基于相场描述的拉压不对称强度结构拓扑优化

运用了基于相场描述的拓扑优化方法,来寻找在拉伸和压缩中表现出不对称强度行为的连续体结构的最优布局。依据Drucker-Prager屈服准则和幂率插值方案,优化问题可以描述为在局部应力约束下的最小化结构的体积。用qp放松法来解决应力约束的奇异性,并采用基于P-norm函数的聚合方法对应力约束进行凝聚,该方法实现了约束个数的降低,同时引入了稳定转化法来处理大量的局部应力约束和高度非线性的应力行为,以修正应力,提高优化收敛的稳定性。在优化问题求解时,使用拉格朗日乘子法对目标函数和应力约束进行处理。利用伴随变量法进行灵敏度分析,并通过求解Allen-Cahn方程更新相场函数设计变量。数值算例证明了该优化模型和相应数值技术的有效性,相关算例还揭示了考虑拉压不同强度和考虑同拉压强度约束时得到的结构优化拓扑构型具有显著的差异。     

多工况多约束下离散变量桁架结构的拓扑优化设计

提出了一个多工况下受应力、位移约束的离散变量桁架结构的拓扑优化方法，给出了结构拓扑形式变更时的约束处理方法及杆件删除策略，使基结构设计空间的维数不断降低，达到最优拓扑，避免了奇异解的出现，在算例中指出了截面离散集和位移约束对最优拓扑的影响；算例给出了满意的拓扑优化解。     

遗传算法求解约束非线性规划及Matlab实现

对于约束非线性规划问题,传统的方法:可行方向法、惩罚函数法计算烦琐且精度不高.用新兴的遗传算法来解决约束非线性规划,核心是惩罚函数的构造.以前的惩罚函数遗传算法有的精度较低,有的过于复杂.本文在两个定义的基础上构造了新的惩罚函数,并在新的惩罚函数的基础上,提出了一种解决约束非线性最优化问题的方法.通过两个例子应用Matlab说明了这个算法的可行性.     

工件的释放时间和加工时间具有一致性的单机在线排序问题研究

工件的释放时间和加工时间具有一致性, 是指释放时间大的工件其加工时间不小于释放时间小的工件的加工时间, 即若$r_{i}\geq r_{j}$, 则$p_{i}\geq p_{j}$。本文在该一致性约束下, 研究最小化最大加权完工时间单机在线排序问题, 和最小化总加权完工时间单机在线排序问题, 并分别设计出$\frac{\sqrt{5}+1}{2}$-竞争的最好可能在线算法。     

资源受限下森林火灾应急救援多目标调度优化

许多森林火灾由于救援资源受限而不能在第一时间扑灭,导致火灾扩大蔓延,进而造成更大的森林资源损失。因此,在救援资源受限情形下,如何对消防救援车辆进行合理的调度安排以快速和低成本地扑灭火灾已成为亟待解决的现实问题。本文研究了一类资源受限下森林火灾应急救援多目标调度优化问题,为该问题构建了多目标混合整数非线性规划模型,优化目标为同时最小化总灭火救援时间和救援车辆总行驶距离。为有效求解该问题,首先将上述非线性模型等价转化为线性模型。然后提出ε-约束法和模糊逻辑相结合的算法对问题进行求解。最后,以大兴安岭山发生的火灾案例和随机生成仿真算例对模型和算法有效性进行验证,结果表明所提出的模型和算法能够有效解决资源受限下森林火灾应急救援问题,并为决策者提供最优的消防调度方案。     

On the Nature of Functional Differentiation: The Role of Self-Organization with Constraints

The focus of this article is the self-organization of neural systems under constraints. In 2016, we proposed a theory for self-organization with constraints to clarify the neural mechanism of functional differentiation. As a typical application of the theory, we developed evolutionary reservoir computers that exhibit functional differentiation of neurons. Regarding the self-organized structure of neural systems, Warren McCulloch described the neural networks of the brain as being “heterarchical”, rather than hierarchical, in structure. Unlike the fixed boundary conditions in conventional self-organization theory, where stationary phenomena are the target for study, the neural networks of the brain change their functional structure via synaptic learning and neural differentiation to exhibit specific functions, thereby adapting to nonstationary environmental changes. Thus, the neural network structure is altered dynamically among possible network structures. We refer to such changes as a dynamic heterarchy. Through the dynamic changes of the network structure under constraints, such as physical, chemical, and informational factors, which act on the whole system, neural systems realize functional differentiation or functional parcellation. Based on the computation results of our model for functional differentiation, we propose hypotheses on the neuronal mechanism of functional differentiation. Finally, using the Kolmogorov–Arnold–Sprecher superposition theorem, which can be realized by a layered deep neural network, we propose a possible scenario of functional (including cell) differentiation.     

Parametric Duality Models for Semiinfinite Multiobjective Fractional Programming Problems Containing Generalized （α,η,ρ）-V-Invex Functions

In this paper, we present several parametric duality results under various generalized ( α,η,ρ )-V- invexity assumptions for a semiinfinite multiobjective fractional programming problem.     

Modified sub-population teaching-learning-based optimization for design of truss structures with natural frequency constraints

In this paper, a modified sub-population teaching-learning-based optimization (MS-TLBO) algorithm is proposed to improve the exploration and exploitation capacities by including the concept of number of teachers, adaptive teaching factor, learning through tutorial, and self-motivated learning in the basic TLBO algorithm. The multiple frequency responses to the structural optimization problems are challenging due to its search space, which is implicit, nonconvex, nonlinear, and often leading to divergence. The viability and efficiency of the proposed method are tested by five structural benchmark problems of shape and size optimization with multiple natural frequency constraints on the planar and space trusses. The results reveal that MS-TLBO is more effective as compared to the original TLBO and other state-of-the-art algorithms.     

Unified symmetry of mechano-electrical systems with nonholonomic constraints

The unified symmetry of mechano-electrical systems with nonholonomic constraints are studied in this paper, the definition and the criterion of unified symmetry of mechano-electrical systems with nonholonomic constraints are derived from the Lagrange-Maxwell equations. The Noether conserved quantity, Hojman conserved quantity and Mei conserved quantity are then deduced from the unified symmetry. An example is given to illustrate the application of the results.