模糊优化算法及其在视觉机器人路径规划中的应用

提出了视觉机器人路径规划的模糊满意优化方法.该算法基于预测控制滚动优化机制,将系统优化目标和受限约束通过模糊隶属度来表示,形成多目标模糊优化问题,解决了在全局环境未知情况下的优化路径问题,仿真结果验证了该方法的有效性.     

改进的万有引力搜索算法在函数优化中的应用

万有引力搜索算法应用于函数优化问题时易陷入局部最优解且优化精度不高。针对这些问题,提出了一种改进的万有引力搜索算法。该算法通过引入反向学习策略、精英策略和边界变异策略,显著地提高了万有引力搜索算法中粒子的探索能力与开发能力,获得了较强的全局优化能力和局部优化能力。通过对6个非线性基准函数进行仿真实验,结果表明：与基本的万有引力搜索算法、加权的万有引力搜索算法和人工蜂群算法相比,改进的万有引力搜索算法在求解复杂函数的优化问题时具有更好的优化性能。     

The pinwheel pattern and its application to the manufacturer's pallet-loading problem

To solve the manufacturer's pallet-loading problem (MPLP), pallet-loading patterns with regular, sound and optimum number layouts should be presented. In this paper, we present a pinwheel pattern as an alternative solution to the pallet-loading problem. The definition, elements, categories, and practical advantages, generating algorithms of the pinwheel patterns, are discussed, and a uniform notation is proposed. With the ranges for all pinwheel instances within an area ratio no more than 76 boxes calculated, the study of geometry shows that each pinwheel pattern has a specific range of box ratio, and it may achieve optimality. The pinwheel pattern can be found for all non-prime numbers of boxes. Further discussions are focused on the dataset, loophole constraint and asymmetric pinwheels. The study suggests that the pinwheel pattern is an advantageous alternative to implement the MPLP.     

罚函数凸优化迭代算法及在无人机路径规划中的应用

针对无人机路径规划问题,建立了具有定常非线性系统、非仿射等式约束、非凸不等式约束的非凸控制问题模型,并对该模型进行了算法设计和求解。基于迭代寻优的求解思路,提出了凸优化迭代求解方法和罚函数优化策略。前者利用凹凸过程(CCCP)和泰勒公式对模型进行凸化处理,后者将经处理项作为惩罚项施加到目标函数中以解决初始点可行性限制。经证明该方法严格收敛到原问题的Karush-Kuhn-Tucker(KKT)点。仿真实验验证了罚函数凸优化迭代算法的可行性和优越性,表明该算法能够为无人机规划出一条满足条件的飞行路径。     

Pareto最大最小蚂蚁算法及其在热轧批量计划优化中的应用

针对双目标旅行商问题提出了基于Pareto概念的最大最小蚂蚁算法(P--MMAS). 通过重新设计状态转移策略、信息素更新策略及局部搜索策略, 同时引入基于自适应网格的多样性保持策略与信息素平滑机制, 使算法能够快速搜索到在目标空间上均匀分布的近似Pareto前端. 通过在6个标准测试函数上的实验及在热轧批量计划优化中的应用, 表明P--MMAS具有良好的优化性能及实用性.     

Haptic assisted aircraft optimal assembly path planning scheme based on swarming and artificial potential field approach

In this research, a novel near optimum automated rigid aircraft engine parts assembly path planning algorithm based on particle swarm optimization approach is proposed to solve the obstacle free assembly path planning process in a 3d haptic assisted environment. 3d path planning using valid assembly sequence information was optimized by combining particle swarm optimization algorithm enhanced by the potential field path planning concepts. Furthermore, the presented approach was compared with traditional particle swarm optimization algorithm (PSO), ant colony optimization algorithm (ACO) and genetic algorithm (CGA). Simulation results showed that the proposed algorithm has faster convergence rate towards the optimal solution and less computation time when compared with existing algorithms based on genetics and ant colony approach. To confirm the optimality of the proposed algorithm, it was further experimented in a haptic guided environment, where the users were assisted with haptic active guidance feature to perform the process opting the optimized assembly path. It was observed that the haptic guidance feature further reduced the overall task completion time.     

强化信息交流的堆优化算法及其机器人路径规划

堆优化算法(heap based optimizer,HBO)是最近提出的一种新型和优秀的智能优化算法,但需完善并拓展其应用领域?针对HBO信息交流不足导致其搜索能力不强的缺陷,提出了一种改进的HBO,即强化信息交流的HBO(information interchange strengthened HBO,ISHBO)?首先,采用一种自身交流机制用于HBO中的最高领导者的状态更新,解决因它无直接领导和同事无法参与搜索过程从而导致搜索效率低下的问题;其次,提出了一种精英反向交流策略和一种精英引导的自由交流策略,以突破当前个体仅与直接领导及同事交流的限制;最后,将以上两种交流策略有机融合用于HBO随机选择的三个个体的状态更新上以提高其搜索能力,如此形成ISHBO?大量的?不同类型的和不同维度的函数优化实验结果表明,与HBO以及其他最先进算法相比,ISHBO的搜索能力更强?另外,智能优化算法用于机器人路径规划的仿真结果也表明,ISHBO在解决实际优化问题上具有更强的竞争性。     

A visual tool for computer supported learning: The robot motion planning example

We introduce an effective computer aided learning visual tool (CALVT) to teach graph-based applications. We present the robot motion planning problem as an example of such applications. The proposed tool can be used to simulate and/or further to implement practical systems in different areas of computer science such as graphics, computational geometry, robotics and networking. In the robot motion planning example, CALVT enables users to setup the working environment by creating obstacles and a robot of different shapes, specifying starting and goal positions, and setting other path or environment parameters from a user-friendly interface. The path planning system involves several phases. Each of these modules is complex and therefore we provide the possibility of visualizing graphically the output of each phase. Based on our experience, this tool has been an effective one in classroom teaching. It not only cuts down, significantly, on the instructor’s time and effort but also motivates senior/graduate students to pursue work in this specific area of research.     

免疫遗传算法及在新产品投入计划中的应用

提出一种新的求解复杂约束优化问题的免疫遗传算法. 算法首先产生大量抗原来训练抗体, 从而建立起一个具有自体和非自体识别能力的免疫系统. 将该免疫系统嵌入到遗传算法中, 即可在遗传过程中不经解码就能识别非法或不可行的染色体. 算法有效地改进了遗传算法求解复杂约束优化问题的性能. 算法用于求解用半无限规划模型描述的新产品投入计划问题, 获得了满意的结果.     

The angular difference function and its application to image registration

The estimation of large motions without prior knowledge is an important problem in image registration. In this paper, we present the angular difference function (ADF) and demonstrate its applicability to rotation estimation. The ADF of two functions is defined as the integral of their spectral difference along the radial direction. It is efficiently computed using the pseudopolar Fourier transform, which computes the discrete Fourier transform of an image on a near spherical grid. Unlike other Fourier-based registration schemes, the suggested approach does not require any interpolation. Thus, it is more accurate and significantly faster.     

竞选算法及其在函数全局最优化问题中的应用

竞选算法是借鉴人类竞选活动中所蕴涵的优化思想而建立的一种优化算法,其搜索机制模拟的是竞选人在整个竞选过程中追求最高支持率的行为。介绍了算法的基本思想、基本原理和计算步骤,并将竞选算法应用于求解函数的全局最优解。通过对标准测试函数优化的数值实验结果表明,竞选算法可快速搜索到函数的全局最优解,并具有较好的稳定性。     

Modified Nelder-Mead self organizing migrating algorithm for function optimization and its application

In this paper, a modified Nelder Mead Self Organizing Migrating Algorithm (mNM-SOMA) has been presented for solving unconstrained optimization problems. It is based on the hybridization of self organizing migrating algorithm (SOMA) with modified Nelder Mead (mNM) Crossover Operator. SOMA is a low population based technique that has good exploration and exploitation qualities, but sometimes converges premature to local optima solution due to lack of diversity preserve mechanism. In this paper an attempt has been made to improve the efficiency of SOMA using a modified NM crossover operator (mNM) for maintaining the diversity in the search space. mNM-SOMA has been tested on a set of 15 test problems, taken form literature and results are compared with the results obtained by self organizing migrating genetic algorithm (SOMGA), SOMA, genetic algorithm (GA) and particle swarm optimization (PSO). For better presentation, results are also analyzed graphically using a Performance Index. Besides this, mNM-SOMA has also been used to solve Frequency Modulation Sounds Parameter Identification Problem. Analysis of numerical results infers mNM-SOMA as a less expensive robust technique.     

A new approach to code motion and its application to hoisting

An approach to code motion and hoisting, a program optimization technique, is discussed. The safety and profitability of optimization in general and hoisting in particular are analyzed. By restricting the analysis to a spanning tree imposed on the program graph, a linear algorithm is developed that provides sufficient but not necessary conditions for hoisting.     

改进光学优化算法及其在函数优化中的应用

光学优化算法是一种新型优化算法,源自物理学中的光学原理。针对基本光学优化算法中适应度函数随进化过程恒定不变导致算法搜索能力差、精度低等不足之处,结合遗传算法中自适应度的改进方法,提出一种可随进化代数动态调整的非线性适应度函数,改进了光学优化算法的适应度函数。通过一系列典型的基准函数测试了改进算法的性能,实验结果验证了改进算法的可行性与有效性。     

面向多峰函数优化的PASFLI算法及应用

针对基本混合蛙跳算法在高维多峰函数优化时早熟及难以找到所有全局极值的问题,提出了一种具有混合智能的多态子种群自适应混合蛙跳免疫算法,证明了算法以概率1收敛于全局最优解。该算法采用双层进化模式,融合了混合蛙跳、免疫克隆选择技术。在低层混合蛙跳操作中,加入了多态自适应子种群机制,提高了子种群多样性,有效抑制了早熟现象;在算法进化后期,提出了全局极值筛选策略,将子种群极值点提升到高层免疫克隆选择操作,进一步提高了全局寻优能力。通过复杂多峰函数仿真实验,表明该算法能够快速有效地给出全部全局最优解。     

Time-dependent shortest paths through a fixed sequence of nodes: application to a travel planning problem

In this paper, we introduce a travel planning problem which is solved by computing time-dependent shortest paths through a fixed sequence of nodes. Given a predetermined itinerary, our travel planning problem consists in finding the best travel plan, involving planes and hotels, based on the traveler's preferences. Our time-dependent framework therefore models plane flights, hotels, stays in each city as well as global time constraints. Given the large size of time-dependent networks, an exact decomposition algorithm is devised to solve instances of realistic size in reasonable computation times.     

改进的AGA及其在约束函数优化中的应用

提出了一种改进的基于自适应惩罚函数的AGA。一方面,采用分裂选择算子,增加了潜在优良个体的生存概率;另一方面,引入基于优势遗传的交叉概率和变异概率,防止了算法的早熟收敛。此外,应用改进的最优保存策略,保证了算法的收敛性和收敛解的有效性。通过对约束函数优化的仿真计算,证明该算法具有快速收敛和鲁棒性好的特点。     

快速演化算法及其在函数优化中的应用

提出一种新的快速演化算法,并把它运用于函数优化问题的求解中.新算法的特征是引入一种基于高斯变异.Cauchy变异以及Lévy变异的混合自适应变异算子,采用多父体搜索策略,提出随机排序选择策略.通过23个标准测试函数进行测试,结果表明,新算法在21个测试函数中的结果比FEP和EP好,具有稳定、高效和快速等特点.     

Robust optimization-based multi-loop PID controller tuning: A new tool and its industrial application

Modern process plants are highly integrated and as a result, decentralized PID control loops are often strongly interactive. The iterative SISO tuning approach currently used in industry is not only time consuming, but does also not achieve optimal performance of the inherently multivariable control system. This paper describes a method and a software tool that allows control engineers/technicians to calculate optimal PID controller settings for multi-loop process systems. It requires the identification of a full dynamic model of the multivariable system, and uses constrained nonlinear optimization techniques to find the controller parameters. The solution is tailored to the specific control system and PID algorithm to be used. The methodology has been successfully applied in many industrial advanced control projects. The tuning results that have been achieved for interacting PID control loops in the stabilizing section of an industrial Gasoline Treatment Unit as well as a Diesel Desulfurization plant are presented.     

化工企业生产计划图形建模优化系统开发及其应用

为优化化工企业生产计划,本文建立了化工过程生产计划优化的混合整数非线性规划(mixed integer nonlinear program- ming)模型,并给出相应的迭代求解算法,实际应用表明该算法可以有效的求解模型。应用该MINLP模型和求解算法,在石化企业生产计划图形建模优化系统(graphic I/O petrochemical industry modeling system,GIOPIMS)已经成功开发和实施的基础上,针对化工企业的特点,重新开发出适合化工企业使用的生产计划图形建模优化系统(graphic I/O chemical industry modeling system,GIOCIMS)。GIOCIMS的实施表明,该系统在化工企业中间产品外购或自产,中间产品外销或深加工,工艺路线选择和装置负荷等优化方面发挥了重要作用,获得显著的经济效益?。