Virtual Machine Deployment Strategy Based on Improved PSO in Cloud Computing

Energy consumption is an important cost driven by growth of computing power, thereby energy conservation has become one of the major problems faced by cloud system. How to maximize the utilization of physical machines, reduce the number of virtual machine migrations, and maintain load balance under the constraints of physical machine resource thresholds that is the effective way to implement energy saving in data center. In the paper, we propose a multi-objective physical model for virtual machine deployment. Then the improved multi-objective particle swarm optimization (TPSO) is applied to virtual machine deployment. Compared to other algorithms, the algorithm has better ergodicity into the initial stage, improves the optimization precision and optimization efficiency of the particle swarm. The experimental results based on CloudSim simulation platform show that the algorithm is effective at improving physical machine resource utilization, reducing resource waste, and improving system load balance

A Novel Double Cluster and Principal Component Analysis-Based Optimization Method for the Orbit Design of Earth Observation Satellites

The weighted sum and genetic algorithm-based hybrid method (WSGA-based HM), which has been applied to multiobjective orbit optimizations, is negatively influenced by human factors through the artificial choice of the weight coefficients in weighted sum method and the slow convergence of GA. To address these two problems, a cluster and principal component analysis-based optimization method (CPC-based OM) is proposed, in which many candidate orbits are gradually randomly generated until the optimal orbit is obtained using a data mining method, that is, cluster analysis based on principal components. Then, the second cluster analysis of the orbital elements is introduced into CPC-based OM to improve the convergence, developing a novel double cluster and principal component analysis-based optimization method (DCPC-based OM). In DCPC-based OM, the cluster analysis based on principal components has the advantage of reducing the human influences, and the cluster analysis based on six orbital elements can reduce the search space to effectively accelerate convergence. The test results from a multiobjective numerical benchmark function and the orbit design results of an Earth observation satellite show that DCPC-based OM converges more efficiently than WSGA-based HM. And DCPC-based OM, to some degree, reduces the influence of human factors presented in WSGA-based HM

Multiobjective differential evolution enhanced with principle component analysis for constrained optimization

Multiobjective evolutionary algorithms (MOEAs) have been successfully applied to a number of constrained optimization problems. Many of them adopt mutation and crossover operators from differential evolution. However, these operators do not explicitly utilise features of fitness landscapes. To improve the performance of algorithms, this paper aims at designing a search operator adapting to fitness landscapes. Through an observation, we find that principle component analysis (PCA) can be used to characterise fitness landscapes. Based on this finding, a new search operator, called PCA-projection, is proposed. In order to verify the effectiveness of PCA-projection, we design two algorithms enhanced with PCA-projection for solving constrained optimization problems, called PMODE and HECO-PDE, respectively. Experiments have been conducted on the IEEE CEC 2017 competition benchmark suite in constrained optimization. PMODE and HECO-PDE are compared with the algorithms from the IEEE CEC 2018 competition and another recent MOEA for constrained optimization. Experimental results show that an algorithm enhanced with PCA-projection performs better than its corresponding opponent without this operator. Furthermore, HECO-PDE is ranked first on all dimensions according to the competition rules. This study reveals that decomposition-based MOEAs, such as HECO-PDE, are competitive with best single-objective and multiobjective evolutionary algorithms for constrained optimization, but MOEAs based on non-dominance, such as PMODE, may not

Enhanced particle swarm optimization based on principal component analysis and line search

Particle swarm optimization (PSO) guides its search direction by a linear learning strategy, in which each particle updates its velocity through a linear combination among its present status, historical best experience and the swarm best experience. The current position of each particle can be seen as a velocity accumulator. Such a storage strategy is easy to achieve, however, it is inefficient when searching in a complex space and has a great restriction on the achieved heuristic information for the promising solutions. Therefore, a new PSO searching mechanism (PCA-PSO) is proposed based on principal component analysis (PCA) and Line Search (LS), in which PCA is mainly used to efficiently mine population information for the promising principal component directions and then LS strategy is utilized on them. PCA-PSO can inherit most of the velocity information of all the particles to guide them to the most promising directions, which have great difference in learning mechanism with usual PSOs. Experimental results and extensive comparisons with hybrid PSOs, pPSA, PCPSO, CLPSO, GL-25, and CoDE show that PCA-PSO consistently and significantly outperforms some PSO variants and is competitive for other state-of-the-art algorithms. © 2013 Elsevier Inc. All rights reserved

Análisis de componentes principales disjuntas por medio de optimización por enjambre de partículas y sus aplicaciones

[ES] En el presente trabajo se divide en cuatro capítulos. En los dos primeros se propone una alternativa al método disjoint principal component analysis, que consiste en un análisis de componentes principales con restricciones y permite determinar componentes disjuntas, que son combinaciones lineales de subconjuntos disjuntos de las variables consideradas en el problema. El nuevo método propuesto se denomina constrained binary optimization by particle swarm disjoint principal component analysis, debido a que está basado en la optimización por enjambre de partículas, notado por CBPSO DC. El nuevo método usa una optimización estocástica diseñada para encontrar soluciones de alta calidad, en situaciones de alta complejidad computacional. El algoritmo del nuevo método parte generando aleatoriamente una población de partículas que iterativamente evolucionan hasta alcanzar el óptimo global, que en este caso está dado en función de las componentes disjuntas. Se proporcionan resultados numéricos que confirman la calidad de las soluciones obtenidas por el nuevo método. En el tercer capítulo se realiza una adaptación de la optimización por enjambre de partículas al análisis HJ Biplot. Este nuevo método recibe el nombre de PSO DHJ Biplot. Además, se realiza una aplicación a datos sobre la evolución de la pandemia COVID-19. En el capítulo cuatro se presentan los contenidos teóricos necesarios para abordar la estadística de datos funcionales. Se realiza una exposición de los operadores lineales sobre espacios de Hilbert. La función de covarianzas de un conjunto de datos funcionales es un ejemplo de este tipo de operadores. A continuación, se enuncia el teorema de Mercer y el teorema de Karhunen-Loève que son los dos pilares en que se sustenta el análisis de componentes principales funcionales. Se definen las componentes principales funcionales y se detalla su forma de cálculo. Se aplica el algoritmo CBPSO DC para obtener las denominadas componentes principales funcionales disjuntas