del Ser, Javier; Matinmikko, Marja; Gil-López, Sergio; Mustonen, Miia; IAThis paper gravitates on the spectrum channel allocation problem where each compounding node of a cognitive radio network is assigned a frequency channel for transmission over a given outgoing link, based on optimizing an overall network performance metric dependant on the level of interference among nearby nodes. In this context, genetically inspired algorithms have been extensively used so far for solving this optimization problem in a computationally efficient manner. This work extends previous preliminary research carried out by the authors on the application of the heuristic Harmony Search (HS) algorithm to this scenario by presenting further results and derivations on both HS-based centralized and distributed spectrum allocation techniques. Among such advances, a novel adaptive island-like distributed allocation procedure is presented, which dramatically decreases the transmission rate required for exchanging control traffic among nodes at a quantifiable yet negligible performance penalty. Extensive simulation results executed over networks of increasing size verify, on one hand, that our proposed technique achieves near-optimum spectral channel assignments at a low computational complexity. On the other hand, the obtained results assess that HS vastly outperforms genetically inspired allocation algorithms for the set of simulated scenarios. Finally, the proposed adaptive distributed allocation approach is shown to attain a control traffic bandwidth saving of more than 90 with respect to the naive implementation of a HS-based island allocation procedure.Item A Coral Reefs Optimization algorithm with Harmony Search operators for accurate wind speed prediction(2015-03-01) Salcedo-Sanz, Sancho; Pastor-Sanchez, Alvaro; Del Ser, Javier; Prieto, Luis; Geem, Zong-Woo; IAThis paper introduces a new hybrid bio-inspired solver which combines elements from the recently proposed Coral Reefs Optimization (CRO) algorithm with operators from the Harmony Search (HS) approach, which gives rise to the coined CRO-HS optimization technique. Specifically, this novel bio-inspired optimizer is utilized in the context of short-term wind speed prediction as a means to obtain the best set of meteorological variables to be input to a neural Extreme Learning Machine (ELM) network. The paper elaborates on the main characteristics of the proposed scheme and discusses its performance when predicting the wind speed based on the measures of two meteorological towers located in USA and Spain. The good results obtained in these experiments when compared to naïve versions of the CRO and HS algorithms are promising and pave the way towards the utilization of the derived hybrid solver in other optimization problems arising from diverse disciplines.Item A grouping harmony search approach for the Citywide WiFi deployment problem(2011) Landa-Torres, Itziar; Gil-Lopez, Sergio; Del Ser, Javier; Salcedo-Sanz, Sancho; Manjarres, Diana; Portilla-Figueras, J. A.; Tecnalia Research & Innovation; IAThis paper presents a novel Grouping Harmony Search (GHS) algorithm for the Citywide Ubiquitous WiFi Network Design problem (WIFIDP). The WIFIDP is a NP-hard problem where private customers owning wireless access points connected to Internet share bandwidth with third parties. Aspects such as allocated budget and router capacities (coverage radius, capacity, price, etc) are taken into account in order to obtain the optimal network deployment (in terms of cost-effectiveness) when applying the GHS algorithm. The approach to tackle the aforementioned WIFIDP problem consists of a hybrid Grouping Harmony Search (GHS) algorithm with a local search method and a technique for repairing unfeasible solutions. Furthermore, the presented GHS algorithm is differential, since each proposed harmony is produced (improvised) based on the same harmony in the previous iteration. This differential scheme employs the grouping concept based on the connectivity between nomadic users and routers, which increases significantly its searching capability. Preliminary Monte Carlo simulations show that this proposed technique statistically outperforms genetically-inspired algorithms previously presented for the WIFIDP, with an emphasis in scenarios with stringent capacity and budget constraints. This first approach paves the way for future research aimed at applying the proposed algorithm to real scenarios.Item Hybridizing Cartesian Genetic Programming and Harmony Search for adaptive feature construction in supervised learning problems(2016) Elola, Andoni; Del Ser, Javier; Bilbao, Miren Nekane; Perfecto, Cristina; Alexandre, Enrique; Salcedo-Sanz, Sancho; IAThe advent of the so-called Big Data paradigm has motivated a flurry of research aimed at enhancing machine learning models by following very diverse approaches. In this context this work focuses on the automatic construction of features in supervised learning problems, which differs from the conventional selection of features in that new characteristics with enhanced predictive power are inferred from the original dataset. In particular this manuscript proposes a new iterative feature construction approach based on a self-learning meta-heuristic algorithm (Harmony Search) and a solution encoding strategy (correspondingly, Cartesian Genetic Programming) suited to represent combinations of features by means of constant-length solution vectors. The proposed feature construction algorithm, coined as Adaptive Cartesian Harmony Search (ACHS), incorporates modifications that allow exploiting the estimated predictive importance of intermediate solutions and, ultimately, attaining better convergence rate in its iterative learning procedure. The performance of the proposed ACHS scheme is assessed and compared to that rendered by the state of the art in a toy example and three practical use cases from the literature. The excellent performance figures obtained in these problems shed light on the widespread applicability of the proposed scheme to supervised learning with legacy datasets composed by already refined characteristics.Item A meta-heuristic learning approach for the non-intrusive detection of impersonation attacks in social networks(2017) Villar-Rodriguez, Esther; Ser, Javier Del; Gil-Lopez, Sergio; Bilbao, Miren Nekane; Salcedo-Sanz, Sancho; Quantum; IACyber attacks have recently gained momentum in the research community as a sharply concerning phenomenon further ignited by the proliferation of social networks, which unfold a variety of ways for cybercriminals to access compromised information of their users. This paper gravitates on impersonation attacks, whose motivation may go beyond economic interests of the attacker towards getting unauthorised access to information and contacts, as often occurs between teenagers and early users of social platforms. This manuscript proposes a meta-heuristically optimised learning model as the algorithmic core of a non-intrusive detection system that relies exclusively on connection time features to detect evidences of an impersonation attack. The proposed scheme hinges on the K-Means clustering approach applied to a set of time features specially tailored to characterise the usage of users, which are weighted prior to the clustering under detection performance maximisation criteria. The obtained results shed light on the potentiality of the proposed methodology for its practical application to real social networks.Item A novel grouping heuristic algorithm for the switch location problem based on a hybrid dual harmony search technique(2011) Gil-Lopez, Sergio; Landa-Torres, Itziar; Del Ser, Javier; Salcedo-Sanz, Sancho; Manjarres, Diana; Portilla-Figueras, Jose A.; IA; Tecnalia Research & InnovationThis manuscript proposes a novel iterative approach for the so-called Switch Location Problem (SLP) based on the hybridization of a group-encoded Harmony Search combinatorial heuristic (GHS) with local search and repair methods. Our contribution over other avantgarde techniques lies on the dual application of the GHS operators over both the assignment and the grouping parts of the encoded solutions. Furthermore, the aforementioned local search and repair procedures account for the compliancy of the iteratively refined candidate solutions with respect to the capacity constraints imposed in the SLP problem. Extensive simulation results done for a wide range of network instances verify that statistically our proposed dual algorithm outperforms all existing evolutionary approaches in the literature for the specific SLP problem at hand. Furthermore, it is shown that by properly selecting different yet optimized values for the operational GHS parameters to the two parts comprising the group-encoded solutions, the algorithm can trade statistical stability (i.e. lower standard deviation of the metric) for accuracy (i.e. lower minimum value of the metric) in the set of performed simulations.Item A Novel Heuristic Approach for the Simultaneous Selection of the Optimal Clustering Method and Its Internal Parameters for Time Series Data(Springer Verlag, 2020) Navajas-Guerrero, Adriana; Manjarres, Diana; Portillo, Eva; Landa-Torres, Itziar; Martínez Álvarez, Francisco; Troncoso Lora, Alicia; Sáez Muñoz, José António; Corchado, Emilio; Quintián, Héctor; IAClustering methods have become popular in the last years due to the need of analyzing the high amount of collected data from different fields of knowledge. Nevertheless, the main drawback of clustering is the selection of the optimal method along with its internal parameters in an unsupervised environment. In the present paper, a novel heuristic approach based on the Harmony Search algorithm aided with a local search procedure is presented for simultaneously optimizing the best clustering algorithm (K-means, DBSCAN and Hierarchical clustering) and its optimal internal parameters based on the Silhouette index. Extensive simulation results show that the presented approach outperforms the standard clustering configurations and also other works in the literature in different Time Series and synthetic databases.Item Novel hybrid heuristics for an extension of the dynamic relay deployment problem over disaster areas(2014-10) Bilbao, Miren Nekane; Gil-López, Sergio; Del Ser, Javier; Salcedo-Sanz, Sancho; Sánchez-Ponte, Mikel; Arana-Castro, Antonio; IA; GENERALIn this paper, we propose a novel autonomous intelligent tool for the optimum design of a wireless relayed communication network deployed over disaster areas. The so-called dynamic relay deployment problem consists of finding the optimum number of deployed relays and their location aimed at simultaneously maximizing the overall number of mobile nodes covered and minimizing the cost of the deployment. In this paper, we extend the problem by considering diverse relay models characterized by different coverage radii and associated costs. To efficiently tackle this problem we derive a novel hybrid scheme comprising: (1) a Harmony Search (HS)-based global search procedure and (2) a modified version of the well-known K-means clustering algorithm as a local search technique. Single- and bi-objective formulations of the algorithm are proposed for emergency and strategic operational planning, respectively. Monte Carlo simulations are run over a emulated scenario based on real statistical data from the Castilla La Mancha region (center of Spain) to show that, in comparison with a standard implementation of the K-means algorithm followed by a exhaustive search procedure over all relay-model combinations, the proposed scheme renders on average better coverage levels and reduced costs providing, at the same time, an intelligent tool capable of simultaneously determining the number and models of the relays to be deployed.Item A novel multi-objective algorithm for the optimal placement of wind turbines with cost and yield production criteria(IEEE Computer Society, 2014) Manjarres, Diana; Sanchez, Valentin; Del Ser, Javier; Landa-Torres, Itziar; Gil-Lopez, Sergio; Vande Walle, Naima; Guidon, Nicolaz; IA; HPA; Tecnalia Research & InnovationDuring the last years wind energy has experimented a significant growth in comparison with other types of renewable energy sources. Accordingly, the number of wind farms has increased sharply to become one of the most developed worldwide infrastructures. Unfortunately, the high number of constraints and restrictions that must be considered nowadays when designing a wind farm deployment (e.g. protected environmental areas or geographical unfeasibility) calls for tools aimed at the cost-effective optimal placement of wind farms, along with an optimized micro-siting of their compounding wind turbines. In this paper a novel multi-objective adaptation of the Harmony Search meta-heuristic algorithm is developed and tested for efficiently solving the problem of optimally deploying wind turbines in wind farms, which is accomplished by simultaneously addressing two conflicting objectives: the yield production and the capital cost of the deployment. Experimental simulation results over a certain region of the Basque Country (northern Spain) will be presented and discussed so as to shed light on the practical applicability of the derived solver.Item On the application of a hybrid harmony search algorithm to node localization in anchor-based wireless sensor networks(2011) Manjarres, Diana; Del Ser, Javier; Gil-Lopez, Sergio; Vecchio, Massimo; Landa-Torres, Itziar; Lopez-Valcarce, Roberto; IA; Tecnalia Research & InnovationIn many applications based on Wireless Sensor Networks (WSNs) with static sensor nodes, the availability of accurate location information of the network nodes may become essential. The node localization problem is to estimate all the unknown node positions, based on noisy pairwise distance measurements of nodes within range of each other. Maximum Likelihood (ML) estimation results in a non-convex problem, which is further complicated by the fact that sufficient conditions for the solution to be unique are not easily identified, especially when dealing with sparse networks. Thereby, different node configurations can provide equally good fitness results, with only one of them corresponding to the real network geometry. This paper presents a novel soft-computing localization technique based on hybridizing a Harmony Search (HS) algorithm with a local search procedure whose aim is to identify the localizability issues and mitigate its effects during the iterative process. Moreover, certain connectivity-based geometrical constraints are exploited to further reduce the areas where each sensor node can be located. Simulation results show that our approach outperforms a previously proposed meta-heuristic localization scheme based on the Simulated Annealing (SA) algorithm, in terms of both localization error and computational cost.Item On the design of a novel two-objective harmony search approach for distance- and connectivity-based localization in wireless sensor networks(2013-02) Manjarres, Diana; Del Ser, Javier; Gil-Lopez, Sergio; Vecchio, Massimo; Landa-Torres, Itziar; Salcedo-Sanz, Sancho; Lopez-Valcarce, Roberto; IA; Tecnalia Research & InnovationIn several wireless sensor network applications the availability of accurate nodes' location information is essential to make collected data meaningful. In this context, estimating the positions of all unknown-located nodes of the network based on noisy distance-related measurements (usually referred to as localization) generally embodies a non-convex optimization problem, which is further exacerbated by the fact that the network may not be uniquely localizable, especially when its connectivity degree is not sufficiently high. In order to efficiently tackle this problem, we propose a novel two-objective localization approach based on the combination of the harmony search (HS) algorithm and a local search procedure. Moreover, some connectivity-based geometrical constraints are defined and exploited to limit the areas in which sensor nodes can be located. The proposed method is tested with different network configurations and compared, in terms of normalized localization error and three multi-objective quality indicators, with a state-of-the-art metaheuristic localization scheme based on the Pareto archived evolution strategy (PAES). The results show that the proposed approach achieves considerable accuracies and, in the majority of the scenarios, outperforms PAES.Item On the heritability of dandelion-encoded harmony search heuristics for tree optimization problems(IEEE, 2015-09-24) Perfecto, Cristina; Bilbao, Miren Nekane; Del Ser, Javier; Ferro, Armando; IATree based optimization problems stand for those paradigms where solutions can be arranged within a tree-like graph whose nodes represent the optimization variables of the problem at hand and their interconnecting edges topological and/or hierarchical relationships between such variables. In this context, a research line of increasing interest during the last decade focuses on the derivation of intelligent solution encoding strategies capable of 1) capturing all topological constraints of this particular class of graphs; and 2) preserving their connectivity properties when they undergo combination/mutation operations within approximative evolutionary solvers. This manuscript takes a step over the state of the art by shedding light on the heri-tability properties of the Dandelion tree encoding approach under avant-garde stochastically-controlled evolutionary operators. In particular we elaborate on the topological heritability of the so-called Harmony Memory Considering Rate (HMCR) exploitative operator of the Harmony Search algorithm, a population-based meta-heuristic algorithm that has so far shown to outperform other evolutionary schemes in a wide range of optimization scenarios. Results from extensive Monte Carlo simulations are discussed in terms of the preserved structural properties of the newly produced solutions with respect to the initial Dandelion-encoded population.Item One-year-ahead energy demand estimation from macroeconomic variables using computational intelligence algorithms(2015-07-15) Salcedo-Sanz, Sancho; Muñoz-Bulnes, J.; Portilla-Figueras, Jose Antonio; Del Ser, Javier; IAThis paper elaborates on a problem of one-year ahead estimation of energy demand based on macroeconomic variables. To this end, two different Computational Intelligence approaches are herein evaluated: (1) a modified Harmony Search (HS) optimization algorithm with an exponential prediction model and (2) an Extreme Learning Machine (ELM). In the case of the HS, a feature selection of the best set of features for the prediction is carried out jointly with the optimization of the model’s parameters. On the other hand, the ELM will be tested with and without the feature selection carried out by the HS approach. We describe several modifications on the proposed HS, which include a hybrid encoding with a binary part for the feature selection, and a real part to tune the parameters of the prediction model. Other adaptations focused on the HS operators are also introduced. The performance of both approaches has been assessed in a real application scenario, corresponding to the total energy demand estimation in Spain, in which we have 14 macroeconomic variables with history values for the last 30 years, including the recent crisis period starting in 2008. The performance of the proposed HS and ELM models incorporating feature selection is shown to provide an accurate one-year-ahead forecast at a higher prediction’s accuracy when compared to previous proposals in the literature. Specifically, the HS and ELM approaches are able to improve the results of a previous approach (based on a genetic algorithm), obtaining an improvement over 15% in this problem of energy demand estimation. As a final experimental evaluation of the proposed algorithm, a similar problem of one-year ahead CO 2 emissions estimation from macro-economic variables is also tackled, and also in this case the HS and ELM are able to obtain significant improvements over a previous approach based on evolutionary computation, over 10% of improvement in this problem.Item Optimal design of Microgrid’s network topology and location of the distributed renewable energy resources using the Harmony Search algorithm(2019-08-01) Camacho-Gómez, C.; Jiménez-Fernández, S.; Mallol-Poyato, R.; Del Ser, Javier; Salcedo-Sanz, S.; IAIn this paper, we tackle the joint optimization of the network topology and the optimal location of distributed renewable energy resources in a Microgrid (MG). The MG network topology optimization problem is focused on obtaining network deployments with minimal cost, whereas the location of distributed renewable generation is associated with the minimization of the electricity losses in the MG lines. In order to solve this joint optimization problem, we analyze the efficiency of the Harmony Search (HS), a novel meta-heuristic solver inspired by the music improvisation procedure observed in jazz bands. We consider two different approaches, the first one is a single-objective formulation of the problem, where the classical HS is applied with some adaptations. The second approach is to consider a multi-objective version of the HS algorithm, able to evolve a whole family of solutions in a Pareto front. Both approaches have been tested on two small-sized MGs: an 8 node MG and a 12 node MG, and results have been compared to an 8 node and a 12 node baseline scenario, respectively, obtaining improvements of up to 42%.Item Resource allocation in rate-limited OFDMA Systems: A hybrid heuristic approach(2011) Del Ser, Javier; Bilbao, Miren Nekane; Gil-Lopez, Sergio; Matinmikko, Marja; Salcedo-Sanz, Sancho; IAThis paper presents a novel resource allocation procedure for OFDMA downlinks, which stems from an hybridization of the Harmony Search and the Differential Evolution heuristic algorithms. In this setup it is known that optimum subcarrier and power allocation is achieved through 1) assigning each subcarrier to the user with highest channel gain at the given frequency, and 2) a Water-Filling procedure over the set of considered channel gains. This work addresses the scenario where stringent rate constraints are imposed for each user at the transmitter, scenario where the previous optimum resource allocation procedure no longer holds. The proposed iterative technique hinges on the aforementioned combinatorial heuristics, jointly with an iterative greedy subcarrier shifting procedure that accounts for the fulfillment of the established rate restrictions. Preliminary simulation results are provided for the extended vehicular ITU channel model, which shed light on the performance of the proposed allocation procedure.