For accessing required services in cloud computing, user submits its task to the cloud datacentre for processing. Therefore, two challenges have been faced by datacentre controllers such as finding the best resources ...
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For accessing required services in cloud computing, user submits its task to the cloud datacentre for processing. Therefore, two challenges have been faced by datacentre controllers such as finding the best resources and mapping user tasks to virtual machines (VMs). To solve these issues, this paper presented a scheduling algorithm named as Modified parallelparticleswarm Optimization (MPPSO). This algorithm is based on the parallel PSO algorithm which reduces the processing time and dynamically adjust the load of each VM that VM can take part in task processing. By using the CloudSim simulator, MPPSO approach is tested against parallelparticleswarm Optimization (PPSO) and Modified particleswarm Optimization (MPSO) algorithm by taking different task and VM sets. From the result our proposed algorithm reduce execution time, makespan time and waiting time by 16%, 15% and 19% while increase the throughput and fitness function value by 16% and 17% respectively.
Computational efficiency is a major challenge for evolutionary algorithm (EA)-based antenna optimisation methods due to the computationally expensive electromagnetic simulations. Surrogate model-assisted EAs considera...
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Computational efficiency is a major challenge for evolutionary algorithm (EA)-based antenna optimisation methods due to the computationally expensive electromagnetic simulations. Surrogate model-assisted EAs considerably improve the optimisation efficiency, but most of them are sequential methods, which cannot benefit from parallel simulation of multiple candidate designs for further speed improvement. To address this problem, a new method, called parallel surrogate model-assisted hybrid differential evolution for antenna optimisation (PSADEA), is proposed. The performance of PSADEA is demonstrated by a dielectric resonator antenna, a Yagi-Uda antenna, and three mathematical benchmark problems. Experimental results show high operational performance in a few hours using a normal desktop 4-core workstation. Comparisons show that PSADEA possesses significant advantages in efficiency compared to a state-of-the-art surrogate model-assisted EA for antenna optimisation, the standard parallel differential evolution algorithm, and parallel particle swarm optimisation. In addition, PSADEA also shows stronger optimisation ability compared to the above reference methods for challenging design cases.
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