共查询到18条相似文献,搜索用时 203 毫秒
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针对多目标蚁群遗传算法(MOAGA)解集边界分布不均的问题,提出改进算法,解决连续空间中带约束条件多目标优化问题.改进算法在基本MOAGA算法的基础上,在选择中引入一定比例的边界决策、单目标最优决策,并提高边界决策的交叉率.实验证明,改进算法解决了基本算法解集分布边界疏中间密的问题,并且能更快的获得散布性较好的Pareto最优解集. 相似文献
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为了提高回归测试的效率,提出了一种基于多目标人工蜂群优化(Multi-Objective Artificial Bee Colony Optimization, MOABCO)算法的多目标测试用例优先级排序(Multi-Objective Test Case Prioritization, MOTCP)方法.针对标准多目标人工蜂群(Multi-Objective Artificial Bee Colony, MOABC)算法容易陷入局部最优解的问题,将差分变异策略融入到新蜜源更新阶段,且基于信息熵改进新蜜源选择方法,以避免算法陷入局部最优并增强了全局搜索能力;然后,将代码覆盖率和测试用例有效执行时间作为优化目标,并用MOABCO算法求Pareto最优解集,以解决MOTCP问题.实验结果表明, MOABCO算法求得的Pareto最优解集在逼近性和分布均匀性上均优于MOABC算法;在解决MOTCP问题上,相对于NSGA-II算法具有更高的收敛速度和更高的缺陷检测率. 相似文献
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针对工艺路线规划中满足多重约束的最优方案选择问题,提出一种细菌觅食和蚁群优化(bacteria foraging ant colony optimization,BFACO)算法。首先,将工艺路线规划转化为对加工元顺序的优化问题,构造满足多种工艺准则的加工元拓扑优先顺序图,并构建了在缩短加工周期、提高加工质量和降低加工成本目标下的最低加工资源更换成本的目标函数;其次,设计加工元序列与加工资源两个搜索阶段的蚁群搜索,拓扑优先顺序图可弥补加工元序列搜索阶段信息素匮乏的缺点,而在加工资源搜索阶段引入细菌觅食优化算法的复制与趋向操作,可使加工元在多个可选加工资源的情况下获得加工资源更换成本最低的加工序列;最后,基于细菌觅食与蚁群算法的融合优化,完成多个加工元序列的信息素积累并输出最优解,解决蚁群算法局部收敛且计算速度慢的问题。将BFACO算法应用于实例并与其他优化算法的优化结果进行对比,结果显示BFACO算法在工艺路线优化方面较其他优化算法具有较高的计算效率,验证了BFACO算法的可行性与有效性。研究表明,BFACO算法可有效应用于同时考虑工艺约束与加工资源更换成本的工艺规划,为实际生产提供高效且灵活的工艺路线的优化选择。 相似文献
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交叉变异的连续蚁群优化算法 总被引:3,自引:2,他引:1
研究了应用于连续空间优化问题的蚁群算法,给出了信息素的留存方式以及搜索策略.另外,针对蚁群算法易陷入局部最优的缺点,在最优蚂蚁周围进行了精细搜索,并加入了自适应的交叉变异算子,从而改进了蚁群算法的全局优化性能.数值仿真结果表明,该算法是一种有效的优化算法. 相似文献
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动力吸振器的多目标优化和多属性决策研究 总被引:2,自引:0,他引:2
在结构振动控制中,为了最大限度发挥吸振器的耗能减振作用.需要寻找吸振器的最优参数,即最优频率比、最优阻尼比和最优质量比,使得结构在不同的频率激励下获得最好的减振效果.本文将基于进化算法的多目标优化技术与多属性决策方法联合运用,针对主系统存在阻尼的减振系统,研究了动力吸振器的优化和决策同题.对于多目标优化问题,采用改进的非支配解排序的多目标进化算法(NSGA Ⅱ),求出Pareto最优解,由这些Pareto最优解构成决策矩阵,使用客观赋权的信息熵方法对最优解的属性进行权值计算.然后用逼近理想解的排序方法(TOPSIS)进行多属性决策(MADM)研究,对Pareto最优解给出排序.文中给出了4个设计参数、3个目标函数的动力吸振器优化设计算例. 相似文献
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通过对拉式膜片弹簧载荷-变形特性的综合分析,考虑各种约束条件,提出了一种新的多目标优化设计数学模型.该模型以在摩擦片磨损极限范围内,弹簧压紧力变化的平均值最小及驾驶员作用在分离轴承装置上的分离操纵力的平均值最小为共同优化目标,使离合器后备系数稳定,离合器分离力的平均作用力较小.蚁群算法是一种新型的元启发式优化算法,该算法具有较强的发现较好解的能力,但同时也存在一些缺点,如容易出现停滞现象、收敛速度慢等.将遗传算法和蚁群算法结合起来,在蚁群算法的每一次迭代中,首先根据信息量选择解分量的初值,然后使用变异操作来确定解的值.最后,通过实例与其他优化方法的结果进行比较.结果表明,该算法有较好的收敛速度及稳定性. 相似文献
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João Batista Queiroz Zuliani Frederico Gadelha Guimarães Carlos Alberto Severiano Junior 《工程优选》2019,51(6):915-940
An automated multi-material approach that integrates multi-objective Topology Optimization (TO) and multi-objective shape optimization is presented. A new ant colony optimization algorithm is presented and applied to solving the TO problem, estimating a trade-off set of initial topologies or distributions of material. The solutions found usually present irregular boundaries, which are not desirable in applications. Thus, shape parameterization of the internal boundaries of the design region, and subsequent shape optimization, is performed to improve the quality of the estimated Pareto-optimal solutions. The selection of solutions for shape optimization is done by using the PROMETHEE II decision-making method. The parameterization process involves identifying the boundaries of different materials and describing these boundaries by non-uniform rational B-spline curves. The proposed approach is applied to the optimization of a C-core magnetic actuator, with two objectives: the maximization of the attractive force on the armature and the minimization of the volume of permanent magnet material. 相似文献
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Displacement back analysis for underground engineering based on immunized continuous ant colony optimization 总被引:1,自引:0,他引:1
The objective function of displacement back analysis for rock parameters in underground engineering is a very complicated nonlinear multiple hump function. The global optimization method can solve this problem very well. However, many numerical simulations must be performed during the optimization process, which is very time consuming. Therefore, it is important to improve the computational efficiency of optimization back analysis. To improve optimization back analysis, a new global optimization, immunized continuous ant colony optimization, is proposed. This is an improved continuous ant colony optimization using the basic principles of an artificial immune system and evolutionary algorithm. Based on this new global optimization, a new displacement optimization back analysis for rock parameters is proposed. The computational performance of the new back analysis is verified through a numerical example and a real engineering example. The results show that this new method can be used to obtain suitable parameters of rock mass with higher accuracy and less effort than previous methods. Moreover, the new back analysis is very robust. 相似文献
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提出了一种基于自适应蚁群优化(AACO)的Volterra核辨识方法。该方法将蚁群算法应用于Volterra时域核的辨识,并能够随着进化次数的增加,自适应调整基本蚁群算法的参数。同时,与相应的基于蚁群优化(ACO)的Volterra核辨识方法进行了对比分析。仿真结果表明,本文提出的方法与蚁群优化辨识方法不论在无噪声环境下,还是在有噪声干扰下,都能得到很好的辨识精度、收敛稳定性和较强的鲁棒抗噪性能,然而,在收敛速度方面,本文提出的方法优于蚁群优化辨识方法。 相似文献
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This article presents an automated technique for preliminary layout (conceptual design) optimization of rectilinear, orthogonal building frames in which the shape of the building plan, the number of bays and the size of unsupported spans are variables. It adopts the knapsack problem as the applied combinatorial optimization problem, and describes how the conceptual design optimization problem can be generally modelled as the unbounded multi-constraint multiple knapsack problem. It discusses some special cases, which can be modelled more efficiently as the single knapsack problem, the multiple-choice knapsack problem or the multiple knapsack problem. A knapsack contains sub-rectangles that define the floor plan and the location of columns. Particular conditions or preferences for the conceptual design can be incorporated as constraints on the knapsacks and/or sub-rectangles. A bi-objective knapsack problem is defined with the aim of obtaining a conceptual design having minimum cost and maximum plan regularity (minimum structural eccentricity). A multi-objective ant colony algorithm is formulated to solve the combinatorial optimization problem. A numerical example is included to demonstrate the application of the present method and the robustness of the algorithm. 相似文献
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This study explores the use of teaching-learning-based optimization (TLBO) and artificial bee colony (ABC) algorithms for determining the optimum operating conditions of combined Brayton and inverse Brayton cycles. Maximization of thermal efficiency and specific work of the system are considered as the objective functions and are treated simultaneously for multi-objective optimization. Upper cycle pressure ratio and bottom cycle expansion pressure of the system are considered as design variables for the multi-objective optimization. An application example is presented to demonstrate the effectiveness and accuracy of the proposed algorithms. The results of optimization using the proposed algorithms are validated by comparing with those obtained by using the genetic algorithm (GA) and particle swarm optimization (PSO) on the same example. Improvement in the results is obtained by the proposed algorithms. The results of effect of variation of the algorithm parameters on the convergence and fitness values of the objective functions are reported. 相似文献
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Kiran K. Annamdas 《工程优选》2013,45(8):737-752
This study proposes particle swarm optimization (PSO) based algorithms to solve multi-objective engineering optimization problems involving continuous, discrete and/or mixed design variables. The original PSO algorithm is modified to include dynamic maximum velocity function and bounce method to enhance the computational efficiency and solution accuracy. The algorithm uses a closest discrete approach (CDA) to solve optimization problems with discrete design variables. A modified game theory (MGT) approach, coupled with the modified PSO, is used to solve multi-objective optimization problems. A dynamic penalty function is used to handle constraints in the optimization problem. The methodologies proposed are illustrated by several engineering applications and the results obtained are compared with those reported in the literature. 相似文献
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目的为解决图像边缘提取方法中由于噪声浸染导致边缘定位精确度降低、边缘信息丢失和虚假边缘等不足,提出基于霍夫变换(HT)耦合蚁群优化(ACO)图像边缘的提取方法。方法对输入图像进行霍夫变换,消除噪声和线段间隔对图像边缘的影响;计算图像像素梯度和像素圆形邻域统计均值的差值,构建二者之间的权重函数,并作为蚁群的信息素和启发信息;利用蚁群优化算法,引导蚁群搜索图像边缘,完成图像边缘提取。结果实验表明,与当前边缘提取技术相比,文中算法具有更高的提取精度与效率,可获取完整、细节丰富的边缘,有效地降低了噪声影响。结论所提算法具有较强的抗噪性能,能进一步改善边缘提取精度,能够较好地用于包装条码识别与图像处理领域。 相似文献