Narrow Results

The main objective of the proposed methodology is multi-objective job scheduling using hybridization of whale and BAT optimization algorithm (WBAT) which is used to change existing solution and to adopt a new good solution based on the objective function. The scheduling function in the proposed job scheduling strategy first creates a set of jobs and cloud node to generate the population by assigning jobs to cloud node randomly and evaluate the fitness function which minimizes the makespan and maximizes the quality of jobs. Second, the function uses iterations to regenerate populations based on WBAT behavior to produce the best job schedule that gives minimum makespan and good quality of jobs. The experimental results show that the performance of the proposed methods is better than the other methods of job scheduling problems.

Alzheimer is an irreversible neurological disorder. It impairs the memory and thinking ability of a person. Its symptoms are not known at an early stage due to which a person is deprived of receiving medication at an early stage. Dementia, a general form of Alzheimer, is difficult to diagnose and hence a proper system for detection of Alzheimer is needed. Various studies have been done for accurate classification of patients with or without Alzheimer’s disease (AD). However, accuracy of prediction is still a challenge depending on the type of data used for diagnosis. Timely identification of true positives and false negatives are critical to the diagnosis. This work focuses on extraction of optimal features using nature-inspired algorithms to enhance the accuracy of classification models. This work proposes two hybrid nature-inspired algorithms — particle swarm optimization with genetic algorithm (PSO_GA) and whale optimization algorithm with genetic algorithm, (WOA_GA) to improve prediction accuracy. The performance of proposed algorithms is evaluated with respect to various existing algorithms on the basis of accuracy and time taken. Experimental results depict that there is trade-off in time and accuracy. Results revealed that the best accuracy is achieved by PSO_GA while it takes higher time than WOA and WOA_GA. Overall WOA_GA gives better performance accuracy when compared to a majority of the compared algorithms using support vector machine (SVM) and AdaSVM classifiers.

Resonant converter (RC) was brought under research in the 80’s widely, which can attain very small switching loss, therefore, facilitating resonant topologies to function at the high switching frequency. It is well addressed in the review that the optimal parameterization of the resonant converter is a crucial task. While the literature has come out with different methodologies, they are highly conceptual and so the uncertainty due to high theoretical impact persists. This paper intends to develop a Parameter Optimization (PO) algorithm for designing and developing of LLC-RC. The proposed algorithm overwhelms the limitation by introducing a nonconceptual model based on the simulated outcome. Specifically, the resonant current under start-up conditions is acquired from the literary outcome, and the intelligent model is constructed. Based on the proposed model, a renowned search algorithm called as Whale Optimization Algorithm (WOA) is exploited to optimize the time constant of the resonant converter, which is a critical design parameter. The objective model is derived as a function of start-up time and so the start-up time can be minimized. Moreover, the response speed of the output voltage is also increased. The proposed Whale Optimization Algorithm based Parameter Optimization (WOAPO) is compared with the conventional techniques such as IAPO, Ant Bee Colony-PO (ABC-PO), Particle Swarm Optimization- PO (PSOPO), FireFly PO (FFPO) and Grey Wolf Optimization (GWOPO). The obtained result verifies the performance of the proposed method in modeling LLC-RC system.