Cyber-Physical Systems (CPS) are the basis for the world’s critical infrastructure and, thus, have the potential to significantly impact human lives in the near future. In recent years, there has been an increasing demand for connectivity in CPS, which has brought to attention the issue of cybersecurity. Aside from traditional information systems threats, CPS face new challenges due to the heterogeneity of devices and protocols, as well as its strong reliability requirements. In this work, we provide a brief overview of the CPS architecture and applications and describe the security challenges in the three CPS layers of perception, transmission, and application. Besides, we discuss how feature selection (FS) may improve intrusion detection performance. In particular, we evaluate how metaheuristic approaches can improve classification performance in CPS perception, transmission, and application layers. Our results reveal that (i) Greedy Randomized Adaptive Search Procedure (GRASP) outperforms traditional filter-based methods, and (ii) using the proposed enhanced approaches in GRASP construction and local search phases can enhance the average F1-Score of five classifier algorithms.

This paper describes a novel high-performance library collection called libbft, specially designed to deal with byzantine fault tolerance for consensus mechanisms. This library is inspired by timed automata theory, and by the groundbreaking work of Practical Byzantine Fault Tolerance (PBFT). As case study, we tackle recently proposed consensus algorithms, such as the Delegated Byzantine Fault Tolerance (dBFT) used on the NEO Blockchain. This library is organized in a multiprogramming language paradigm, being already implemented on C++ and Golang. The library handles states and transitions from regular automata machines, besides timed transitions, activated through conditions, actions and events. We also propose a mathematical notation to represent these elements, and an automatic tool to export the generated state machine into graphviz platform. We also integrate the library with state-of-the-art communication protocols, like the gRPC (Google RPC) and protobuf, which provides efficient and language-independent communication. This paper briefly describes the technological background behind libbft, practical applications and preliminary computational experiments.

This work proposes a new algorithm that combines Iterated Local Search (ILS), Variable Neighborhood Search (VNS), Random Variable Neighborhood Descent (RVND) and a constructive initial search to solve the Traveling Car Renter Problem (CaRS). The CaRS a variation of the Traveling Salesman Problem where a customer wants to visit a set of cities using a rental car. The customer has a fixed amount of car options available to choose and when he returns the car, he will not be able to use it again. The traveling cost varies according to the car used, being the same value in the opposite direction. In addition to this cost, the customer also pays for a vehicle return. The objective is to minimize costs and fees. The results were compared to the state of the art and showed better performances.

A Service-Level Agreement (SLA) usually refers to computational services (e.g., cloud/web services), indicating contract goals and expected Quality of Service (QoS), recently extended for transportation problems called Logistics SLA. Transportation problems are being systematically studied for Smart City (SC) applications due to its huge importance: public transportation services, drone delivery services, battery recharging for electric vehicles, and also transportation for private companies considering real-time traffic information. Many of these transportations problems involve not only one-way deliveries, but also pickups, forming a set of routes with desired QoS such as maximum route length/time, delivery/pickup sequences and time-windows. In order to achieve all desired QoS, while minimizing routing distances, this problem can be seen as an extension of the challenging Vehicle Routing Problem (VRP), which is known to be NP-Hard. Computational intelligence strategies such as metaheuristics are often employed to find near-optimal solutions in short computational times. In this paper, we deal with a practical industrial problem involving employees transportation to a workplace in a Brazilian metropolis, involving minimization of operational costs, achievement of QoS requirements and visualization of the routes.

This work proposes a variable neighbourhood search (FTS) that uses a fractal‐based local search primarily designed for images. Searching for specific content in images is posed as an optimisation problem, where evidence elements are expected to be present. Evidence elements improve the odds of finding the desired content and are closely associated to it in terms of spatial location. The proposed local search algorithm follows the fashion of a chain of triangles that engulf each other and grow indefinitely in a fractal fashion, while their orientation varies in each iteration. The authors carried out an extensive set of experiments, which confirmed that FTS outperforms state‐of‐the‐art metaheuristics. On average, FTS was able to locate content faster, visiting less incorrect image locations. In the first group of experiments, FTS was faster in seven out of nine cases, being >8% faster on average, when compared to the second best search method. In the second group, FTS was faster in six out of seven cases, and it was >22% faster on average when compared to the approach ranked second best. FTS tends to outperform other metaheuristics substantially as the size of the image increases.

This work deals with the Traveling Salesman Problem with Hotel Selection (TSPHS), a variant of the classic Traveling Salesman Problem (TSP). In the TSPHS, a set of hotels can be visited in strategic points of the route, dividing it in a minimum number of trips. Each trip must not exceed a given time limit, minimizing also the total time traveled. The TSPHS is NP-Hard, being a generalization of the TSP, so the main approaches in literature are based in Mathematical Programming and Metaheuristics. The metaheuristics are generic heuristics capable of escaping from local optima, usually obtaining good quality solutions in low computational time. It is developed a heuristic based on Variable Neighborhood Search, compared with the best algorithms in literature using classic instances. Computational results indicate that the proposed algorithm finds solutions with fewer trips in low computational time, with a traveled total time comparable to the best known solutions.

This paper proposes a new Evolutionary Algorithm for the Dynamic Resource Constrained Project Scheduling Problem. This algorithm has new features that get around some problems like premature convergence and other ones. The indirect representation approach was used because it allows the construction of a feasible solution from any input priorities. A parallel version is also proposed, making good use of multicore processors available nowadays. The results of sequential and parallel versions were very significant, improving in almost all ways the best results present in literature.

This paper addresses the image registration problem applying genetic algorithms. The image registration's objective is the definition of a mapping that best match two set of points or images. In this work the point matching problem was addressed employing a method based on nearest-neighbor. The mapping was handled by affine transformations. Experiments were conducted using three 2D synthetic point-sets with different affine transformations and noise. The results were compared against other optimization techniques. The similarity of two point-sets is measured using the Euclidean distance between matched points.

This work presents a new hybrid evolutionary algorithm for the dynamic resource constrained task scheduling problem (DRCTSP). The most important differences between the new EA and the previously proposed EAs are an intensification/diversification mechanism that tries to avoid premature convergence in local optimal solutions and a version combining an exact method (CPLEX) with EAs. Some preliminary tests were done and results are very promising.

The complexity of the static scheduling problem on heterogeneous resources has motivated the development of low complexity heuristics such as list scheduling. However, the greedy characteristic of such heuristics can, in many cases, generate poor results. This work proposes the integration of list scheduling heuristics with search mechanisms based on both genetic algorithms and GRASP, to efficiently schedule tasks on distributed systems. The results show that the hybrid approach is robust and can converge quickly to good quality solutions.

In this paper, we consider a combinatorial optimization problem that arises in telecommunications networks design. It is known as the SONET ring assignment problem (SRAP). In this problem, each client site has to be assigned to exactly one SONET ring and a special ring interconnects the other rings together. The problem is to find a feasible assignment of the client sites minimizing the total number of rings used. We describe a hybrid greedy randomized adaptive search procedure (GRASP), including the path-relinking concept, for finding good-quality solutions of the SRAP. Computational experiments on benchmark instances are reported, comparing the GRASP with path-relinking with previously proposed pure GRASP (without path-relinking) and with other algorithms found in the literature. Experimental results illustrate the effectiveness of the proposed method, over other methods, to obtain solutions that are either optimal or very close to it.

There are many oil wells in onshore fields that need artificial lift methods in Brazil. These wells periodically need some maintenance services that are performed by workover rigs, available on a limited number. The problem consists in optimizing the workover rigs schedule by minimizing the production loss associated with the yield of the wells that are waiting for service. We propose a hybrid GRASP algorithm to solve this problem. Experimental results illustrate the effectiveness of the hybrid version in terms of quality solution.

We present improvements in the performance of standard genetic algorithms (GAs) as regards the solution of highly complex combinatorial optimization problems. These improvements are related to some modifications in the GA, including local search and/or diversification procedures. The performance of each proposed version is evaluated through a graph partitioning problem. Extensive computational experiments show that our evolutionary algorithms outperform a genetic algorithm proposed in the literature, by significantly improving the quality of the final solutions with similar computational times.

This paper presents several strategies for parallel implementations of the greedy randomized adaptive search procedure (GRASP) and the variable neighborhood search (VNS) applied to a combinatorial optimization problem known as the traveling purchaser problem (TPP). Parallel algorithms based on master-worker, completely distributed and independent models, using static and dynamic load balance were proposed. The performance of these parallel algorithms was analyzed comparing them among themselves and with their sequential versions.