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.

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.

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.

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 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.

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.

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.