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This paper presents a method to assess the state of convergence of Ant Colony Optimization algorithms (ACO) using network science metrics. ACO is a computational intelligence technique inspired by the behavior of ants in nature, and it is commonly used to solve combinatorial optimization problems. In the ACO, artificial ants seek for solutions to the tackled problem, transversing the graph that represents the combinatorial problem. For each displacement, the ants deposit pheromone on the edges which they passed. The ants use the pheromone for indirect communication, and the amount of pheromone weighs the intensity of interaction between the ants. The graph of pheromones forms a network that evolves along the iterations. Network Science allows studying the structure and the dynamics of networks. This area of study provides metrics used to extract global information from networks in a particular moment. This paper aims to show that two network science metrics, the Clustering coefficient, and the Assortativity, can be adapted and used to assess the pheromone graph and extract information to identify the convergence state of the ACO. We analyze the convergence of the four variations of the ACO in the Traveling Salesman Problem (TSP). Based on the obtained results, we demonstrate that it is possible to evaluate the convergence of the ACO for the TSP based on the proposed metrics, especially the adapted clustering coefficient. [ABSTRACT FROM AUTHOR]

This paper presents the Hierarchical Hopfield Neural Networks (HHNN). HHNN is a novel Hopfield Neural Network (HNN) approach. HHNN is composed of a hierarchy of self-sufficient HNNs, aiming to reduce the neural network structure and mitigate convergence problems. The HNNN composition depends on the applied problem. In this paper, the problem approached is the inter-domain routing for communication networks. Thus, the hierarchy of HNNs mimics the structure of communication networks (domains, nodes, and links). The proof of concept and the comparison between HNNN with the state-of-art HNN occurs using an implementation of them in the Java programming language. Besides, the performance analysis of the HHNN runs on a parallel hardware platform, using VHDL to develop it. The results have demonstrated a reduction of 93 . 75 % and 99 . 98 % in the number of neurons and connections to build the neural network, respectively. Furthermore, the mean time to achieve convergence of HHNN is rough 1 . 52 % of the total time needed by the current state-of-art HNN approach. It is also less susceptible to early convergence problems when used in communications networks with a large number of nodes. Last, but not least, the VHDL implementation shows that convergence time of HHNN is comparable to routing algorithms used in practical applications. [ABSTRACT FROM AUTHOR]

Physical topology design of optical networks is frequently accomplished by using evolutionary approaches. However, fitness evaluation for this type of problems is time consuming and the overall optimization process presents a huge execution time. In this paper we propose a new method that uses a multi-objective evolutionary approach to handle the design of all-optical networks. We focused on the simultaneous optimization of the network topology and the device specifications in order to minimize both the capital expenditure of the network and the network performance. Our method uses surrogate models to accelerate the fitness evaluation and a novel network generative model based on preferential attachment to generate the seeds for the evolutionary process. Our approach can provide high quality solutions with a very small execution time when compared to the previous approaches. In order to assess our proposal we performed a set of simulations aiming to analyze the convergence ability and the diversity of the generated solutions for scenarios considering uniform and non-uniform traffic matrices. From our results, we obtained an evolutionary approach that presents better solutions than previous proposals for all analyzed scenarios. Our proposal presents an execution time that is up to 84% and 88% lower than the execution time needed by the previous approaches for uniform and non-uniform traffic, respectively. [ABSTRACT FROM AUTHOR]

Abstract: We report measurements of cross-saturation bandwidth on signal wavelength in single-pumped Thulium doped fiber amplifiers (TDFA) using two different pump wavelengths (1426 and 1050 nm) and a highly doped (5000 ppm) Thulium-doped fiber module. The 1050 nm pump has a lower cross-saturation effect than the 1426 nm, and an increase in cross-saturation is observed as the signal wavelength decreases within the S band. We also experimentally investigated the transient response of a single-pumped TDFA at 1050 nm, and analyze the amount of surviving channel gain, gain excursion and time-constants or the highly doped fibers. [Copyright &y& Elsevier]

Ensuring the reliable operation of diesel/Heavy fuel oil (HFO) engines in Thermoelectric Power Plants (TPPs) requires constant monitoring and control of operational parameters. Time series forecasting methods can predict physical system characteristics based on historical data, which can help regulate engine operational parameters. Despite the widespread use of these techniques in coal and natural gas powered TPPs, further examination of their application in large diesel/HFO engines in Brazilian TPPs operating under dispatch conditions is needed. This work investigated the fuel consumption generalization capacity of linear, nonlinear, and hybrid prediction models, both considering univariate and multivariate approaches, related to a TPP engine-driven generator in Pernambuco, Brazil. For multivariate model development, exogenous variables were selected based on change of causality overtime analysis, which sought not only to infer the relationship between signals but also to identify the influence regime of each causality during operations. The feature selection step initially identified a performance improvement when eight additional features were used. However, each feature causality throughout the operations indicated that only four signals helped significantly predict consumption in different ways. The exogenous variables introduction to ARIMA and NAR models resulted in significant improvements only to the nonlinear approach, NARX, which could recognize operational disturbances more quickly when compared to other analyzed models. In addition, the application of additive and multivariate hybrid models provided the ability to detect more complex variations and, at the same time, maintain model stability during full-load operation, benefiting from linear and nonlinear characteristics capturing ability related to the combined model. • Engines' FOC was analyzed based on univariate and multivariate prediction models. • Linear, nonlinear, and hybrid prediction models were used. • Exogenous variables were selected based on change of causality overtime analysis. • Exogenous variable's introduction to NAR resulted in significant improvements. • Hybrid prediction models detected complex variations and maintained model stability. [ABSTRACT FROM AUTHOR]

The last few years have seen an increasing demand for high-capacity Internet services, and this need has intensified in the years 2020 and 2021. In 2020 and 2021, Internet usage grew by 50% in several European countries, mainly due to home office, video streaming, hybrid teaching, and others. High-capacity optical networks usually meet this growing demand for Internet services. Thus, investigations that can improve the quality of optical networks are highly relevant in the current context. One of the research problems in this area is related to the physical topology design (PTD) of optical networks, which is classified as NP-hard. Several studies on PTD consider the application of meta-heuristics that obtain suboptimal solutions in a time compatible with engineering applications. However, meta-heuristics and local search techniques have been combined in several other optimization problems, which is not typical for the PTD problem. This paper proposes a solution to the PTD problem that combines a known multipurpose optimization algorithm, the NSGA-III, with operators considering the domain-specific knowledge of the problem to provide superior-quality networks. According to our results, the new proposal presents quality up to 8% higher than previous proposals concerning the hypervolume metrics (HV), maintaining a similar computational cost. • Proposal of pivot rules for local search in many objective algorithms for PTD. • Provide a method for efficient exploration of neighborhood for network optimization. • Proposal of a new local search that considers both convergence and diversity. • Hybridization of NSGA-III with the proposed LS, providing superior quality solutions. [ABSTRACT FROM AUTHOR]

The increase in available data has attracted the interest in clustering approaches as a way of coherently aggregating them and identify patterns in big data. Hence, Swarm Intelligence techniques can outperform standard clustering techniques in some real problems. Indeed, they can replace standard techniques in some cases. The knowledge regarding the problem is not enough to select the best algorithm. It is also necessary to unveil which techniques are relevant in the literature. This paper presents a systematic mapping review on recent investigations of swarm-inspired algorithms to tackle clustering problems. We selected 161 articles from the most important scientific databases, which were published over the last six years. We discuss many aspects, such as the most used fitness functions, validation indexes, encoding schemes, hybrid proposals, frequent applications, among others. We provide an overview of how to apply the swarm methods together with a critical analysis of the current and future perspectives in the field. [ABSTRACT FROM AUTHOR]