Game Theory Optimization via Diverse Genetic Crossover Intelligence

Game theory is explored via a maze application where combinatorial optimization occurs with the objective of traversing through a defined maze with an aim to enhance decision support and locate the optimal travel sequence while minimizing computation time. This combinatorial optimization approach is initially demonstrated by utilizing a traditional genetic algorithm (GA), followed by the incorporation of artificial intelligence utilizing embedded rules based on domain-specific knowledge. The aim of this initiative is to compare the results of the traditional and rule-based optimization approaches with results acquired through an intelligent crossover methodology. The intelligent crossover approach encompasses a two-dimensional GA encoding where a second chromosome string is introduced within the GA, offering a sophisticated means for chromosome crossover amongst selected parents. Additionally, parent selection intelligence is incorporated where the best-traversed paths or population members are retained and utilized as potential parents to mate with parents selected within a traditional GA methodology. A further enhancement regarding the utilization of saved optimal population members as potential parents is mathematically explored within this literature.

Asymptotically Optimal Simulation Budget Allocation under Fixed Confidence Level by Ordinal Optimization

Ordinal optimization concentrates on isolating a subset of good designs with high probability and reduces the required simulation time dramatically for discrete event simulation. To obtain the same probability level,we may optimally allocate our computing budget among different designs,instead of equally simulating all different designs. In this paper we present an effective approach to optimally allocate computing budget for discrete-event system simulation. While ordinal optimization can dramatically reduce the computation cost, our approach can further reduce the already-low cost.

Optimization of HMM Parameters Based on Chaos and Genetic Algorithm for Hand Gesture Recognition

In order to prevent standard genetic algorithm (SGA) from being premature, chaos is introduced into GA, thus forming chaotic anneal genetic algorithm (CAGA). Chaos ergodicity is used to initialize the population, and chaotic anneal mutation operator is used as the substitute for the mutation operator in SGA. CAGA is a unified framework of the existing chaotic mutation methods. To validate the proposed algorithm, three algorithms, i. e. Baum-Welch, SGA and CAGA, are compared on training hidden Markov model (HMM) to recognize the hand gestures. Experiments on twenty-six alphabetical gestures show the CAGA validity.

GENETIC ALGORITHMS AND GAME THEORY FOR HIGH LIFT DESIGN PROBLEMS IN AERODYNAMICS

A multi-objective evolutionary optimization method (combining genetic algorithms(GAs)and game theory(GT))is presented for high lift multi-airfoil systems in aerospace engineering.Due to large dimension global op-timization problems and the increasing importance of low cost distributed parallel environments,it is a natural idea to replace a globar optimization by decentralized local sub-optimizations using GT which introduces the notion of games associated to an optimization problem.The GT/GAs combined optimization method is used for recon-struction and optimization problems by high lift multi-air-foil desing.Numerical results are favorably compared with single global GAs.The method shows teh promising robustness and efficient parallel properties of coupled GAs with different game scenarios for future advanced multi-disciplinary aerospace techmologies.

Robust optimization design on impeller of mixed-flow pump

To increase the robustness of the optimization solutions of the mixed-flow pump,the impeller was firstly indirectly parameterized based on the 2D blade design theory.Secondly,the robustness of the optimization solution was mathematically defined,and then calculated by Monte Carlo sampling method.Thirdly,the optimization on the mixed-flow pump′s impeller was decomposed into the optimal and robust sub-optimization problems,to maximize the pump head and efficiency and minimize the fluctuation degree of them under varying working conditions at the same time.Fourthly,using response surface model,a surrogate model was established between the optimization objectives and control variables of the shape of the impeller.Finally,based on a multi-objective genetic optimization algorithm,a two-loop iterative optimization process was designed to find the optimal solution with good robustness.Comparing the original and optimized pump,it is found that the internal flow field of the optimized pump has been improved under various operating conditions,the hydraulic performance has been improved consequently,and the range of high efficient zone has also been widened.Besides,with the changing of working conditions,the change trend of the hydraulic performance of the optimized pump becomes gentler,the flow field distribution is more uniform,and the influence degree of the varia-tion of working conditions decreases,and the operating stability of the pump is improved.It is concluded that the robust optimization method proposed in this paper is a reasonable way to optimize the mixed-flow pump,and provides references for optimization problems of other fluid machinery.

Web-Based Synthetic Optimization Design System of Micro-Components

In order to meet the requirement of network synthesis optimization design for a micro component, a three-level information frame and functional module based on web was proposed. Firstly, the finite element method （FEM） was used to analyze the dynamic property of coupled-energy-domain of virtual prototype instances and to obtain some optimal information data. Secondly, the rough set theory （RST） and the genetic algorithm （GA） were used to work out the reduction of attributes and the acquisition of principle of optimality and to confirm key variable and restriction condition in the synthesis optimization design. Finally, the regression analysis （RA） and GA were used to establish the synthesis optimization design model and carry on the optimization design. A corresponding prototype system was also developed and the synthesis optimization design of a thermal actuated micro-pump was carded out as a demonstration in this paper.

Optimization on Bicycle Sharing Network

In this paper, we propose an ordinal optimization based simulation optimization algorithm to determine a target distribution of bicycles for a bicycle sharing network to minimize an expected cost. The proposed algorithm consists of two stages. The first stage is using GA （genetic algorithm） assisted by a surrogate model to select an estimated good enough subset of solutions. The second stage is to identify the best solution among the solutions obtained from stage one using optimal computing budget allocation technique. We have tested the proposed algorithm on a bicycle sharing network and compared the test results with those obtained by the GA with exact model. The test results demonstrate that the proposed algorithm can obtain a good enough solution within reasonable computing time and outperforms the comparing method.

Multi-objective optimization of crimping of large-diameter welding pipe

Crimping is widely adopted in the production of large-diameter submerged-arc welding pipes. Traditionally, designers obtain the technical parameters for crimping from experience or by trial and error through experiments and the finite element(FE) method. However, it is difficult to achieve ideal crimping quality by these approaches. To resolve this issue, crimping parameter design was investigated by multi-objective optimization. Crimping was simulated using the FE code ABAQUS and the FE model was validated experimentally. A welding pipe made of X80 high-strength pipeline steel was considered as a target object and the optimization problem for its crimping was formulated as a mathematical model and crimping was optimized. A response surface method based on the radial basis function was used to construct a surrogate model; the genetic algorithm NSGA-II was adopted to search for Pareto solutions; grey relational analysis was used to determine the most satisfactory solution from the Pareto solutions. The obtained optimal design of parameters shows good agreement with the initial design and remarkably improves the crimping quality. Thus, the results provide an effective approach for improving crimping quality and reducing design times.

Satellite Constellation Configuration Design with Rapid Performance Calculation and Ordinal Optimization

Satellite constellation configuration design is a complicated and time-consuming simulation optimization problem. In this paper, a new method called the rapid method for satellite constellation performance calculation is developed by the Hermite interpolation technique to reduce the computing complication and time. The constellation configuration optimization model is established on the basis of the rapid performance calculation. To reduce the search space and enhance the optimization efficiency, this paper presents a new constellation optimization strategy based on the ordinal optimization （00） theory and expands the algorithm realization for constellation optimization including precise and crude models, ordered performance curves, selection rules and selected subsets. Two experiments about navigation constellation and space based surveillance system （SBSS） are carried out and the analysis of simulation results indicates that the ordinal optimization for satellite constellation configuration design is effective.

Trajectory Planning for Automated Driving Based on Ordinal Optimization

This paper proposes an approach based on Ordinal Optimization（OO） to solve trajectory planning for automated driving. As most planning approaches based on candidate curves optimize the trajectory curve and the velocity profile separately, this paper formulates the problem as an unified Non-Linear Programming（NLP） model,optimizing the trajectory curve and the acceleration profile（acceleration is the derivative of velocity） simultaneously.Then a hybrid optimization algorithm named OODE, developed by combining the idea of OO and Differential Evolution（DE）, is proposed to solve the NLP model. With the acceleration profile optimized ＂roughly＂, OODE computes and compares ＂rough＂（biased but computationally-easier） curve evaluations to select the best curve from candidates, so that a good enough curve can be obtained very efficiently. Then the acceleration profile is optimized again ＂accurately＂ with the selected curve. Simulation results show that good enough solutions are ensured with a high probability and our method is capable of working in real time.

Remanufacturing planning based on constrained ordinal optimization

Resource planning for a remanufacturing system is in general extremely difficult in terms of problem size,uncertainties,complicated constraints,etc.In this paper,we present a new method based on constrained ordinal optimization(COO)for remanufacturing planning.The key idea of our method is to estimate the feasibility of plans by machine learning and to select a subset with the estimated feasibility based on the procedure of horse racing with feasibility model(HRFM).Numerical testing shows that our method is efficient and effective for selecting good plans with high probability.It is thus a scalable optimization method for large scale remanufacturing planning problems with complicated stochastic constraints.

需求变动视角下虚拟养老服务人员调度研究

针对虚拟养老服务人员实时调度问题,基于需求变动视角分别构建成本最优的调度优化模型和扰动最小的干扰管理模型,通过改进灰狼优化算法的位置更新公式,引入非支配排序设计多目标遗传灰狼优化算法。通过求解标准算例对比算法求解指标验证了算法的优越性,通过设计并求解算例验证模型的可行性。研究结果表明:相较于重调度法,干扰管理模型能够显著降低干扰事件对各主体的影响,生成更为丰富的决策集合,更加适合虚拟养老服务人员的调度问题。

一种基于自适应边界约束的高效遗传算法

针对遗传算法中用于多父体重组的系数向量缺乏高效生成方法的问题,提出一种基于自适应边界约束(ABC)的高效遗传算法。该方法依据前一个系数的值,自适应缩放后一个系数的边界,可在任意多的父代重组情形下快速生成系数向量。在CEC2017标准数据集上的实验结果表明,所提算法在29个复杂优化问题上的表现都优于经验概率分布(EDBF)算法。

基于多岛遗传算法和动量叶素理论的风力机叶片外形优化设计研究

通过调整标准NACA翼型的控制参数建立风力机叶片翼型,采用加权平均法定义优化目标函数,建立叶片外形优化模型。采用多岛遗传算法对优化模型进行求解,获得优化后翼型参数。采用Fluent软件对优化前后的风力机叶片压力梯度、气动性能进行仿真,结果表明,优化后风力机叶片的输出功率与风能利用系数增大,叶根处的弯矩减少。

基于遗传序优化算法的配电网规划

针对传统遗传算法存在收敛过早、终止条件难以确定等缺陷,将序优化理论与遗传算法相结合,用序优化的思想来指导遗传进化操作,通过算法的混合集成了序优化理论和遗传算法的优良特性,从而实现以较高的概率高效地搜索到全局最优解。对于2个IEEE配电网规划算例,以综合变电站和馈线的年投资费用、折旧费用、运行费用之和为目标函数,得到了最优的配电网拓扑结构,验证了该遗传序优化算法的有效性和实用性。

多馈入交直流输电系统的模糊控制器协调优化算法

设计了一套阻尼区域间功率振荡的模糊控制器。在多馈入交直流输电系统的直流功率控制系统和发电机励磁系统中同时采用了该模糊控制器,并对影响其性能的关键参数进行了协调优化。为了解决优化结果容易限于局部最优的问题,采用了遗传算法进行全局并行寻优,同时引入序优化理论在概率意义上保证优化解的质量。仿真结果表明:与常规阻尼控制器相比,模糊控制器能更好地提高交直流互联系统的动态稳定性且具有鲁棒性。序优化遗传算法比传统遗传算法具有更稳定的性能,可作为多馈入交直流输电系统的模糊控制器参数协调优化的一种有效方法。

考虑网络抗毁性的配电网网架多目标规划

复杂网络理论中的网络结构抗毁性从自身连接性角度描述了网络抵御破坏的能力。文中将其引入配电网网架规划中,建立了以投资、维护、运行费用之和最小,以及网络抗毁度最大为目标的配电网网架多目标规划模型,并采用向量序优化方法优化该多目标模型。向量序优化理论包括排序比较和目标软化等核心思想,能确保以很高的概率求得足够好的解,计算量大大减少,可以满足配电网规划寻求最优或次优方案的工程需要。算例优化结果表明,采用向量序优化求解配电网网架规划可以找到较优的规划方案,且与现代启发式算法相比,计算时间更少,搜索效率更高。

基于模糊序优化的风功率概率模型非参数核密度估计方法

风功率概率分布模型的研究对于风电场规划以及运行都具有重要意义。提出了一种基于模糊序优化的风功率概率密度模型非参数核密度估计方法。该方法利用风电运行数据样本构建风功率概率密度的非参数核密度估计模型;然后建立用于模型带宽选择的多目标优化模型;最后利用模糊序优化对带宽优化模型进行求解。实际算例结果表明,所提建模方法完全由样本数据驱动,不需要对概率密度模型进行先验主观假设,因而具有更高的建模精度和更强的适用性。

应用序优化理论的快速切负荷机组布点方案

具备快速切负荷(fast cut back,FCB)功能的火电机组能在外部电网发生故障时带厂用电运行,在故障消除之后迅速对外供电,是一种性能优异的黑启动电源。分析了FCB机组对电网恢复的影响,以电网机组恢复时间最短为目标,考虑机组启动时间、启动功率、线路充电时间等约束条件,建立了FCB机组优化布点方案模型。在满足工程需要的前提下提高计算效率,引入序优化理论,确保以足够高的概率得到足够好的FCB机组的布点方案。通过经典IEEE-118节点算例和某实际系统算例验证了模型和算法的有效性。算例结果表明,合理的FCB机组布点方案能够减少系统恢复时间。