RADL: a resource and deadline‑aware dynamic load‑balancer for cloud tasks

Cloud service providers acquire the computing resources and allocate them to their clients. To effectively utilize the resources and achieve higher user satisfaction, efficient task scheduling algorithms play a very pivotal role. A number of task scheduling technique have been proposed in the literature. However, majority of these scheduling algorithms fail to achieve efficient resource utilization that causes them to miss tasks deadlines. This is because these algorithms are not resource and deadline-aware. In this research, a Resource and deadline Aware Dynamic Load-balancer (RADL) for Cloud, tasks have been presented. The proposed scheduling scheme evenly distribute the incoming workload of compute-intensive and independent tasks at run-time. In addition, RADL approach has the capability to accommodate the newly arrived tasks (with shorter deadlines) efficiently and reduce task rejection. The proposed scheduler monitors/updates the task and VM status at run-time. Experimental results show that the proposed technique has attained up to 67.74%, 303.57%, 259.2%, 146.13%, 405.06%, and 259.14% improvement for average resource utilization, meeting tasks deadlines, lower makespan, task response time, penalty cost, and task execution cost respectively as compared to the state-of-the-art tasks scheduling heuristics using three benchmark datasets

Rulet Elektromanyetik Alan Optimizasyon (R-EFO) Algoritması

Meta-sezgisel optimizasyon algoritmalarının yerel arama performansları üzerinde etkili olan iki temel öğe seçim yöntemleri ve arama operatörleridir. Bu makale çalışmasında olasılıksal bir seçim yöntemi olan rulet tekerleğinin güncel bir meta-sezgisel arama tekniği olan elektromanyetik alan optimizasyon (electromagnetic field optimization, EFO) algoritmasının yerel arama performansı üzerindeki etkisi araştırılmaktadır. Elektromanyetik optimizasyon algoritmasında çözüm adayları topluluğu uygunluk değerlerine bağlı olarak pozitif, nötr ve negatif alanlara ayrılmaktadır. Bu üç alandan seçilen çözüm adayları ise arama sürecine rehberlik etmektedirler. Bu süreçte çözüm adayları açgözlü ve rastgele seçim yöntemleri ile belirlenmektedir. Bu makale çalışmasında ise negatif alandan çözüm adaylarının seçimi için rulet tekniği kullanılmaktadır. Deneysel çalışmalarda literatürdeki en güncel sürekli değer problemleri olan CEC17 test seti kullanılmıştır. Deneysel çalışma sonuçları istatistiksel olarak ikili karşılaştırmalarda kullanılan wilcoxon runk sum test ile analiz edilmiştir. Analiz sonuçlarına göre rulet seçim yöntemi EFO algoritmasının arama performansını kayda değer şekilde artırmaktadır

Comprehensive Taxonomies of Nature- and Bio-inspired Optimization: Inspiration versus Algorithmic Behavior, Critical Analysis and Recommendations

In recent years, a great variety of nature- and bio-inspired algorithms has been reported in the literature. This algorithmic family simulates different biological processes observed in Nature in order to efficiently address complex optimization problems. In the last years the number of bio-inspired optimization approaches in literature has grown considerably, reaching unprecedented levels that dark the future prospects of this field of research. This paper addresses this problem by proposing two comprehensive, principle-based taxonomies that allow researchers to organize existing and future algorithmic developments into well-defined categories, considering two different criteria: the source of inspiration and the behavior of each algorithm. Using these taxonomies we review more than three hundred publications dealing with nature-inspired and bio-inspired algorithms, and proposals falling within each of these categories are examined, leading to a critical summary of design trends and similarities between them, and the identification of the most similar classical algorithm for each reviewed paper. From our analysis we conclude that a poor relationship is often found between the natural inspiration of an algorithm and its behavior. Furthermore, similarities in terms of behavior between different algorithms are greater than what is claimed in their public disclosure: specifically, we show that more than one-third of the reviewed bio-inspired solvers are versions of classical algorithms. Grounded on the conclusions of our critical analysis, we give several recommendations and points of improvement for better methodological practices in this active and growing research field.Comment: 76 pages, 6 figure

Improved Slime-Mould-Algorithm with Fitness Distance Balance-based Guiding Mechanism for Global Optimization Problems

In this study, the performance of Slime-Mould-Algorithm (SMA), a current Meta-Heuristic Search algorithm, is improved. In order to model the search process lifecycle process more effectively in the SMA algorithm, the solution candidates guiding the search process were determined using the fitness-distance balance (FDB) method. Although the performance of the SMA algorithm is accepted, it is seen that the performance of the FDB-SMA algorithm developed thanks to the applied FDB method is much better. CEC 2020, which has current benchmark problems, was used to test the performance of the developed FDB-SMA algorithm. 10 different unconstrained comparison problems taken from CEC 2020 are designed by arranging them in 30-50-100 dimensions. Experimental studies were carried out using the designed comparison problems and analyzed with Friedman and Wilcoxon statistical test methods. According to the results of the analysis, it has been seen that the FDB-SMA variations outperform the basic algorithm (SMA) in all experimental studies

Single- and multi-objective modified aquila optimizer for optimal multiple renewable energy resources in distribution network

Nowadays, the electrical power system has become a more complex, interconnected network that is expanding every day. Hence, the power system faces many problems such as increasing power losses, voltage deviation, line overloads, etc. The optimization of real and reactive power due to the installation of energy resources at appropriate buses can minimize the losses and improve the voltage profile, especially for congested networks. As a result, the optimal distributed generation allocation (ODGA) problem is considered a more proper tool for the processes of planning and operation of power systems due to the power grid changes expeditiously based on the type and penetration level of renewable energy sources (RESs). This paper modifies the AO using a quasi-oppositional-based learning operator to address this problem and reduce the burden on the primary grid, making the grid more resilient. To demonstrate the effectiveness of the MAO, the authors first test the algorithm performance on twenty-three competitions on evolutionary computation benchmark functions, considering different dimensions. In addition, the modified Aquila optimizer (MAO) is applied to tackle the optimal distributed generation allocation (ODGA) problem. The proposed ODGA methodology presented in this paper has a multi-objective function that comprises decreasing power loss and total voltage deviation in a distribution system while keeping the system operating and security restrictions in mind. Many publications investigated the effect of expanding the number of DGs, whereas others found out the influence of DG types. Here, this paper examines the effects of different types and capacities of DG units at the same time. The proposed approach is tested on the IEEE 33-bus in different cases with several multiple DG types, including multi-objectives. The obtained simulation results are compared to the Aquila optimizer, particle swarm optimization algorithm, and trader-inspired algorithm. According to the comparison, the suggested approach provides a superior solution for the ODGA problem with faster convergence in the DN

Advances in Artificial Intelligence: Models, Optimization, and Machine Learning

The present book contains all the articles accepted and published in the Special Issue “Advances in Artificial Intelligence: Models, Optimization, and Machine Learning” of the MDPI Mathematics journal, which covers a wide range of topics connected to the theory and applications of artificial intelligence and its subfields. These topics include, among others, deep learning and classic machine learning algorithms, neural modelling, architectures and learning algorithms, biologically inspired optimization algorithms, algorithms for autonomous driving, probabilistic models and Bayesian reasoning, intelligent agents and multiagent systems. We hope that the scientific results presented in this book will serve as valuable sources of documentation and inspiration for anyone willing to pursue research in artificial intelligence, machine learning and their widespread applications

Power system performance improvement in the presence of renewable sources

Electromechanical oscillations is a phenomenon in which a generator oscillates against other generators in the power system, the damping of these oscillations has therefore become a priority objective, The objective of our work is to ensure maximum damping of low frequency oscillations and to guarantee the overall stability of the system for different operating points by the use of power stabilizers (PSSs). To achieve this goal, we developed an improved metaheuristic optimization method based on the crows search algorithm (CSA) applied on an objective function extracted from the eigenvalue analysis of the power system. A comparative study was made, with a classic stabilizer, genetic algorithm-based PSS (GA-PSS), a particle-swarm-based PSS (PSO-PSS) and other stabilizers based on recent algorithms. The performances of these optimization methods were evaluated on a single machine connected to an infinite bus (SMIB) via a linear model time domain simulation. On the other hand, the effect of integrating a photovoltaic PV generator on the stability of the power system is presented, as well as solutions to increase the amount of integration of the PV generator without losing the stability of the system