A hybrid of firefly and improved particle swarm optimization algorithms for load balancing in cloud environments: Performance evaluation

An adaptive decoding biased random key genetic algorithm for cloud workflow scheduling

The Industrial Internet of Things is cited as the latest means for making manufacturing more flexible, cost effective, and responsive to changes in customer demands. In this paper, we present a mobile cloud based scheduling strategy for the industrial Internet of Things. Several computing paradigms, such as mobile cloud computing, fog computing, and edge computing can be integrated to the industrial Internet of Things, which allow to offload tasks to the cloud for execution. We model the task scheduling problem as an energy consumption optimization problem, while taking into account task dependency, data transmission, and some constraint conditions, such as response time deadline and cost, and further solve it by genetic algorithms. A series of simulation experiments are conducted to evaluate the performance of the algorithm and the results have shown that our proposal is more efficient than the baseline approach.

A Mobile Cloud Based Scheduling Strategy for Industrial Internet of Things

This paper presents an intelligent recognition sys-tem for handwritten Pashto letters. However, handwritten char-acter recognition is challenging due to the variations in shape and style. In addition to that, these characters naturally vary among individuals. The identification becomes even daunting due to the lack of standard datasets comprising of inscribed Pashto letters. In this work, we have designed a database of moderate size, which encompasses a total of 4488 images, stemming from 102 distinguishing samples for each of the 44 letters in Pashto. Furthermore, the recognition framework extracts zoning features followed by K-Nearest Neighbour (KNN) and Neural Network (NN) for classifying individual letters. Based on the evaluation, the proposed system achieves an overall classification accuracy of approximately 70.05% by using KNN, while an accuracy of 72% through NN at the cost of an increased computation time.

/pdf/knn-and-ann-based-recognition-of-handwritten-pashto-letters-2s4m076ves.pdf

KNN and ANN-based Recognition of Handwritten Pashto Letters using Zoning Features

In many areas, such as mobile robots, video games and driverless vehicles, path planning has always attracted researchers’ attention. In the field of mobile robotics, the path planning problem is to plan one or more viable paths to the target location from the starting position within a given obstacle space. Evolutionary algorithms can effectively solve this problem. The non-dominated sorting genetic algorithm (NSGA-II) is currently recognized as one of the evolutionary algorithms with robust optimization capabilities and has solved various optimization problems. In this paper, NSGA-II is adopted to solve multi-objective path planning problems. Three objectives are introduced. Besides the usual selection, crossover and mutation operators, some practical operators are applied. Moreover, the parameters involved in the algorithm are studied. Additionally, another evolutionary algorithm and quality metrics are employed for examination. Comparison results demonstrate that non-dominated solutions obtained by the algorithm have good characteristics. Subsequently, the path corresponding to the knee point of non-dominated solutions is shown. The path is shorter, safer and smoother. This path can be adopted in the later decision-making process. Finally, the above research shows that the revised algorithm can effectively solve the multi-objective path planning problem in static environments.

https://www.mdpi.com/2076-3417/8/11/2253/pdf

Mobile Robot Path Planning with a Non-Dominated Sorting Genetic Algorithm

In the Infrastructure-as-a-Service cloud computing model, virtualized computing resources in the form of virtual machines are provided over the Internet. A user can rent an arbitrary number of computing resources to meet their requirements, making cloud computing an attractive choice for executing real-time tasks. Economical task allocation and scheduling on a set of leased virtual machines is an important problem in the cloud computing environment. This paper proposes a greedy and a genetic algorithm with an adaptive selection of suitable crossover and mutation operations (named as AGA) to allocate and schedule real-time tasks with precedence constraint on heterogamous virtual machines. A comprehensive simulation study has been done to evaluate the performance of the proposed algorithms in terms of their solution quality and efficiency. The simulation results show that AGA outperforms the greedy algorithm and non-adaptive genetic algorithm in terms of solution quality.

https://www.mdpi.com/2073-431X/6/2/15/pdf

Hard Real-Time Task Scheduling in Cloud Computing Using an Adaptive Genetic Algorithm

In article by Mahmood [1], the results for a genetic algorithm (GA), adaptive genetic algorithm (AGA), and greedy algorithm were not correctly reported in Section 5 due to a programming error[...]

/pdf/correction-mahmood-et-al-hard-real-time-task-scheduling-in-558w19peks.pdf

Rashed A. Bahlool

Papers

Hard Real-Time Task Scheduling in Cloud Computing Using an Adaptive Genetic Algorithm

Correction: Mahmood et al. Hard Real-Time Task Scheduling in Cloud Computing Using an Adaptive Genetic Algorithm. Computers 2017, 6, 15