Search

Cloud computing is becoming irresistible considering its benefits, such as low maintenance, up-front costs and ease of scaling. On the other hand, the proliferation of cloud users is causing the expansion of more data centres that require a lot of power. As such, it triggered the problem of energy consumption in data centre storage systems and emission of carbon footprints in the cloud environments. To mitigate the problems, many approaches based on hybrid GA-PSO were proposed in the literature for tasks scheduling in the cloud infrastructure to efficiently manage cloud resources, enhance energy efficiency and quality of service (QoS). The already discussed GA-PSO typically applied randomization in generating initial population. However, in solving tasks scheduling problems, randomness slows convergence speed of the algorithm. In this paper, we propose an enhanced hybrid of Genetic algorithm (GA) and Particle Swarm Optimization (PSO) (GA-PSO) by applying small position values (SPV) to generate initial population to deviate from the limitation of the randomness and improve convergence speed. The proposed enhance GA-PSO with SPV is applied to efficiently schedule tasks to cloud computing resources. The result indicated that the propose GA-PSO perform better than the classical hybrid GA-PSO algorithm in terms of makespan and resource utilization.

Feature selection (FS) aims to select the subsets of the most informative features by ignoring the redundant ones and consequently, improving the classification performance. Hence, consider as a two objective optimisation problem. Moreover, most of the existing work treats FS as single-objective by combining the two aims into a single fitness function. As such, there is a trade-off between the number of selected features and classification performance. To create a balance between the conflicting aim of the FS and yet improve classification performance, this study proposes the use of nondominated sorting genetic algorithm NSGAIII. Filter-based FS are scalable to large dimensional datasets and computationally fast. However, their classification performance is low because they lack feature interaction among the selected subset of features. Based on that mutual information (MI) along with entropy, are proposed as a filter-based evaluation measure along with the NSGAIII to have NSGAIIIMI and NSGAIIIE. The results obtained was compared with the existing single-objective, NSGAII as well as strength Pareto evolutionary algorithm with both MI and entropy. NSGAIII can successfully evolve the set of nondominated solutions and performs better in terms of the number of selected features, classification error rate and computational time on the majority of the datasets.