Aiming at the trouble to track the optima in dynamic environments with estimation of distribution algorithms (EDAs). An estimation of distribution algorithm with scatter search (EDASS) is proposed in this paper. Its basic idea is to employ a scatter search to increase the diversity in a guided fashion and an adaptive leader clustering method to locate multiple local optima. Both the information of current population and the part history information were referred for building probability model. The experimental results show that the EDASS is effective for dynamic optimisation problems.
Resources scheduling is a major challenge in cloud computing because of its ability to provide many on-demand information technology services according to needs of customers. In order to acquire the best balance between speed of operation, average response time, and integrated system utilization in the resource allocation process in cloud computing, an improved bat algorithm with time-varying wavelet perturbations was proposed. The algorithm provided a perturbation strategy of time-varying Morlet wavelet with the waving property to prevent from local optimum greatly and improve the converging speed and accuracy through the guide of individual distribution to control diversity and time-varying coefficient of wavelets. The experiments showed the proposed could significantly upgrade the overall performance and the capability of resource scheduling in cloud service compared to similar algorithms.
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