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	Optimal resources allocation algorithm for optional redundancy and monitoring strategies
	He, Pan1; Tan, Chun1; Yuan, Yue1; Wu, Kaigui2
	2016
摘要	In big data environment, the use of optional redundancy and monitoring strategy in one system increases the usage of resource and causes state space expansion for optimal resources allocation model. The performance of existing evolutionary search algorithms should be improved for the solution space formed by both integer and non-integer variables. To improve the algorithm efficiency, a memetic algorithm based on triple element array is proposed on the analysis of search neighborhood. First of all, the impact of change of variables such as monitoring rate on the system reliability increase is analyzed and then changing-length neighbor generation method is proposed for monitoring rate on neighbor analysis. The neighbor generation method is also proposed for strategy options considering the relations between components. After that, local search operator is refined through the iterative search among components, which increases the search range while maintaining the local advantage of individuals. This operator is used for improving the whole framework of memetic algorithm. Experiment results indicates that this algorithm can be used to get the solution of strategy option of each component and the corresponding optimized parameters for multiple optional strategies. Compared with existing multi-strategy search algorithms, the improved memetic algorithm could get better resources allocation results under the same reliability constraint. The local search operator does not have great impact on the stability of the whole algorithm. © 2016, Science Press. All right reserved.
DOI	10.7544/issn1000-1239.2016.20148204
发表期刊	Jisuanji Yanjiu yu Fazhan/Computer Research and Development
ISSN	10001239
卷号	53期号:3页码:682-696
通讯作者	Yuan, Yue (yuanyue@cigit.ac.cn)
收录类别	EI
语种	中文