Good practice proposal for the implementation, presentation, and comparison of metaheuristics for solving routing problems

CloudOps: Towards the Operationalization of the Cloud Continuum: Concepts, Challenges and a Reference Framework

The current trend of developing highly distributed, context aware, heterogeneous computing intense and data-sensitive applications is changing the boundaries of cloud computing. Encouraged by the growing IoT paradigm and with flexible edge devices available, an ecosystem of a combination of resources, ranging from high density compute and storage to very lightweight embedded computers running on batteries or solar power, is available for DevOps teams from what is known as the Cloud Continuum. In this dynamic context, manageability is key, as well as controlled operations and resources monitoring for handling anomalies. Unfortunately, the operation and management of such heterogeneous computing environments (including edge, cloud and network services) is complex and operators face challenges such as the continuous optimization and autonomous (re-)deployment of context-aware stateless and stateful applications where, however, they must ensure service continuity while anticipating potential failures in the underlying infrastructure. In this paper, we propose a novel CloudOps workflow (extending the traditional DevOps pipeline), proposing techniques and methods for applications’ operators to fully embrace the possibilities of the Cloud Continuum. Our approach will support DevOps teams in the operationalization of the Cloud Continuum. Secondly, we provide an extensive explanation of the scope, possibilities and future of the CloudOps.

Review of Metaheuristics Inspired from the Animal Kingdom

The search for powerful optimizers has led to the development of a multitude of metaheuristic algorithms inspired from all areas. This work focuses on the animal kingdom as a source of inspiration and performs an extensive, yet not exhaustive, review of the animal inspired metaheuristics proposed in the 2006–2021 period. The review is organized considering the biological classification of living things, with a breakdown of the simulated behavior mechanisms. The centralized data indicated that 61.6% of the animal-based algorithms are inspired from vertebrates and 38.4% from invertebrates. In addition, an analysis of the mechanisms used to ensure diversity was performed. The results obtained showed that the most frequently used mechanisms belong to the niching category.

Q-Learnheuristics: Towards Data-Driven Balanced Metaheuristics

One of the central issues that must be resolved for a metaheuristic optimization process to work well is the dilemma of the balance between exploration and exploitation. The metaheuristics (MH) that achieved this balance can be called balanced MH, where a Q-Learning (QL) integration framework was proposed for the selection of metaheuristic operators conducive to this balance, particularly the selection of binarization schemes when a continuous metaheuristic solves binary combinatorial problems. In this work the use of this framework is extended to other recent metaheuristics, demonstrating that the integration of QL in the selection of operators improves the exploration-exploitation balance. Specifically, the Whale Optimization Algorithm and the Sine-Cosine Algorithm are tested by solving the Set Covering Problem, showing statistical improvements in this balance and in the quality of the solutions.

Surveillance Routing of COVID-19 Infection Spread Using an Intelligent Infectious Diseases Algorithm

In this study, the Intelligent Infectious Diseases Algorithm (IIDA) has been developed to locate the sources of infection and survival rate of coronavirus disease 2019 (COVID-19), in order to propose health care routes for population affected by COVID-19. The main goal of this computational algorithm is to reduce the spread of the virus and decrease the number of infected people. To do so, health care routes are generated according to the priority of certain population groups. The algorithm was applied to New York state data. Based on infection rates and reported deaths, hot spots were determined by applying the kernel density estimation (KDE) to the groups that have been previously obtained using a clustering algorithm together with the elbow method. For each cluster, the survival rate -the key information to prioritize medical care- was determined using the proportional hazards model. Finally, ant colony optimization (ACO) and the traveling salesman problem (TSP) optimization algorithms were applied to identify the optimal route to the closest hospital. The results obtained efficiently covered the points with the highest concentration of COVID-19 cases. In this way, its spread can be prevented and health resources optimized.

COEBA: A Coevolutionary Bat Algorithm for Discrete Evolutionary Multitasking

Multitasking optimization is an emerging research field which has attracted lot of attention in the scientific community. The main purpose of this paradigm is how to solve multiple optimization problems or tasks simultaneously by conducting a single search process. The main catalyst for reaching this objective is to exploit possible synergies and complementarities among the tasks to be optimized, helping each other by virtue of the transfer of knowledge among them (thereby being referred to as Transfer Optimization). In this context, Evolutionary Multitasking addresses Transfer Optimization problems by resorting to concepts from Evolutionary Computation for simultaneous solving the tasks at hand. This work contributes to this trend by proposing a novel algorithmic scheme for dealing with multitasking environments. The proposed approach, coined as Coevolutionary Bat Algorithm, finds its inspiration in concepts from both co-evolutionary strategies and the metaheuristic Bat Algorithm. We compare the performance of our proposed method with that of its Multifactorial Evolutionary Algorithm counterpart over 15 different multitasking setups, composed by eight reference instances of the discrete Traveling Salesman Problem. The experimentation and results stemming therefrom support the main hypothesis of this study: the proposed Coevolutionary Bat Algorithm is a promising meta-heuristic for solving Evolutionary Multitasking scenarios.

Combination of Data-Driven Active Disturbance Rejection and Takagi-Sugeno Fuzzy Control with Experimental Validation on Tower Crane Systems

In this paper a second-order data-driven Active Disturbance Rejection Control (ADRC) is merged with a proportional-derivative Takagi-Sugeno Fuzzy (PDTSF) logic controller, resulting in two new control structures referred to as second-order data-driven Active Disturbance Rejection Control combined with Proportional-Derivative Takagi-Sugeno Fuzzy Control (ADRC–PDTSFC). The data-driven ADRC–PDTSFC structure was compared with a data-driven ADRC structure and the control system structures were validated by real-time experiments on a nonlinear Multi Input-Multi Output tower crane system (TCS) laboratory equipment, where the cart position and the arm angular position of TCS were controlled using two Single Input-Single Output control system structures running in parallel. The parameters of the data-driven algorithms were tuned in a model-based way using a metaheuristic algorithm in order to improve the efficiency of energy consumption.

Smart Bandwidth Assignation in an Underlay Cellular Network for Internet of Vehicles

The evolution of the IoT (Internet of Things) paradigm applied to new scenarios as VANETs (Vehicular Ad Hoc Networks) has gained momentum in recent years. Both academia and industry have triggered advanced studies in the IoV (Internet of Vehicles), which is understood as an ecosystem where different types of users (vehicles, elements of the infrastructure, pedestrians) are connected. How to efficiently share the available radio resources among the different types of eligible users is one of the important issues to be addressed. This paper briefly analyzes various concepts presented hitherto in the literature and it proposes an enhanced algorithm for ensuring a robust co-existence of the aforementioned system users. Therefore, this paper introduces an underlay RRM (Radio Resource Management) methodology which is capable of (1) improving cellular spectral efficiency while making a minimal impact on cellular communications and (2) ensuring the different QoS (Quality of Service) requirements of ITS (Intelligent Transportation Systems) applications. Simulation results, where we compare the proposed algorithm to the other two RRM, show the promising spectral efficiency performance of the proposed RRM methodology.