In this paper, a strong limit theorem on gambling strategy for binary Bernoulli sequence, (i.e.) irregularity theorem, is extended to random selection for dependent m-valued random variables, via using a new method-di...In this paper, a strong limit theorem on gambling strategy for binary Bernoulli sequence, (i.e.) irregularity theorem, is extended to random selection for dependent m-valued random variables, via using a new method-differentiability on net. Furthermore, by allowing the selection function to take value in finite interval [-M,M], the conception of random selection is generalized.展开更多
Combining the characteristics of peer-to-peer （P2P） and grid, a super-peer selection algorithm--SSABC is presented in the distributed network merging P2P and grid. The algorithm computes nodes capacities using their...Combining the characteristics of peer-to-peer （P2P） and grid, a super-peer selection algorithm--SSABC is presented in the distributed network merging P2P and grid. The algorithm computes nodes capacities using their resource properties provided by a grid monitoring and discovery system, such as available bandwidth, free CPU and idle memory, as well as the number of current connections and online time. when a new node joins the network and the super-peers are all saturated, it should select a new super-peer from the new node or joined nodes with the highest capacity. By theoretical analyses and simulation experiments, it is shown that super-peers selected by capacity can achieve higher query success rates and shorten the average hop count when compared with super-peers selected randomly, and they can also balance the network load when all super-peers are saturated. When the number of total nodes changes, the conclusion is still valid, which explains that the algorithm SSABC is feasible and stable.展开更多
The gravitational search algorithm (GSA) is a population-based heuristic optimization technique and has been proposed for solving continuous optimization problems. The GSA tries to obtain optimum or near optimum solut...The gravitational search algorithm (GSA) is a population-based heuristic optimization technique and has been proposed for solving continuous optimization problems. The GSA tries to obtain optimum or near optimum solution for the optimization problems by using interaction in all agents or masses in the population. This paper proposes and analyzes fitness-based proportional (rou- lette-wheel), tournament, rank-based and random selection mechanisms for choosing agents which they act masses in the GSA. The proposed methods are applied to solve 23 numerical benchmark functions, and obtained results are compared with the basic GSA algorithm. Experimental results show that the proposed methods are better than the basic GSA in terms of solution quality.展开更多
文摘In this paper, a strong limit theorem on gambling strategy for binary Bernoulli sequence, (i.e.) irregularity theorem, is extended to random selection for dependent m-valued random variables, via using a new method-differentiability on net. Furthermore, by allowing the selection function to take value in finite interval [-M,M], the conception of random selection is generalized.
基金The National High Technology Research and Development Program of China (863 Program) (No.2007AA01Z422)the NaturalFoundation of Anhui Provincial Education Department (No.2006KJ041B,KJ2007B073)
文摘Combining the characteristics of peer-to-peer （P2P） and grid, a super-peer selection algorithm--SSABC is presented in the distributed network merging P2P and grid. The algorithm computes nodes capacities using their resource properties provided by a grid monitoring and discovery system, such as available bandwidth, free CPU and idle memory, as well as the number of current connections and online time. when a new node joins the network and the super-peers are all saturated, it should select a new super-peer from the new node or joined nodes with the highest capacity. By theoretical analyses and simulation experiments, it is shown that super-peers selected by capacity can achieve higher query success rates and shorten the average hop count when compared with super-peers selected randomly, and they can also balance the network load when all super-peers are saturated. When the number of total nodes changes, the conclusion is still valid, which explains that the algorithm SSABC is feasible and stable.
基金supported by Scientific Research Project of Selçuk University
文摘The gravitational search algorithm (GSA) is a population-based heuristic optimization technique and has been proposed for solving continuous optimization problems. The GSA tries to obtain optimum or near optimum solution for the optimization problems by using interaction in all agents or masses in the population. This paper proposes and analyzes fitness-based proportional (rou- lette-wheel), tournament, rank-based and random selection mechanisms for choosing agents which they act masses in the GSA. The proposed methods are applied to solve 23 numerical benchmark functions, and obtained results are compared with the basic GSA algorithm. Experimental results show that the proposed methods are better than the basic GSA in terms of solution quality.
