求多峰函数全部全局最优解的改进遗传算法

本文研究了多峰优化问题,利用梯度算子和筛选策略,得到了一种可求解多峰函数全部最优解的改进型遗传算法.数值模拟结果表明,该算法在处理复杂多峰函数优化问题时,局部搜索能力和克服过早收敛能力方面相对于传统遗传算法均有很大提高.     

一种改进适应度函数的遗传算法

针对遗传算法搜索导优中适应度函数的设计不当,将难以体现个体差异和选择操作的作用,从而造成早熟收敛的问题,构建了两种基于顺序的适应度函数的模型.适应度函数的设计使得在进化过程中控制选择压力,种群竞争力得到增强,早熟现象得到改善.并将改进的算法应用在复杂函数优化问题上,MATLAB优化结果表明,算法在种群多样性、搜索速度、计算精度上均有改善,推动遗传算法在工程领域的应用.     

基于遗传禁忌算法的双资源约束下并行生产线调度研究

并行生产线调度问题兼有并行机器和流程车间调度问题的特点,是一类新型的调度问题。在考虑遗传算法早熟收敛特性和禁忌搜索法自适应优点的基础上,将遗传算法和禁忌搜索法结合起来,提出了基于遗传算法和禁忌搜索算法的双资源并行作业车间的调度优化问题算法,即考虑到了产品的调度受到机器、工人等资源制约的影响,对算法中种群的构造,适应度计算,遗传操作等方面进行了研究,最后给出了实例仿真和结论。     

作业车间调度问题的改进混合遗传算法

作业车间调度是一类求解困难的组合优化问题,本文在考虑遗传算法早熟收敛问题和禁忌搜索法自适应优点的基础上,将遗传算法和禁忌搜索法相结合,提出了一种基于遗传和禁忌搜索的混合算法,并用实例对该算法进行了仿真研究.结果表明,该算法有很好的收敛精度,是可行的,与传统的算法相比较,有明显的优越性.     

用自适应伪并行遗传算法求解双准则三维运输问题

采用并行计算的思想,在单台计算机上应用自适应的伪并行遗传算法,求解了双准则三维运输问题,最后通过实验验证该方法可产生适合需求的解,同时体现了该算法有较快的收敛速度.     

施工网络计划优化的极值种群遗传算法

针对普通遗传算法用于施工网络计划优化的缺点,通过种群划分与极值搜索,建立了网络计划优化的极值种群改进遗传算法模型,有效地避免了陷入局部极值点,应用证明,该算法与普通遗传算法相比,具有优化速度快、求解精度高,全局寻优能力强等优点,尤其适合于大型复杂工程网络的优化计算。     

基于自适应子种群和动态反向学习的改进鸡群算法

针对鸡群算法(Chicken swarm optimization,CSO)求解复杂高维问题收敛精度低、容易陷入局部极值等问题,提出了一种基于自适应子种群和动态反向学习的改进鸡群(ICSO)算法.根据鸡群算法迭代进化进程,自适应确定公鸡种群规模大小,并据此将母鸡种群和小鸡分成若干个子种群;设计进化停滞判定机制,并引入动态反向学习因子以改进算法个体更新方式,有效保持鸡群样本多样性和算法全局深度搜索能力.典型测试函数仿真实验结果表明,与SFLA算法、PSO等智能优化算法相比,ICSO算法具有更高的收敛精度和更优的复杂函数优化能力.     

改进的粒子群算法在桁架结构优化设计中的应用

应用改进的粒子群算法进行桁架结构优化设计。首先,在确定初始种群时用随机方向法产生一组适应环境值较高的初始种群,使算法快速收敛于全局最优解,降低了算法的时间复杂度;其次,将模糊推理应用于算法的参数动态调整中,提高种群的适应搜索空间环境的能力;最后,将改进的粒子群算法应用于桁架结构优化设计中.算例表明,改进后算法的搜索性能得到了一定改善,为其应用于大型复杂的工程结构优化设计提供了借鉴.     

一种基于局部搜索算子的遗传算法

为了提高遗传算法的收敛速度及局部搜索能力,设计了一种基于优良模式的局部搜索算子.同时对传统免疫算法中基于浓度的选择算子进行了改进,设计了一种基于适应度值和浓度的混合选择算子,从而有效的阻止了算法出现"早熟"现象.进一步给出了算法的步骤,并利用有限马尔可夫链证明了该算法的收敛性,最后通过对四个经典测试算法性能的函数的数字仿真,说明该算法对多峰值函数优化问题明显优于基本遗传算法.     

基于近邻刺激的改进粒子群优化算法

针对粒子群算法在优化复杂问题时收敛速度慢和易早熟的缺点,提出了基于近邻刺激的改进粒子群优化算法(NSPSO),通过增加一维刺激机制,加强了种群中粒子相互间的信息交流,改善了粒子的学习能力和算法的搜索能力.同时,粒子在速度更新时自适应采用两种更新机制,防止了陷入局部最优,也增强了粒子的搜索效率.在8个测试函数上的实验表明,与一些改进的粒子群算法相比较,在6个函数上的收敛精度和收敛速度都优于其他改进算法,且t检验结果证明了NSPSO算法具有明显的优势,并通过混合动力汽车能源控制策略的优化问题,进一步验证了NSPSO的有效性.     

非线性互补问题非精确逐次逼近法的全局收敛性

本文针对非线性互补问题,提出了与其等价的非光滑方程的非精确逐次逼近算法,并在一定条件下证明了该算法的全局收敛性。     

基于相似度的样本分类算法

结合粗糙集理论中相似关系的思想,提出了一种基本相似度的样本分类算法.并通过实例说明了该分类算法的有效性。     

非平衡交通分配的拟Frank-Wolfe迭代算法

提出了基于最短路动态生成的一种新的非平衡交通分配迭代算法.在每轮迭代中,将按全有全无方法在当前最短路上分配的交通量与前一轮迭代所得到的交通量加权组合,而各O-D对的加权系数则依据Logit原则来确定.和Frank-Wolfe算法不同,不必通过一维搜索确定加权系数.同时又避免了Logit方法要求枚举所有路径的困难.本文还证明了算法的收敛性,而计算实例显示,由本算法所得结果与平衡交通分配非常接近,因而它是一个高效而可靠的交通分配算法,适用于大、中型道路交通网络的交通分配计算.     

Probability-one homotopy maps for mixed complementarity problems

Probability-one homotopy algorithms have strong convergence characteristics under mild assumptions. Such algorithms for mixed complementarity problems (MCPs) have potentially wide impact because MCPs are pervasive in science and engineering. A probability-one homotopy algorithm for MCPs was developed earlier by Billups and Watson based on the default homotopy mapping. This algorithm had guaranteed global convergence under some mild conditions, and was able to solve most of the MCPs from the MCPLIB test library. This paper extends that work by presenting some other homotopy mappings, enabling the solution of all the remaining problems from MCPLIB. The homotopy maps employed are the Newton homotopy and homotopy parameter embeddings.     

正态云模型果蝇优化算法及其应用

提出了一种基于正态云模型的果蝇优化算法(NCMFOA).该算法通过直接将果蝇位置赋值给气味浓度判定值和引入正态云模型来刻画果蝇嗅觉搜索行为的随机性与模糊性,从而解决了果蝇优化算法(FOA)不能搜索负值空间的缺陷,并有效克服了FOA算法在解决复杂优化问题时容易陷入局部极值的不足.通过正态云模型熵值的动态调整,使得NCMFOA算法在进化的前期阶段具有较强的随机性与模糊性,以提高算法的全局探索能力;随着迭代次数的增加,算法搜索行为的随机性与模糊性逐渐减弱,使得其局部开发能力逐渐增强,算法收敛精度得到提高.此外,通过引入视觉实时更新方案,进一步加速了算法的收敛速度.用经典的基准测试函数验证了NCMFOA算法的可行性与有效性,结果表明该算法具有收敛速度快、收敛精度高以及鲁棒性好等优点,对于高维复杂优化问题,该算法同样获得了良好的优化效果.将NCMFOA算法用于解决混沌系统的参数估计问题,进一步验证了该算法具有较强的解决实际工程优化问题的能力.     

Line-Sum Symmetry via the DomEig Algorithm

We propose a new algorithm, the DomEig algorithm, for obtaining the line-sum-symmetric similarity-scaling of a given irreducible, essentially nonnegative matrix A. It is based on results concerning the minimum dominant eigenvalue of an essentially nonnegative matrix under trace-preserving perturbations of its diagonal. In this note we relate the minimum dominant eigenvalue problem to the problem of determining the diagonal scaling matrix for line-sum-symmetry. We present the DomEig algorithm, prove its convergence, and discuss briefly the results of a comparison of this algorithm with another algorithm, the DSS algorithm, often used for line-sum symmetry. The experiments suggest that, for matrices of order greater than 50, the convergence rate, measured either in flop counts or CPU time, is significantly greater for DomEig than for DSS, with the improvement in rate increasing as the order increases. The algorithm may be useful in such applications as the scaling of large social accounting matrices.     

Stabilized sequential quadratic programming for optimization and a stabilized Newton-type method for variational problems

The stabilized version of the sequential quadratic programming algorithm (sSQP) had been developed in order to achieve fast convergence despite possible degeneracy of constraints of optimization problems, when the Lagrange multipliers associated to a solution are not unique. Superlinear convergence of sSQP had been previously established under the strong second-order sufficient condition for optimality (without any constraint qualification assumptions). We prove a stronger superlinear convergence result than the above, assuming the usual second-order sufficient condition only. In addition, our analysis is carried out in the more general setting of variational problems, for which we introduce a natural extension of sSQP techniques. In the process, we also obtain a new error bound for Karush–Kuhn–Tucker systems for variational problems that holds under an appropriate second-order condition.     

求解约束优化问题的一个对偶算法

１．引言 考虑下述形式的不等式约束优化问题：其中 ＝０,１,…,ｍ,是连续可微函数．求解（１．１）的数值方法有很多,传统方法有乘子法,序列一次规划方法,等等（见 Ｂｅｒｔｓｅｋａｓ（１９８２）, Ｈａｎ（１９７６, １９７７））．近年来对求解（１．１）的原始－对偶算法的研究已成为非线性规划领域的新的热点,如ＥＩ－Ｂａｋｒｙ,Ｔａｐｉａ,Ｔｓｕｃｈｉｙａ　＆　Ｚｈａｎｇ（１９９６）,Ｙａｍａｓｈｉｔａ（１９９２,１９９６,１９９７）等;尽管这些原始－对偶算法具有好的收敛性质和计算效果,但其算法结构相对…     

On the convergence of cross decomposition

Cross decomposition is a recent method for mixed integer programming problems, exploiting simultaneously both the primal and the dual structure of the problem, thus combining the advantages of Dantzig—Wolfe decomposition and Benders decomposition. Finite convergence of the algorithm equipped with some simple convergence tests has been proved. Stronger convergence tests have been proposed, but not shown to yield finite convergence.In this paper cross decomposition is generalized and applied to linear programming problems, mixed integer programming problems and nonlinear programming problems (with and without linear parts). Using the stronger convergence tests finite exact convergence is shown in the first cases. Unbounded cases are discussed and also included in the convergence tests. The behaviour of the algorithm when parts of the constraint matrix are zero is also discussed. The cross decomposition procedure is generalized (by using generalized Benders decomposition) in order to enable the solution of nonlinear programming problems.     

An SQP-type algorithm for nonlinear second-order cone programs

We propose an SQP-type algorithm for solving nonlinear second-order cone programming (NSOCP) problems. At every iteration, the algorithm solves a convex SOCP subproblem in which the constraints involve linear approximations of the constraint functions in the original problem and the objective function is a convex quadratic function. Those subproblems can be transformed into linear SOCP problems, for which efficient interior point solvers are available. We establish global convergence and local quadratic convergence of the algorithm under appropriate assumptions. We report numerical results to examine the effectiveness of the algorithm. This work was supported in part by the Scientific Research Grant-in-Aid from Japan Society for the Promotion of Science.