在各种软件开发语言中,Java语言作为一种跨平台的编程语言,"一次编译,到处运行"的特点,极大降低了跨平台软件开发的复杂度。JVM内存的合理使用对虚拟机稳定、优化地工作具有重要的作用。本文以Sun Hot Spot 1.6为例,主要探讨...在各种软件开发语言中,Java语言作为一种跨平台的编程语言,"一次编译,到处运行"的特点,极大降低了跨平台软件开发的复杂度。JVM内存的合理使用对虚拟机稳定、优化地工作具有重要的作用。本文以Sun Hot Spot 1.6为例,主要探讨并验证Serial收集器的内存分配与回收策略。展开更多
An improved genetic algorithm for searching optimal parameters in n-dimensional space is presented, which encodes movement direction and distance and searches from coarse to precise. The algorithm can realize global o...An improved genetic algorithm for searching optimal parameters in n-dimensional space is presented, which encodes movement direction and distance and searches from coarse to precise. The algorithm can realize global optimization and improve the search efficiency, and can be applied effectively in industrial optimization, data mining and pattern recognition.展开更多
In rechargeable wireless sensor networks, a sensor cannot be always benefi cial to conserve energy when a network can harvest excessive energy from the environment due to its energy replenished continually and limited...In rechargeable wireless sensor networks, a sensor cannot be always benefi cial to conserve energy when a network can harvest excessive energy from the environment due to its energy replenished continually and limited energy storage capacity. Therefore, surplus energy of a node can be utilized for strengthening packet delivery efficiency and improving data collection rate. In this work, we propose an algorithm to compute an upper data generation rate that maximizes it as an optimization problem for a network with multiple sinks, which is formulated as a linear programming problem. Subsequently, a dual problem by introducing Lagrange multipliers is constructed, and subgradient algorithms are used to solve it in a distributed manner. The resulting algorithms are guaranteed to converge to an optimal data generation rate, which are illustrated by an example in which an optimum data generation rate is computed for a network of randomly distributed nodes. Through extensive simulation and experiments, we demonstrate our algorithm is efficient to maximize data collection rate in rechargeable wireless sensor networks.展开更多
文摘An improved genetic algorithm for searching optimal parameters in n-dimensional space is presented, which encodes movement direction and distance and searches from coarse to precise. The algorithm can realize global optimization and improve the search efficiency, and can be applied effectively in industrial optimization, data mining and pattern recognition.
基金supported by The Natural Science Foundation of Jiangsu Province of China(Grant No.BK20141474)funded by China Postdoctoral Science Foundation(Grant No.2015M571639)+3 种基金three Projects Funded by The Jiangsu Planned Projects for Postdoctoral Research Funds(Grant No.1402018C)The Key Laboratory of Computer Network and Information Integration(Southeast University)Ministry of Education(Grant No.K93-9-2015-09C)The Priority Academic Program Development(PAPD)of Jiangsu Higher Education Institutions
文摘In rechargeable wireless sensor networks, a sensor cannot be always benefi cial to conserve energy when a network can harvest excessive energy from the environment due to its energy replenished continually and limited energy storage capacity. Therefore, surplus energy of a node can be utilized for strengthening packet delivery efficiency and improving data collection rate. In this work, we propose an algorithm to compute an upper data generation rate that maximizes it as an optimization problem for a network with multiple sinks, which is formulated as a linear programming problem. Subsequently, a dual problem by introducing Lagrange multipliers is constructed, and subgradient algorithms are used to solve it in a distributed manner. The resulting algorithms are guaranteed to converge to an optimal data generation rate, which are illustrated by an example in which an optimum data generation rate is computed for a network of randomly distributed nodes. Through extensive simulation and experiments, we demonstrate our algorithm is efficient to maximize data collection rate in rechargeable wireless sensor networks.
