Ant colony optimization （ACO） is a new heuristic algo- rithm which has been proven a successful technique and applied to a number of combinatorial optimization problems. The traveling salesman problem （TSP） is amo...Ant colony optimization （ACO） is a new heuristic algo- rithm which has been proven a successful technique and applied to a number of combinatorial optimization problems. The traveling salesman problem （TSP） is among the most important combinato- rial problems. An ACO algorithm based on scout characteristic is proposed for solving the stagnation behavior and premature con- vergence problem of the basic ACO algorithm on TSP. The main idea is to partition artificial ants into two groups： scout ants and common ants. The common ants work according to the search manner of basic ant colony algorithm, but scout ants have some differences from common ants, they calculate each route＇s muta- tion probability of the current optimal solution using path evaluation model and search around the optimal solution according to the mutation probability. Simulation on TSP shows that the improved algorithm has high efficiency and robustness.展开更多
Traditionally, the optimization algorithm based on physics principles has some shortcomings such as low population diversity and susceptibility to local extrema. A new optimization algorithm based on kinetic-molecular...Traditionally, the optimization algorithm based on physics principles has some shortcomings such as low population diversity and susceptibility to local extrema. A new optimization algorithm based on kinetic-molecular theory(KMTOA) is proposed. In the KMTOA three operators are designed: attraction, repulsion and wave. The attraction operator simulates the molecular attraction, with the molecules moving towards the optimal ones, which makes possible the optimization. The repulsion operator simulates the molecular repulsion, with the molecules diverging from the optimal ones. The wave operator simulates the thermal molecules moving irregularly, which enlarges the searching spaces and increases the population diversity and global searching ability. Experimental results indicate that KMTOA prevails over other algorithms in the robustness, solution quality, population diversity and convergence speed.展开更多
针对形状不规则复杂面目标多弹瞄准点优化算法计算效率低、稳定性差、优化能力不足的问题,提出一种基于弹药圆概率偏差(Circular Error Probable,CEP)的毁伤概率矩阵库(Damage Probability Matrix Library,DPML)和改进启发式退火优化机...针对形状不规则复杂面目标多弹瞄准点优化算法计算效率低、稳定性差、优化能力不足的问题,提出一种基于弹药圆概率偏差(Circular Error Probable,CEP)的毁伤概率矩阵库(Damage Probability Matrix Library,DPML)和改进启发式退火优化机制的高效瞄准点优化算法(Efficient Aiming Point Optimization Algorithm,EAPOA)。构建多弹瞄准点优化模型时,除考虑目标形状、导弹毁伤能力外,还考虑导弹直接毁伤、间接毁伤和多弹种联合毁伤等复杂因素对目标毁伤效果的影响。提出一种基于DPML的毁伤概率快速估计算法,提升算法优化效率和鲁棒性;设计一种基于候选瞄准点序列化的优化算法框架,并提出基于全局搜索和改进退火机制的启发式优化算法,降低瞄准点组合求解空间大小并提升算法优化能力。通过6个复杂面目标测试用例验证算法性能。研究结果表明,所提的EAPOA相比于增强精英保留策略遗传算法具有更强的优化能力,且平均优化时间仅为其1/5~1/3,在优化收益和计算效率上具有明显优势。展开更多
最优线程数设置是影响多线程程序性能和功耗的关键之一。然而,目前寻找最优线程数的算法通常是从单一固定起点开始搜索,往往会造成搜索精度低、搜索开销大的问题。最优线程数的分布和位置与多种因素有关,包括程序所属类型、优化目标(性...最优线程数设置是影响多线程程序性能和功耗的关键之一。然而,目前寻找最优线程数的算法通常是从单一固定起点开始搜索,往往会造成搜索精度低、搜索开销大的问题。最优线程数的分布和位置与多种因素有关,包括程序所属类型、优化目标(性能、功耗和EDP(Energy-delay Product))、并行的多线程区域、软硬件配置参数等。围绕能效优先的最优线程数搜索问题,提出了能效优先的特定起点分类最优线程数搜索算法(Energy-Efficiency-First Optimal Thread Number Search Algorithm based on Specific Starting Point Classification,简称TS^(3)方法)”,通过设计基于程序分类的特殊起点设定方法来确定搜索起点,并采用启发式算法和二分查找方法搜索最优线程数,提升搜索效率,有效提升了能效优先目标(性能最优、功耗最优、能效EDP最优)下的最优线程数搜索精度并降低了搜索开销。在两个x86和一个ARM平台上用8个benchmark对算法有效性进行了详细实验验证,结果表明,与Baseline相比,TS^(3)方法的性能平均提升0.29%(平台A)、0.17%(平台B)、10.77%(平台C);功耗平均降低2.35%(平台A)、1.87%(平台B)、15.97%(平台C);EDP平均降低6.36%(平台A)、5.07%(平台B)、46.94%(平台C)。在3个平台上,与目前经典搜索方法相比,TS^(3)方法的性能平均提升10.16%,功耗平均降低13.45%,EDP平均降低23.77%;搜索开销平均降低86.8%。展开更多
为帮助企业更好地适应现实业务中的动态环境,研究了运输价格不确定性的多时段多式联运路径与存储协同优化模型。首先,在运输价格确定的环境下建立整数规划数学模型。其次,在运输价格不确定的环境下建立鲁棒优化模型,并将鲁棒优化模型转...为帮助企业更好地适应现实业务中的动态环境,研究了运输价格不确定性的多时段多式联运路径与存储协同优化模型。首先,在运输价格确定的环境下建立整数规划数学模型。其次,在运输价格不确定的环境下建立鲁棒优化模型,并将鲁棒优化模型转化为等价的线性鲁棒对等问题。随后,在传统k-短路算法、迭代贪婪(iterative greedy,IG)算法和自适应大邻域搜索算法(adaptive large neighbourhood search,ALNS)的基础上,提出了混合启发式算法MKIGALNS求解。最后,通过不同规模的算例实验,验证了所提出模型的正确性以及算法的有效性。实验结果表示,在10组算例中,不允许存储时的平均总运营成本为439191元,允许存储时的平均总运营成本为391378元,因此应当进行存储决策,有利于运营成本的降低。鲁棒实验结果表明,随着不确定预算取值的变化,总运营成本以及多时段多式联运运营策略发生变化,揭示了其内在联系。展开更多
Consideration of the travel time variation for rescue vehicles is significant in the field of emergency management research.Because of uncertain factors,such as the weather or OD(origin-destination)variations caused b...Consideration of the travel time variation for rescue vehicles is significant in the field of emergency management research.Because of uncertain factors,such as the weather or OD(origin-destination)variations caused by traffic accidents,travel time is a random variable.In emergency situations,it is particularly necessary to determine the optimal reliable route of rescue vehicles from the perspective of uncertainty.This paper first proposes an optimal reliable path finding(ORPF)model for rescue vehicles,which considers the uncertainties of travel time,and link correlations.On this basis,it investigates how to optimize rescue vehicle allocation to minimize rescue time,taking into account travel time reliability under uncertain conditions.Because of the non-additive property of the objective function,this paper adopts a heuristic algorithm based on the K-shortest path algorithm,and inequality techniques to tackle the proposed modified integer programming model.Finally,the numerical experiments are presented to verify the accuracy and effectiveness of the proposed model and algorithm.The results show that ignoring travel time reliability may lead to an over-or under-estimation of the effective travel time of rescue vehicles on a particular path,and thereby an incorrect allocation scheme.展开更多
基金supported by the National Natural Science Foundation of China(60573159)
文摘Ant colony optimization （ACO） is a new heuristic algo- rithm which has been proven a successful technique and applied to a number of combinatorial optimization problems. The traveling salesman problem （TSP） is among the most important combinato- rial problems. An ACO algorithm based on scout characteristic is proposed for solving the stagnation behavior and premature con- vergence problem of the basic ACO algorithm on TSP. The main idea is to partition artificial ants into two groups： scout ants and common ants. The common ants work according to the search manner of basic ant colony algorithm, but scout ants have some differences from common ants, they calculate each route＇s muta- tion probability of the current optimal solution using path evaluation model and search around the optimal solution according to the mutation probability. Simulation on TSP shows that the improved algorithm has high efficiency and robustness.
基金Project(61174140)supported by the National Natural Science Foundation of ChinaProject(13JJA002)supported by Hunan Provincial Natural Science Foundation,ChinaProject(20110161110035)supported by the Doctoral Fund of Ministry of Education of China
文摘Traditionally, the optimization algorithm based on physics principles has some shortcomings such as low population diversity and susceptibility to local extrema. A new optimization algorithm based on kinetic-molecular theory(KMTOA) is proposed. In the KMTOA three operators are designed: attraction, repulsion and wave. The attraction operator simulates the molecular attraction, with the molecules moving towards the optimal ones, which makes possible the optimization. The repulsion operator simulates the molecular repulsion, with the molecules diverging from the optimal ones. The wave operator simulates the thermal molecules moving irregularly, which enlarges the searching spaces and increases the population diversity and global searching ability. Experimental results indicate that KMTOA prevails over other algorithms in the robustness, solution quality, population diversity and convergence speed.
文摘针对形状不规则复杂面目标多弹瞄准点优化算法计算效率低、稳定性差、优化能力不足的问题,提出一种基于弹药圆概率偏差(Circular Error Probable,CEP)的毁伤概率矩阵库(Damage Probability Matrix Library,DPML)和改进启发式退火优化机制的高效瞄准点优化算法(Efficient Aiming Point Optimization Algorithm,EAPOA)。构建多弹瞄准点优化模型时,除考虑目标形状、导弹毁伤能力外,还考虑导弹直接毁伤、间接毁伤和多弹种联合毁伤等复杂因素对目标毁伤效果的影响。提出一种基于DPML的毁伤概率快速估计算法,提升算法优化效率和鲁棒性;设计一种基于候选瞄准点序列化的优化算法框架,并提出基于全局搜索和改进退火机制的启发式优化算法,降低瞄准点组合求解空间大小并提升算法优化能力。通过6个复杂面目标测试用例验证算法性能。研究结果表明,所提的EAPOA相比于增强精英保留策略遗传算法具有更强的优化能力,且平均优化时间仅为其1/5~1/3,在优化收益和计算效率上具有明显优势。
文摘最优线程数设置是影响多线程程序性能和功耗的关键之一。然而,目前寻找最优线程数的算法通常是从单一固定起点开始搜索,往往会造成搜索精度低、搜索开销大的问题。最优线程数的分布和位置与多种因素有关,包括程序所属类型、优化目标(性能、功耗和EDP(Energy-delay Product))、并行的多线程区域、软硬件配置参数等。围绕能效优先的最优线程数搜索问题,提出了能效优先的特定起点分类最优线程数搜索算法(Energy-Efficiency-First Optimal Thread Number Search Algorithm based on Specific Starting Point Classification,简称TS^(3)方法)”,通过设计基于程序分类的特殊起点设定方法来确定搜索起点,并采用启发式算法和二分查找方法搜索最优线程数,提升搜索效率,有效提升了能效优先目标(性能最优、功耗最优、能效EDP最优)下的最优线程数搜索精度并降低了搜索开销。在两个x86和一个ARM平台上用8个benchmark对算法有效性进行了详细实验验证,结果表明,与Baseline相比,TS^(3)方法的性能平均提升0.29%(平台A)、0.17%(平台B)、10.77%(平台C);功耗平均降低2.35%(平台A)、1.87%(平台B)、15.97%(平台C);EDP平均降低6.36%(平台A)、5.07%(平台B)、46.94%(平台C)。在3个平台上,与目前经典搜索方法相比,TS^(3)方法的性能平均提升10.16%,功耗平均降低13.45%,EDP平均降低23.77%;搜索开销平均降低86.8%。
文摘为帮助企业更好地适应现实业务中的动态环境,研究了运输价格不确定性的多时段多式联运路径与存储协同优化模型。首先,在运输价格确定的环境下建立整数规划数学模型。其次,在运输价格不确定的环境下建立鲁棒优化模型,并将鲁棒优化模型转化为等价的线性鲁棒对等问题。随后,在传统k-短路算法、迭代贪婪(iterative greedy,IG)算法和自适应大邻域搜索算法(adaptive large neighbourhood search,ALNS)的基础上,提出了混合启发式算法MKIGALNS求解。最后,通过不同规模的算例实验,验证了所提出模型的正确性以及算法的有效性。实验结果表示,在10组算例中,不允许存储时的平均总运营成本为439191元,允许存储时的平均总运营成本为391378元,因此应当进行存储决策,有利于运营成本的降低。鲁棒实验结果表明,随着不确定预算取值的变化,总运营成本以及多时段多式联运运营策略发生变化,揭示了其内在联系。
基金Projects(72071202,71671184)supported by the National Natural Science Foundation of ChinaProject(22YJCZH144)supported by Humanities and Social Sciences Youth Foundation,Ministry of Education of China+3 种基金Project(2022M712680)supported by Postdoctoral Research Foundation of ChinaProject(22KJB110027)supported by Natural Science Foundation of Colleges and Universities in Jiangsu Province,ChinaProject(D2019046)supported by Initiation Foundation of Xuzhou Medical University,ChinaProject(2021SJA1079)supported by General Project of Philosophy and Social Science Research in Jiangsu Universities,China。
文摘Consideration of the travel time variation for rescue vehicles is significant in the field of emergency management research.Because of uncertain factors,such as the weather or OD(origin-destination)variations caused by traffic accidents,travel time is a random variable.In emergency situations,it is particularly necessary to determine the optimal reliable route of rescue vehicles from the perspective of uncertainty.This paper first proposes an optimal reliable path finding(ORPF)model for rescue vehicles,which considers the uncertainties of travel time,and link correlations.On this basis,it investigates how to optimize rescue vehicle allocation to minimize rescue time,taking into account travel time reliability under uncertain conditions.Because of the non-additive property of the objective function,this paper adopts a heuristic algorithm based on the K-shortest path algorithm,and inequality techniques to tackle the proposed modified integer programming model.Finally,the numerical experiments are presented to verify the accuracy and effectiveness of the proposed model and algorithm.The results show that ignoring travel time reliability may lead to an over-or under-estimation of the effective travel time of rescue vehicles on a particular path,and thereby an incorrect allocation scheme.
