基于自适应ε的约束优化算法

针对目前约束优化算法易陷入局部最优和鲁棒性不好等缺点,提出基于自适应ε的约束优化算法。首先,通过改进的个体比较准则,充分利用优秀不可行个体的有效信息,加大对搜索空间的探索力度,从而提高种群多样性;其次,提出自适应ε调整策略,平衡目标函数和约束违反度之间的关系,进而更加合理地进行个体比较。对13个标准测试函数的对比实验表明,本文算法不仅能够以较高精度收敛到全局最优解,而且鲁棒性较好。     

求解约束优化的模拟退火PSO算法

针对有约束最优化问题,提出了基于模拟退火的粒子群优化(particle swarm optimization simulated annealing, PSO SA)算法。该算法利用模拟退火算法以一定概率接受较差点的概率突跳特性,克服粒子群优化算法易陷入局部最优的缺陷。采用可行性原则进行约束处理,并在模拟退火算法产生新粒子的过程中保留最优不可行解的信息,弥补了可行性原则处理最优点位于约束边界附近时存在的不足。4个典型工程优化设计的实验结果表明,该算法能够寻得更优的约束最优化解.     

Remarks on a benchmark nonlinear constrained optimization problem

1 .INTRODUCTIONThe benchmark nonlinear constrained problemwas originally put forward by Hi mmelblau[1]in1972 , and ever since then it has been regardedas a benchmark nonlinear constrained opti miza-tion problem for intelligence opti mization algo-rithms . However , just because of the citationerror made by Homaifar[2]in 1994 , two abso-lutely different results are obtained by independ-ent researchers . This problem is reevaluated inthe present study and the best-known solutionfor the“mis…     

求解约束优化问题的改进粒子群算法

针对高维复杂约束优化问题,提出了一种基于平滑技术和一维搜索的粒子群算法(NPSO)。该算法使粒子的飞行无记忆性,结合平滑函数和一维搜索重新生成停止进化粒子的位置,增强了在最优点附近的局部搜索能力;定义了不可行度阈值,利用此定义给出了新的粒子比较准则,该准则可以保留一部分性能较优的不可行解微粒,使微粒能快速的找到位于约束边界或附近的最优解;最后,为了扩大粒子的搜索范围,引进柯西变异算子。仿真结果表明,对于复杂约束优化问题,算法寻优性能优良,特别是对于超高维约束优化问题,该算法获得了更高精度的解。     

求解模糊机会约束规划模型的微粒群算法

针对现在不确定规划领域中存在的模糊机会约束规划模型和群智能优化方法-微粒群算法,提出了一种求解模糊机会约束规划模型的新混合智能算法.该算法利用微粒群算法并运用模糊模拟技术对模糊机会约束规划模型的规划问题进行分析和数值求解,因而无需像传统的基于遗传算法的混合智能算法那样需要很长时间并经过复杂的计算才能得到结果.通过对实际模型的分析和数值求解,给出了应用该方法的具体步骤,说明了该混合智能算法的合理性和有效性.     

求解约束优化问题的内外交叉遗传算法

针对很多约束优化问题的最优解位于可行域的边界上或其附近的特点,提出了一种新的遗传算法.算法将种群中的可行解和不可行解分别存贮在两个容器中,新设计的交叉算子(内外交叉法)尽量让可行域内的可行解与可行域外的不可行解交叉,并顺着有利的方向一维搜索到可行域边界,此举既增大了个体接近全局最优解的几率, 又增强了算法的收敛速度;粒子群变异法则吸取粒子群 (PSO)算法的优点,让粒子沿粒子自身历史最优和全局最优的方向变异, 而选择算子则采取了保留固定比例不可行解的方法. 仿真结果证明了算法能够在种群规模小,迭代次数少的情况下迅速接近或找到全局最优解.     

Multiobjective evolutionary algorithm for dynamic nonlinear constrained optimization problems

A new method to solve dynamic nonlinear constrained optimization problems (DNCOP) is proposed. First, the time (environment) variable period of DNCOP is divided into several equal subperiods. In each subperiod, the DNCOP is approximated by a static nonlinear constrained optimization problem (SNCOP). Second, for each SNCOP, inspired by the idea of multiobjective optimization, it is transformed into a static bi-objective optimization problem. As a result, the original DNCOP is approximately transformed into several static bi-objective optimization problems. Third, a new multiobjective evolutionary algorithm is proposed based on a new selection operator and an improved nonuniformity mutation operator. The simulation results indicate that the proposed algorithm is effective for DNCOP.     

约束多目标优化问题的进化算法及其收敛性

提出了带约束多目标优化问题的一种新解法。首先定义了个体的序值和个体的约束度,利用这两个定义给出了一种新的适应度函数和开关选择算子,从而对种群中的个体进行评估或排序时无需特别关心个体是否可行,避免了罚函数选择参数的困难。用概率论有关理论证明了算法的收敛性。用标准的Benchmark函数进行了仿真实验,仿真结果表明,新算法对约束多目标优化问题的求解是有效的。     

基于模式搜索的类电磁算法求解约束优化问题

针对约束优化问题,提出了一种基于模式搜索的类电磁算法。引入了粒子的违反度函数,将约束优化问题转化为双目标无约束优化模型来求解;提出了双目标模型中粒子的电荷和受力的计算公式,引导不可行粒子转化为满足约束条件的粒子;为了提高算法的搜索能力,结合模式搜索算法改进种群中的粒子,为类电磁算法提供了有效的局部信息。与以往算法仿真结果相比,新算法具有性能好、较稳定的优点。     

约束优化的空间变迹算法的旁瓣抑制应用

合成孔径雷达(synthetic aperture radar, SAR)回波信号具有很高的动态范围, 会导致强目标的旁瓣覆盖临近较弱目标的主瓣, 造成漏检。传统的加窗方法在消除旁瓣的同时会导致分辨率的下降和目标主瓣能量的降低, 空间变迹(spatially variant apodization, SVA)算法的提出有效解决了这个矛盾。然而, 现有的各种SVA改进算法在有效抑制旁瓣的同时却导致了主瓣能量的降低。因此, 本文提出了一种基于约束优化的改进的SVA算法。通过严格约束滤波器的单调性和有效点的选取, 有效解决了主瓣能量降低的问题, 可以在有效抑制旁瓣的同时保留图像的细节信息, 有利于对图像目标进一步的检测与识别。     

A line search filter secant method for nonlinear equality constrained optimization

<正> This paper formulates and analyzes a line search method for general nonlinear equalityconstrained optimization based on filter methods for step acceptance and secant methods for searchdirection.The feature of the new algorithm is that the secant algorithm is used to produce a searchdirection,a backtracking line search procedure is used to generate step size,some filtered rules areused to determine step acceptance,second order correction technique is used to reduce infeasibility andovercome the Maratos effect.Global convergence properties of this method are analyzed:under mildassumptions it is showed that every limit point of the sequence of iterates generated by the algorithmis feasible,and that there exists at least one limit point that is a stationary point for the problem.Moreover,it is also established that the Maratos effect can be overcome in our new approach by addingsecond order correction steps so that fast local superlinear convergence to a second order sufficient localsolution is achieved.Finally,the results of numerical experiments are reported to show the effectivenessof the line search filter secant method.     

A filter secant method with nonmonotone line search for equality constrained optimization

<正> This paper proposes a filter secant method with nonmonotone line search for non-linearequality constrained optimization.The Hessian of the Lagrangian is approximated using the BFGSsecant update.This new method has more flexibility for the acceptance of the trial step and requires lesscomputational costs compared with the monotone one.The global and local convergence of the proposedmethod are given under some reasonable conditions.Further,two-step Q-superlinear convergence rateis established by introducing second order correction step.The numerical experiments are reported toshow the effectiveness of the proposed algorithm.     

基于改进蚁群优化的多目标资源受限项目调度方法

多目标资源受限项目调度是一类典型的NP难组合优化问题,具有广泛的实际应用背景.本文提出了一种带局部搜索的改进蚁群优化算法用于求解多目标资源受限项目调度问题,优化指标为最小化项目工期和资源投资.首先,采用改进的蚁群优化算法获取Pareto解集;其次,通过基于带逻辑约束的Insert和Swap邻域搜索方法对已获得的非支配解进行局部搜索,进一步提高算法的性能;最后,基于PSPLIB国际标准测试集的数值仿真实验与现有最好的算法比较,验证了所提算法的有效性和高效性.     

A line search filter inexact SQP method for nonlinear equality constrained optimization

This paper proposes an inexact SQP method in association with line search filter technique for solving nonlinear equality constrained optimization.For large-scale applications,it is expensive to get an exact search direction,and hence the authors use an inexact method that finds an approximate solution satisfying some appropriate conditions.The global convergence of the proposed algorithm is established by using line search filter technique.The second-order correction step is used to overcome the Maratos effect,while the line search filter inexact SQP method has q-superlinear local convergence rate.Finally,the results of numerical experiments indicate that the proposed method is efficient for the given test problems.     

资源约束的拼件维修模型与粒子群求解算法

为了获得最优的装备作战单元的拼件维修方案与任务分配方案, 建立了一种同时优化拼件维修方案与任务分配方案的非线性规划模型, 模型中同时考虑了不同任务对武器系统的具体需求、武器系统的客观情况、维修资源约束, 可以最大化地协调任务、装备群与维修资源之间的矛盾, 所以更加贴合实际. 设计了基于粒子群算法的求解算法, 包括算法框架、粒子的表示、初始化、适应度函数、更新方法等. 最后, 应用该粒子群算法对具体实例进行了求解, 分析表明模型与算法可以有效地优化任务分配方案与拼件维修方案, 提高装备作战单元任务成功概率, 为决策者制定决策提供指导.     

基于PSO-ABC的混合算法求解复杂约束优化问题

为了改善粒子群优化（particle swarm optimization, PSO)算法在处理复杂约束优化问题时的求解效果,提出了一种基于粒子群和人工蜂群的混合优化(particle swarm optimization artificial bee colony,PSO-ABC)算法。在采用可行性规则进行约束处理的基础上,将PSO种群分为可行子群和不可行子群,并在ABC算法从粒子种群中选择蜜源时,保留部分较优的可行解信息和约束违反程度较低的不可行解信息,弥补了联赛选择算子在处理最优点位于约束边界附近的问题时存在的不足。同时,使用禁忌表存储局部极值,减小了PSO算法陷入局部最优的危险。针对4个标准测试实例的实验结果表明,该算法能够寻得更优的约束最优化解,且稳健性更强。     

Necessary conditions for efficient solution of vector optimization problems

In this paper,by using Ljusternik’s theorem and the open mapping theorem of convex process,the author gives necessary conditions for the efficient solution to the vector optimization problems without requiring that the ordering cone in the objective space has a nonempty interior.     

组合工况下供水管网的智能优化设计

基于遗传算法,首次同时考虑最高时、事故时、消防时多个供水工况,建立供水管网管径组合优化设计模型,采用C++平台实现了优化算法,通过实例应用证明,与传统的单工况优化及先单工况优化,再进行多工况校核的方法相比,计算效率有明显提高,特别是针对大规模复杂供水管网的改扩建问题,经济效益明显.     

基于互补问题的混合交通信号配时优化模型

根据最优化原理与非线性互补理论,针对混合交通下单点交叉口信号优化配时问题,以延误时间、停车次数和通行能力作为性能指标,研究了城市交通控制的优化模型,提出了与之等价的基于绿灯时间的非线性互补问题,并给出了信号优化配时的牛顿算法.算例表明此方法可行有效,在道路等级相差悬殊及交通量相当的情况下,较蚂蚁算法,总延误有很大的减少;在各进口道饱和度较小的情况下,较仿真结果,性能指标有明显提高.为混合交通下单点交叉口信号优化配时研究提出一种新思路和新方法.     

A family of the local convergence of the improved secant methods for nonlinear equality constrained optimization subject to bounds on variables

This paper studies a family of the local convergence of the improved secant methods for solving the nonlinear equality constrained optimization subject to bounds on variables. The Hessian of the Lagrangian is approximated using the DFP or the BFGS secant updates. The improved secant methods are used to generate a search direction. Combining with a suitable step size, each iterate switches to trial step of strict interior feasibility. When the Hessian is only positive definite in an affine null subspace, one shows that the algorithms generate the sequences converging q-linearly and two-step q-superlinearly. Furthermore, under some suitable assumptions, some sequences generated by the algorithms converge locally one-step q-superlinearly. Finally, some numerical results are presented to illustrate the effectiveness of the proposed algorithms.