Energy consumption is a critical issue in the operation of data networks, and software-defined networks (SDN) have emerged as a promising alternative. This article addresses jointly the importance of improving energy efficiency while paying attention to the serviced traffic flow. Inspired by this issue, this study introduces a proposal for flow routing and assigning these specific traffic flows to SDN devices in a multi-objective optimization context called Multi-ojective Routing and Device Assignment (MoRDA), which jointly considers energy efficiency and flow blocking. This study proposes an approach based on Multi-Objective Evolutionary Algorithms (MOEAs) to compute a set of non-dominated optimal solutions. The simulation results validate that the proposed approach is promising compared to another approach of the state-of-the-art in obtaining efficient Pareto sets at a lower computational cost on several test instances.

Elastic Optical Networks (EONs) have emerged as a promising solution to meet the increasing demands for bandwidth and flexibility in modern communication networks. However, efficient resource allocation, including Routing, Spectrum, and Core Assignment (RSCA), represents a critical challenge due to its computational complexity. Given the NP-hard nature of RSCA, this work relies on heuristic and metaheuristic optimization techniques, inspired by Genetic Algorithms (GA). Considering a multicore optical fibers EON and a set of unicast requests, these algorithms seek to compute the path, the index core in each optical link, and frequency slots for each request that minimizes the blocking rate, average of the spectrum used rate, and standard deviation of the spectrum used rate, subject to spectrum continuity, spectrum contiguity, and spectrum non-overlapping assignment. This study applies the proposed algorithm under incremental semi-dynamic traffic and evaluates the performance of core selection policies of first fit (FF), minimum cost given as the number of slots in neighboring cores (MC), minimum cost weighted between number of slots in neighboring cores and the local core (MW). The results show the suitability of the MC and MW approaches over the traditional FF approach.

Accurate prediction of river levels is essential to anticipate extreme events such as floods and low flows. This paper presents a short-term forecasting model of the Paraguay River level for the port of Asunción, based on Gated Recurrent Unit (GRU) networks reinforced with hydro-meteorological covariates and an adaptive retraining scheme. To achieve the above, this study incorporates cumulative flows and rainfall from multiple stations, and a Bayesian search was applied to select the optimal combinations of variables and adjust the model's hyperparameter.The approach was evaluated on daily data between 1995 and 2022, using temporal cross-validation with a sliding window. Compared to a baseline model already superior to traditional methods, the proposed model achieved an Nash-Sutcliffe model Efficiency coefficient (NSE) of 0.9455 and reduced the Error Porcentual Absoluto Medio (MAPE) by half. In addition, it maintained higher predictive stability over a 28-day horizon in complex hydrological periods. These results demonstrate the potential of GRU models enriched with multi-source information as practical tools for water management in vulnerable watersheds.

Software-defined networking (SDN) and network functions virtualization (NFV) are promising technologies for demand services that require building flexible multicast transmission mechanisms with requirements for data processing functions at the network nodes. The multicast routing problem in NFVSDN networks seeks to compute multicast-routing trees and place virtual network functions (VNFs), satisfying the traffic demand with optimal resource use and fair data transmission. Since the problem is computationally complex with conflicting objective functions, this paper approaches multicast routing and VNF placement as a multiobjective optimization problem (MOP), minimizing the total resource cost and the maximum transmission delay variance. In this context, this study develops solutions based on Multiobjective Evolutionary Algorithms (MOEAs). Simulations performed on test instances show that the proposals are promissory by computing efficient and non-dominated solutions when compared to a state-of-the-art mono-objective approach.

Diabetic retinopathy is an eye complication caused by a widespread disease named diabetes mellitus. The examination of retinal fundus images procured by retinography is the most commonly used method for diagnosing diabetic retinopathy. Strategies based on deep learning have shown promising results in detecting diabetic retinopathy, achieving performance similar to that of the human eye regarding image inspection. However, the performance of these strategies heavily depends on fine-tuning the algorithm hyper-parameters and big datasets. In this work, we propose training a Deep Learning model with Low-Rank Adaptation (LoRA) approach to classify three stages of Diabetic Retinopathy: i) no sign of diabetic retinopathy, ii) Non-proliferative diabetic retinopathy, and iii) proliferative diabetic retinopathy. We propose using a low-rank representation to reduce significantly the number of trainable parameters. The experiment shows that the LoRA approach for image classification of the three stages of diabetic retinopathy manages to obtain state-of-the-art results even with a small dataset.

Routing, Modulation Level, and Spectrum Assignment (RMLSA) problems are critical to the success of Elastic Optical Networks (EONs). The literature reports several techniques for approaching RMLSA. However, the complexity of this problem necessitates the development of innovative approaches, which is the very essence of our work. Reinforcement Learning (RL) is an emerging alternative to dynamic RMLSA due to its ability to adapt to changes in the network's state through a learning process. Accordingly, this work proposes approaching the problem with RL techniques based on the Q-learning model. The experimental study conducts simulations over several test instances. The results confirm that the proposed approach shows promise in obtaining good results compared to the state-of-the-art heuristics

Elastic optical network survivability is essential for a viable multicast service implementation. In turn, multicast tree over virtual network embedding has become a vital service for multicast traffic. The study of multicast protection techniques in virtual network embedding and elastic optical networks has received significant attention. However, multicast protection is incipient when virtual network embedding uses elastic optical networks as the substrate layer. Consequently, this work approaches virtual multicast tree embedding over elastic optical networks and protection against single optical link failures, which we call the virtual optical multicast tree embedding and protection problem. This study proposes a genetic algorithmbased approach that works with different multicast protection schemes: dedicated dual-tree, shared dual-tree, dedicated subgraph, and shared sub-graph. Given a network topology, a set of virtual optical multicast requests, and a multicast protection scheme, the proposed approach seeks to calculate a solution that minimizes the total spectrum used, the number of blocked requests, and the number of unprotected incrusted requests. Numerical simulations on different network topologies and traffic loads were performed to analyze the impact of the protection schemes. The results show that the shared subgraph achieves better results regarding the total spectrum used and blocking. If the traffic requires dedicated protection, the dedicated subgraph scheme is more efficient than the dual-tree.

Diabetic retinopathy is an eye complication of a widespread disease named diabetes mellitus. The most widely used method for diagnosing diabetic retinopathy is the analysis of retinal fundus images obtained by retinography. Deep Learning-based methods have shown promising results as a diagnostic tool for diabetic retinopathy, achieving, in some cases, performance close to the human inspection of images. However, the performance of these methods relies heavily on fine-tuning the algorithm hyperparameters and big data sets. In this work, we propose training a Deep Learning network with evolutionary algorithms to classify three stages of Diabetic Retinopathy: i) no sign of diabetic retinopathy, ii) Non-proliferative dia-betic retinopathy, and iii) proliferative diabetic retinopathy. We propose a neuroevolution methodology for selecting the most efficient Deep Learning model. The results of the neuroevolution methodology were improved by including Simulated Annealing strategies, Population Reinitialization, and ensembles. With high accuracy, sensitivity, specificity, and kappa index rates of 0.889, 0.889, 0.951, and 0.822, respectively, in the best case found, the experiments show that our neuroevolution methodology for selecting the Deep Learning model hyperparameters is a competitive alternative for training deep neural networks to classify three stages of diabetic retinopathy even with a small data set.

Image segmentation is one of the first steps in most image processing procedures. The segmentation aims to obtain a more meaningful or simplified image representation by grouping pixels with common characteristics, which allows regions or features of interest to be uniquely identified. The result of the segmentation has a significant impact on the subsequent steps. Segmentation is part of several superior applications such as artificial vision, medical, topographic, and astronomical image analysis. No single or universal segmentation process gets optimal performance for all image types. Hence, determining a function that fits specific image types or applications becomes a detailed, complex, and not trivial task requiring much time and effort. In this paper, we propose using Multi-Objective Evolutionary Algorithms (MOEAs) as a training tool that combines operations that represent the techniques and strategies commonly used for generating image segmentation. As a result, sequences of operations are suitable for specific applications or image types. The objective functions used to guide the evolutionary process are sensitivity maximization (TPR) and specificity maximization (TNR), the basic components of ROC analysis. Sensitivity and specificity are commonly used as classification metrics to evaluate the quality of a proposed segmentation compared to an ideal segmentation. We used sensitivity and specificity as objective functions rather than accuracy because, as stated in [1], the dependence on prevalence makes accuracy less effective than a simultaneous consideration of sensitivity and specificity. Experiments were conducted on multiple images that share common characteristics obtained from image databases, specifically: i) benign and malignant melanoma images, ii) ophthalmoscopic retinal images, and iii) binary cell form images, where the segmentation generated by the proposed algorithm was compared with ideal segmentation. The results are quite promising and show that using MOEAs to generate sequences of segmentation operations valid for specific applications is feasible.

The problem of multicast routing, modulation level, and spectrum assignment is essential for the efficient performance of elastic optical networks. This problem seeks to compute light forests with the least optical network resources to satisfy multicast requests. The proposed strategies in the literature addressed this problem with exact or heuristic techniques, which are suitable when the number of requests is low. Developing scalable and efficient strategies is critical when the problem is complex, therefore, this study proposes a metaheuristic approach based on genetic algorithms. Given a set of multicast requests and a network topology, the proposed approach seeks to compute a set of light forests that minimizes (1) the number of blocked requests, (2) the maximum used frequency slot, and (3) the number of used transponders. Numerical simulations were performed to study the proposed algorithm performance under different static traffic loads and network topologies. The experimental results show that the proposed algorithm is promising for obtaining competitive solutions in reasonable computational time compared to the competitive heuristic approaches of state-of-the-art.

The stage of transporting semiconductor chips from the wafer to the support strip is crucial in the integrated circuit manufacturing process. This process can be modeled as a combinatorial optimization problem where the objective is to reduce the total distance the robotic arm must travel to pick up each chip and place it in its corresponding position within the support structure. This problem is of the pick-and-place type and is NP-hard. The (approximate) solution proposals of state-of-the-art methods include rule-based approaches, genetic algorithms, and reinforcement learning. In the present work one of these methods is analyzed, which models the problem as one of binary integer programming and proposes a genetic algorithm. Based on this analysis, we proposed and evaluated other methods, including a greedy algorithm that improves the state-of-the-art results for test cases usually used in the literature.

Efficient attendance of 911 emergency calls is a significant challenge, mainly in cities with high rates of violence. This problem has been addressed by considering it as a coverage problem with violence rates but without guaranteeing maximum response time or minimizing response time without guaranteeing coverage. Consequently, in this work, we address this problem by considering a coverage maximization according to the violence index guaranteeing maximum response and coverage. For this purpose, we developed a tool that builds a weighted graph with the violence index, given a city map and the history of recorded incidents. Next, inspired by the previous works, we propose a new mathematical formulation that maximizes coverage according to the rate of violence subject to total coverage and guarantees maximum response time. Furthermore, we apply a Tabu Search to calculate the best solution. Considering actual data from the city of Asunción (Paraguay), a numerical simulation was performed using the strategy in the system 911, a state-of-theart algorithm based on coverage, and our contribution. The simulation results show that the proposed algorithm can find security coverage solutions with a better allocation for the areas with a high rate of violence than the other alternatives evaluated.

Elastic Optical Networks (EON) are a considerably new technology and have a promising future due to their fast speed, flexibility, and spectrum efficiency. The main point to consider in EON networks is the routing and spectrum assignment (RSA), an NP-complete problem. This work focuses on cases where there is semi-dynamic traffic with variable bandwidth. Initially, we broadly study techniques already proposed to face this problem, focusing on the best use of the spectrum and thus avoid spectrum reassignment, which will be the main objective of this work. This work proposes two promissory algorithms based on Artificial Bee Colony (ABC) and Bee Colony Optimization (BCO) with the encoding of bee based on routing and permutation. For both encodings, we use Fixed Alternative Routing and Mid-Fit and First-Fit spectrum assignment. Various and extensive simulations were conducted with different traffic loads and network topologies considering blocking probability and entropy measures. The efficiency of the algorithms varies according to the topologies. In general terms, routing-based ABC had better results in larger topologies, while permutation-based BCO having outstanding results in smaller topologies. Still, it does not have good results in the same way for larger topologies.

The acquisition of microscopic images of minerals with good contrast is critical for the identification and analysis of their properties. However, in many cases, the microscopic images of minerals obtained are unclear due to the image environment, imperfect adjustment of the microscopy operators or improper collection of samples. In this paper, we present an algorithm to enhance the microscopic images of minerals by multiscale Top-Hat transform using contrast adjustment weights. First, the multiple dark and bright features of the mineral image are extracted using the top-hat transform. Secondly, bright scale differences and dark scale differences obtained in the previous step are calculated. Third, all the intensities of the multiple dark and bright features from the previous steps are summed separately. Finally, the bright features adjusted for a contrast weight are then added to the image and dark features adjusted for the same weight are subtracted from the image. Experimental results on various kinds of microscopic mineral images verified the effective performance of this proposed enhancing the contrast, improving the detail and spatial information about the images

The educational infrastructure in the Department of Caazapá, as in other regions of Paraguay, presents characteristics that do not favor the development of the educational process. Caazapá currently has 469 schools in this department, and the average number of students per school is 83. If we also consider that 62% of schools have less than 15 students per class, it can be inferred that there is an underutilization of the infrastructure and cost overruns in large part of the schools. In contrast, 1% of the schools have on average more than 49 students per classroom. This inefficient distribution of schools causes high investment costs for improving and maintaining schools and resource management problems. It is imperative to the application of strategies that are oriented to the optimization of available resources. This study adopts a mixed-integer linear programming model to select schools to minimize operating costs, investment in infrastructure, and transportation. We combine operation research techniques with geographic information systems to analyze the problem and interpret the results. The results show opportunities for improvement in the design of the educational network, and it is feasible to reduce investment costs by consolidating the demand in fewer establishments than currently exists. Additionally, this result would also allow generating economies of scale to optimize the operating costs of the establishments.

This work investigated and implemented the integration and effective use of Computer Aided Diagnostic and Detection systems (CAD) with a Picture Archiving and Communication System (PACS) and how this could provide standardized data and expand the coverage of specialized professionals. Likewise, standardized data allowed interoperability between systems. The designed CAD is focused on Diabetic Retinopathy diagnosis with three main functions: (I) digital image processing for detection, segmentation and extraction of retina image anomaly features, (II) binary classification, and finally (III) converter of the results to the DICOM standard (Digital Imagine and Communication Systems). It was decided to use an open source and free license PACS called Orthanc. The results show the successful integration of the systems, since the studies entered in the CAD are accessed directly from the PACS. At the same time, usability tests show a high degree of efficiency, effectiveness and satisfaction of clinical users in the use of the system.

The huge bandwidth exploited in optical fibers and the ability to handle multiple simultaneous transmissions on the same fiber due to the WDM technology, have made the problem of protection, multicast routing and wavelength allocation (MPRWA) critical for the success of point-to-multipoint applications. In order to maintain the quality of service required by these applications, the network faces the restriction of rapid recovery in cases of failure. Also, it must minimize the different costs that this entails, and prioritize requests in case of not having the necessary resources for recovery. In this context, this work deals with the design of the main multicast route and its protection, with quality of protection (QoP) levels. For this reason, two protection schemes have been addressed: dualtree based and biconnected-subgraph based. To achieve this, competitive evolutionary techniques are applied; where the total number of links used, the number of wavelength converters, the number of splitter nodes, and the number of destinations served and protected are objective functions simultaneously optimized in a Pareto context. The experimental tests were carried out on different network topologies and multicast demands, considering the hypervolume as a Pareto quality measure. The results suggest that the subgraph-based strategy is more promising, obtaining better results than the protection based on dual-tree.

This work proposes a solution to the problem of management automation in the Emergency Department of the Hospital de Clínicas of the National University of Asunción. This tool will generate benefits for both the patient and the hospital's target staff, since by computerizing the processes, which is still done manually, there will be more time to dedicate better care to the patient and doctors could spend part of their time analyzing the statistics and information, that can be generated through the application to carry out investigations in the emergency care area, as well as optimize the resources of both, the staff and the logistics used in the Hospital. To achieve the main objective of this work, an application based on international health standards has been developed, as well as good practices in the care processes in an extremely sensitive area such as the Emergency Department. With this application it is possible to innovate in the Emergency Department, given that after more than 100 years of history of the Hospital, for the first time it intends to automate processes and generate online information quickly and efficiently from this modern tool optimizing attention to patients in the emergency area.

One of the most important unsolved problems in the area of Computational Biology is the prediction of protein structures. A key element in this problem is the prediction of contacts in a protein from its amino acid sequence, since it provides fundamental information for the determination of its three-dimensional structure. Due to the attention devoted to this subproblem, especially in the last decade, there are a large number of methods in the literature that obtain very good results; but there is still a considerable room for improvement. In the 13th edition of the Critical Assessment of protein Structure Prediction (CASP), a notable progress has been achieved in this area due to the use of deep learning and deep convolutional residual neural networks in state-of-the-art methods; in addition to the use of additional information from other predictions, such as solvent accessibility, conformation of the secondary structure, etc. The present work analyzes the performance of the most outstanding CASP13 methods, considering a larger test set (483 proteins) with proteins of four different classes according to SCOP. The results were evaluated using the CASP metrics. The analysis indicates that most of the selected methods have an accuracy above 90% for the test set used; SPOT-Contact being the best prediction method in general, and at least one of the best in each of the SCOP classes. The test cases and implementations made for the evaluation of results are publicly available.

TapeYty is a tool developed to calculate optimal paths for urban garbage collection vehicles in the Asuncióon city. This tool generates benefits mainly in the economic and environmental aspects of the city where a large number of people brings a high generation of waste. This makes the complexity of garbage management even greater. The routing problem is treated as the open rural postman problem which seeks to minimize the distance to be traveled by the collection vehicles. To achieve the objective TapeYty is based on mathematical programming techniques and Geographical Informatiom System (GIS) tool which allows the management of the route network, being able able to update the road way and their state of blocked or nonblocked. This implies that when changes of state of the streets TapeYty modifies the graph that represents the road network and re-calculates the solutions providing new optimal routes to each vehicle of collection. The tool has provided solutions that save on average 20% of distance traveled compared to current tour.

Software defined networking (SDN) is an emerging technology based on the separation of the control plane and the data plane. This allows to obtain benefits, in comparison with traditional networks, in terms of network management, global monitoring-control, cost reduction, and in particular the energy saving by the strategic activation of devices. In this paper, we propose an approach that seeks to minimize the global energy consumption of the network by suspending inactive devices, such as chassis and line cards, as well as limiting the use of links in traffic sessions. For this purpose, we developed an Integer Linear Programming (ILP) model for the SDN routing problem in order to obtain the minimum energy consumption, subject to satisfy all traffic demands. The experimental results on two network topologies for a set of static traffic requests indicate that the proposed model is promising, saving up to 42% of the global energy consumption obtaining a better performance to the models proposed in the literature. On the other hand, the experimental results for incremental semi-dynamic traffic indicate that the performance of the optimization with re-routing improves the approach without re-routing when increasing the traffic in the network, but this improvement is not always perceptible. The approach without re-routing in terms of scalability is promising, by increasing the traffic load not generate interruptions to the traffic already attended and affect the quality of the service.

This paper proposes a solution to the problem of automated management of Acute Lymphoblastic Leukemia (ALL) in children that are being treated at the Hospital de Clínicas (HC) which belongs to the Faculty of Medical Sciences of the National University of Asunción (FCM-UNA). For this purpose, we are proposing the design and implementation of a computer application called Pediatric Hemato-Oncological Protocols Automation (APHOPe), which facilitates obtaining information and managing the implementation of the procedure protocols and hematological and oncological treatment used in the Pediatric Hemato-Oncology Department (HOPe). APHOPe can provide statistical data and applied treatments so the effectiveness-efficiency relation of the global and particular protocols for each patient can be analyzed. In this context, APHOPe allows the generation of various Ad Hoc protocols depending on the treatment needs of the medical personnel. In this stage the system provides the 2008 Protocol LLA which is a variant of the Berlin-Frankfurt-Munster Protocol. The usability and acceptability tests indicate that the proposed system is an effective and flexible tool for the automated management of the hemato-oncological protocols.

Searching for adequate input parameters of a Contrast Enhancement Algorithm is a fundamental task, aimed to get more suitable images, in terms of contrast, for decision making or further applications. Several Optimization approaches take a single image as the input of the process, so they get poor performance. In this proposal, a group of several images of the same type are taken as input of a Robust Multi-Objective Particle Swarm Optimization approach, in order to achieve more suitable input parameters for a kind of images, instead of a single image. The results are a set of input parameters for the Contrast Enhancement Algorithm, suitable for Contrast Enhancement of a group of images of the same type.

Fragmentation in Elastic Optical Networks is an issue caused by isolated, non-aligned, and non-contiguous frequency slots that can not be used to allocate new connection request to the network, due to the optical layer restrictions imposed to the Routing and Spectrum Assignment (RSA) algorithms. To deal with this issue, several studies about Spectrum Defragmentation have been presented. In this work, we analyze the most important Non-Hitless Defragmentation Algorithms found in the literature, with proactive and reactive approaches that include rerouting and non-rerouting schemes, and compare their performance in terms of Blocking Probability, Entropy, and Bandwidth Fragmentation Ratio. Simulations results showed that the Fragmentation Aware schemes outperformed the other algorithms in low traffic load, but the Reactive schemes got better results in high traffic load.

Estimating effort is a very important task in any organization. Significant over or under-estimates can be very expensive for software project companies. The use of computing intelligence methods has been recently proposed for software development effort estimation. In this study, we present new models to estimate the effort required for the development of software projects. These new models were calculated using Linear Genetic Programming (PGL). The results show that the proposed models get more precise and more effective estimation for Mean Magnitude Relative error (MMRE) and Mean Magnitude of Relative Error relative to the Estimate (MMER) than using the constructive cost model (COCOMO). We performed the study based on three stages according to the type of project. The models were designed and validated by simulation with the public repository dataset COCOMO81 and NASA93. Performance of the proposed models EE_PGLa, EE_PGLb and EE_PGLc are more accurate than COCOMO.

Given the importance and complexity of the problem of robust network design, this work, studies the robust network design subject to guarantee certain level of quality of service, so that the reservation of an adjustable bandwidth for each node, the network is not negatively influenced by traffic from the rest of the network. Therefore a MultiObjective Evolutionary Algorithm (MOEA) is proposed to solve and find the robust network design, inspired by the concept of price of robustness, which simultaneously minimizes the cost of network, inequity traffic and maximizes the traffic service in the worst case scenario. Finally, experimental results show the benefits of the proposed approach to get a set of non-dominated solution on which it can make, a better decision making.

Melanoma is a type of skin cancer and is caused by the uncontrolled growth of atypical melanocytes. In recent decades, computer aided diagnosis is used to support medical professionals; however, there is still no globally accepted tool. In this context, similar to state-of-the-art we propose a system that receives a dermatoscopy image and provides a diagnostic if the lesion is benign or malignant. This tool is based on next modules: Preprocessing, Segmentation, Feature Extraction and Classification. Preprocessing involves the removal of hairs. Segmentation is to isolate the lesion. Feature extraction is considering the ABCD dermoscopy rule. The classification is performed by the Support Vector Machine. Experimental evidence indicates that the proposal has 90.63 % accuracy, 95 % sensitivity and 83.33 % specificity on a dataset of 104 dermatoscopy images. These results are favorable considering the performance of diagnosis by traditional progress in the area of dermatology.

The performence of centralized and distributed routing in wavelength converter allocation problem are studied in this work. The distributed routing is based on selfish routing in which each connection tries to improve its blocking probability. In counterpart, in centralized management, the routing of connexions are calculated by cooperative approach to improve the overall blocking probability of system. In the cooperative context, it is proposed a pure evolutionary algorithm which calculates simultaneously the converters allocation and traffic load flows. For selfish routing, an evolutionary algorithm calculates the converters allocation while the traffic load flow assignment that maximizes the benefit of each connection is accomplished by simulations. Both approaches are compared using Pareto Anarchy Price measure which is a proposal of this work. Experimental results indicate that, when the traffic load increased the Pareto Anarchy Price improves, and paradoxically, the quality of solutions gets worse.

In the current field study of the survival of WDM networks using quality of protection (QoP), exist the need to adjust flexibly the QoP levels to the variety of existing demands of connection, mainly due to a lack of fairness distribution and optimal resources administration. This paper proposes a paradigm for quantifying degrees of protection service based on recovery probability for simple link failure. For the optimum design of primary and backup paths is proposed an approach based on Genetic Algorithm that calculates paths subject to QoP of each unicast request. The Experimental results indicate that the proposed approach is a promising solution to obtain less expensive and more fairness services compared to traditional protection approaches based on non-flexible QoP.

A network design is robust if it is able to deal with any traffic requirement under certain bounds and physical network conditions. The robust network design is a complex problem of growing importance where, in general, the only information available are traffic bounds of the network links. This work proposes a Genetic Algorithm to design robust networks with optimal capacity of links considering a stable routing with uncertain traffic that can be divided in k sub-routes. The uncertain traffic is handled by usign the hose model which imposes a maximum input/output traffic for each network node. Experimental results for a set of instances with different number of routes show the convenience of a stable routing against a non-divisible routing (k = 1). However, an increasing of the k value implies an increasing of the number of viable solutions, thus, a trade-off relation between k and the quality of solutions obtained by the proposed algorithm was detected.

This work deals with the survivable optical network design problem for static multicast traffic, subject to simple node fails. Given a set of multicast request, it is proposed an algorithm based on Multi-Ojective Ant Colony Optimization which tries to find the best network design, as well as the primary and back-up multicast trees, with protection against node fails. The proposed algorithm simultaneously minimizes the network design cost and the maximum end-to-end optical delay. The experimental results over different instances show the benefits of three protection approaches based on total or partial network reconfiguration.