The surveillance multi-sensor placement is an important optimization problem that consists of positioning several sensors of different types to maximize the coverage of a determined area while minimizing the cost of the deployment. In this work, we tackle a modified version of the problem, consisting of spatially distributed multi-sensor placement for indoor surveillance. Our approach is focused on security surveillance of sensible indoor spaces, such as military installations, where distinct security levels can be considered. We propose an evolutionary algorithm to solve the problem, in which a novel special encoding (integer encoding with binary conversion) and effective initialization have been defined to improve the performance and convergence of the proposed algorithm. We also consider the probability of detection for each surveillance point, which depends on the distance to the sensor at hand, to better model real-life scenarios. We have tested the proposed evolutionary approach in different instances of the problem, varying both size and difficulty and obtained excellent results regarding the cost of sensors’ placement and convergence time of the algorithm. • Tackle a spatially distributed multi-sensors placement problem with security levels. • Useful for security surveillance of sensible indoor spaces, such as military installations. • Proposal of an evolutionary algorithm with specific encoding and effective initialization. • Comparison with alternative algorithms in different-sized scenarios.

In this article, we propose a novel flexible architecture, with different algorithmic procedures, for effective story segmentation of broadcast news from subtitle files. The proposed system exploits spatial and temporal distance, as well as sentence similarity, to classify different stories in news broadcasts. The computational algorithms which form the architecture mainly focus on each sentence's features (temporal distance, spatial distance, and semantic similarity), and are combined to build an overall classifier. The first algorithm in the architecture focuses on the segmentation task, detecting boundaries between news. The second and third algorithms identify high semantic correlation between pieces of text, whether they are consecutive in space or not. Video Text Track (VTT) subtitle files are used to evaluate the performance of the proposed approach, although any file format that includes temporal information could also be considered. These VTT files may contain text errors and inaccuracies, and the proposed algorithms have been designed to deal with noisy content.

Network clustering is traditionally accomplished by relying just on the topology information, while a traffic-aware clustering approach has been recently proposed. The latter approach employs traffic matrices to take into account the intensity of the relationship between nodes. The extra effort needed to gather the traffic matrices is warranted if the composition of the clusters obtained in the traffic-based approach is significantly different from that obtained under the topology-based approach. In this paper we compare the outcomes of the two approaches, using the Rand Index as a similarity metric. For a variety of established clustering algorithms, and two large datasets, we show that the two approaches provide significantly different results, since the Rand Index lies far below one.

The selection of hyper-parameters in support vector machines for regression (SVMr) is an essential step in the training process of these learning machines. Unfortunately, there is not an exact method to obtain the optimal values of SVM hyper-parameters. Therefore, it is necessary to use a search algorithm in order to find the best set of hyper-parameters. Grid Search is the most commonly used option to perform such a hyper-parameters search, though other possibilities based on evolutionary computation algorithms have been proposed in the literature. In this paper we analyze the use of a standard genetic algorithm with binary encoding, which allows a fast exploration of the hyper-parameters space. We include a kind of tabu-list in the proposed algorithm, where we keep the last individuals generated by the genetic algorithm to avoid re-training of the SVMr with them. This technique allows a good improvement of the SVMr training time respect to the grid search approach, while keeping the machine accuracy almost unaltered.

This paper tackles the problem of sizing a standalone hybrid photovoltaic-batteries-hydrogen (PV-hydrogen) system, by applying an evolutionary algorithm. The system is specifically designed to cover the power necessities of remote, isolated telecommunications facilities, so it must be able to work in an unattended way during at least 2 years. Under this specific constraint, we develop an evolutionary algorithm which optimizes the number of PV panels and their distribution to feed two different arrays of batteries, and also the slope and azimuth of the panels. The well-known simulation program TRNSYS has been used in order to simulate the behavior of the real PV-hydrogen system. The evolutionary algorithm looks for the set of parameters which best performance of the system provide, in terms of hydrogen pressure remaining after two years and cost of the complete PV panels. The performance of the proposed evolutionary algorithm has been tested for the case of a real PV-hydrogen system sited at National Spanish Institute for Aerospace Technology (INTA), Torrejón de Ardoz, Madrid, Spain, where the proposed approach obtained a good solution which fulfils the constraint specifications of the system.

This 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.

While network clustering is traditionally accomplished just relying on the topology of the network, the new traffic-aware clustering approach employs traffic matrices to take into account the intensity of the relationship between nodes. In the context of traffic-aware clustering we propose a new Evolutionary Clustering algorithm and compare it with the Spectral Filtering algorithm. We compare them using both the Modularity and the Traffic-aware Scaled Coverage metrics, and two real-world datasets, each made of 1000 traffic matrices, respectively from Abilene and Géant networks. Our experiments show that Evolutionary Clustering performs better on all traffic matrices, excepting a minor number of traffic matrices in the Abilene network when the Modularity metric is employed.

Polyphase codes are widely used in radar systems as a pulse compression approach due to the fact that they produce lower side-lobes in the compressed signal than other methods. Unfortunately, the efficient design of such codes comprises by itself a non-linear n-dimensional NP-hard optimization problem, which has been so far tackled by using evolutionary techniques. In this paper we present a novel heuristic approach consisting of a Harmony Search algorithm hybridized with both a dynamic-step gradient-guided and a random walk local search procedures. The second procedure is applied when the gradient of the underlying fitness function is zero, i.e. when facing a flat region during the search process over the solution space. A simulation-based comparison study with the best results found in the literature is presented, from where it is concluded that our proposed algorithm outperforms other existing approaches in the literature for n=1,...,15.

A call center is a system used by companies to provide a number of services to customers, which may vary from providing simple information to gathering and dealing with complaints or more complex transactions. The design of this kind of system is an important task, since the trend is that companies and institutions choose call centers as the primary option for customer relationship management. This paper presents an evolutionary algorithm based on Dandelion encoding to obtain near-optimal service trees which represent the structure of the desired call center. We introduce several modifications to the original Dandelion encoding in order to adapt it to the specific problem of service tree design. Two search space size reduction procedures improve the performance of the algorithm. Systematic experiments have been tackled in order to show the performance of our approach: first, we tackle different synthetic instances, where we discuss and analyze several aspects of the proposed evolutionary algorithm, and second, we tackle a real application, the design of the call center of an Italian telecommunications company. In all the experiments carried out we compare our approach with a lower bound for the problem based on information theory, and also with the results of a Huffman algorithm we have used for reference.

In this paper we describe the application of a Hybrid Grouping Genetic Algorithm (HGGA) to the recent challenge of deploying metropolitan wireless networks, exploiting existing broadband infrastructure, by opening WiFi-enabled customers' DSL routers to third parties, or WiFi network Design Problem or WiFiDP. The application of a HGGA to this problem aims to produce the layout of a cost effective network deployment plan, considering real life aspects such as budget and DSL router characteristics (coverage, DSL capacity at a specific location, unit price, etc.) The total cost of deployment (i.e. the cost of opening all selected DSL routers for public use) should not exceed the allocated budget. The hybrid grouping genetic algorithm proposed includes a specific encoding to tackle the WiFiDP, in which the group part also includes the type of router to be installed. Moreover, a repairing and local search procedures are included in the algorithm to obtain better performance and always finding feasible solutions. The performance and effectiveness of the proposed HGGA is evaluated using two randomly generated WiFiDP instances (considering 1000 and 2000 users) that were used to perform several experiments. From theses datasets, we compare the results of the proposed HGGA with that of a greedy optimization algorithm previously proposed to solve the WiFiDP challenge.

A priority two-queue system, where high-priority customers receive a variable service rate is analyzed. The model can be used as a first approximated analysis for token-ring local-area networks where voice/data integration is accomplished. The voice service rate is variable according to the queue size at the instant the server reaches the voice queue. It is assumed that the larger the number of voice packets found by the server, the higher the voice service rate is. In contrast with other approaches, instead of dropping old voice packets, the proposed scheme can be considered to drop some amount of redundant information, transmitting only the remaining part, depending on the voice queue size. This strategy provides continuity of the voice service (at the price of reducing redundancy) in such a way that the loss of quality is distributed among several voice packets, which results in more regularity in the service.>

This paper proposes a neural network model for wind speed prediction, a very important task in wind parks management. Currently, several physical-statistical and artificial intelligence (AI) wind speed prediction models are used to this end. A recently proposed hybrid model is based on hybridizations of global and mesoscale forecasting systems, with a final downscaling step using a multilayer perceptron (MLP). In this paper, we test an alternative neural model for this final step of downscaling, in which projection hyperbolic tangent units (HTUs) are used within feed forward neural networks. The architecture, weights and node typology of the HTU-based network are learnt using a hybrid evolutionary programming algorithm. This new methodology is tested over a real problem of wind speed forecasting, in which we show that our method is able to improve the performance of previous MLPs, obtaining an interpretable model of final regression for each turbine in the wind park.

In this paper we present an application of the grouping genetic algorithm to the problem of assigning students to laboratory groups in university courses. This problem includes an important constraint of capacity, due to laboratories usually have a maximum number of equips or computers available, so the number of total students in a group is constrained to be equal or less than the capacity of the laboratory. In addition, our approach considers the case in which the students provide a sorted list of preferred laboratory groups, so the objective of the assignment must take this point into account. Another case in which lecturers' preferences are considered is also treated. The performance of the approach is shown in several test instances of the problem and compared with the results of an existing heuristic algorithm.

Call centers represent now an important channel for customer care activities. Most services provided by call centers are now accomplished in automated way to save operational costs. The customer is guided through a menu of options, which can be represented as a service tree, down to the desired service. In order to meet QoS constraints the service tree must be designed to minimize the average service time. An optimization algorithm, based on the combined use of Dandelion encoding and evolutionary operators, is proposed here to design the service trees with minimum average service time. The proposed algorithm provides trees whose average service time is lower than that obtained by the reference Huffman coding and is typically within 2% of the absolute lower bound represented by the entropic bound.

This paper presents the hybridization of global and mesoscale weather forecasting models with neural networks in order to tackle a problem of short-term wind speed prediction. The mean hourly wind speed forecast at aero-generators in a wind park is an important parameter used to predict the total energy production of the park. Our model for short-term wind speed forecast integrates two different meteorological prediction global models, observations at surface level and in different heights using atmospheric soundings. Also, it includes a mesoscale prediction model and a neural network to obtain the wind speed forecast in an specific point of the wind park. In the experiments carried out, we present some results of wind speed forecast in two aero-generators in a wind park at the south east of Spain. The results are encouraging, and show that our hybrid weather forecast models-neural network approach is able to obtain good short-term predictions of wind speed at specific points.

]This paper presents a hybrid genetic algorithm for solving a logic-type puzzle known as lights-up puzzle. The algorithm uses a binary Hopfield neural network to solve part of the puzzle constraint as it looks for good quality solution in term of the puzzle’s objective function. We show the good performance of our approach in a number of lights up puzzles instances downloaded from the internet.

This paper presents a hybrid evolutionary programming algorithm to solve the spread spectrum radar polyphase code design problem. The proposed algorithm uses an Evolutionary Programming (EP) approach as global search heuristic. This EP is hybridized with a gradient-based local search procedure which includes a dynamic step adaptation procedure to perform accurate and efficient local search for better solutions. Numerical examples demonstrate that the algorithm outperforms existing approaches for this problem.