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 state transition rule 状态转移规则(1)
 状态转移规则
 The ant colony system is introduced and a new algorithm for TSP is presented. This algorithm is a distributed parallel algorithm with a new state transition rule and new global updating rule. 从介绍蚂蚁系统出发，提出了新的状态转移规则及全局修正规则，结合这些新规则设计出一个新算法，该算法是一个分布式并行算法。 短句来源

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 state transition rule
 Based on the state transition rule, the local updating rule and the global updating rule of ant colony algorithm, we propose an improved ant colony algorithm of the least-cost quality of service (QoS) unicast routing. One final modification must be made to the state transition rule. Non-uniform CA differ from their uniform counterpart in the state transition rule diversity exhibited by the non-uniform ones.
 The ant colony system is introduced and a new algorithm for TSP is presented. This algorithm is a distributed parallel algorithm with a new state transition rule and new global updating rule. Experiment results demonstrate that the algorithm is powerful and effective. 从介绍蚂蚁系统出发，提出了新的状态转移规则及全局修正规则，结合这些新规则设计出一个新算法，该算法是一个分布式并行算法。计算机试验结果表明，该算法对求解ＴＳＰ问题十分有效。 Ant Colony Optimization(ACO)algorithm is a kind of meta-heuristic algorithm for solving NP-hard problems.Using positive feedback and parallel-computing principle,it has strong capability to explore solution.Recently,ACO algorithm is widely used to study TSP problems.This paper proposes Branching Ant Colony with Dynamic Perturbation(DPBAC)algorithm,which improves traditional ACO algorithm in 5 aspects:introducing Branching Method to choose starting points,improving state transition rules,introducing Mutation... Ant Colony Optimization(ACO)algorithm is a kind of meta-heuristic algorithm for solving NP-hard problems.Using positive feedback and parallel-computing principle,it has strong capability to explore solution.Recently,ACO algorithm is widely used to study TSP problems.This paper proposes Branching Ant Colony with Dynamic Perturbation(DPBAC)algorithm,which improves traditional ACO algorithm in 5 aspects:introducing Branching Method to choose starting points,improving state transition rules,introducing Mutation Method to shorten tours,improving pheromone updating rules and introducing Conditional Dynamic Perturbation Method.Showed by simulated experiments,DPBAC algorithm can improve the shortcomings,as costing too much time and tending to stick in local minimum and so on,of traditional ACO algorithm. 蚁群优化算法是一种求解组合优化难题的强启发式算法,它利用正反馈和并行计算原理,具备很强的搜索能力。近年来,蚁群优化算法广泛应用于TSP问题的研究。本文提出分支蚁群动态扰动(DPBAC)算法,该算法主要从5个方面对基本蚁群算法做出改进:引入分支策略选取出发城市;改进状态转移规则;引入变异策略改进蚂蚁路径;改进信息素更新规则;引入条件动态扰动策略。实验表明,该算法可以有效改善基本蚁群算法搜索时间较长、容易陷入局部极小等缺点。
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