A genetic fuzzy system to model pedestrian walking path in a built environment

A study on the pedestrian’s steering behaviour through a built environment in normal circumstances is presented in this paper. The study focuses on the relationship between the environment and the pedestrian’s walking trajectory. Owing to the ambiguity and vagueness of the relationship between the pedestrians and the surrounding environment, a genetic fuzzy system is proposed for modelling and simulation of the pedestrian’s walking trajectory confronting the environmental stimuli. We apply the genetic algorithm to search for the optimum membership function parameters of the fuzzy model. The proposed system receives the pedestrian’s perceived stimuli from the environment as the inputs, and provides the angular change of direction in each step as the output. The environmental stimuli are quantified using the Helbing social force model. Attractive and repulsive forces within the environment represent various environmental stimuli that influence the pedestrian’s walking trajectory at each point of the space. To evaluate the effectiveness of the proposed model, three experiments are conducted. The first experimental results are validated against real walking trajectories of participants within a corridor. The second and third experimental results are validated against simulated walking trajectories collected from the AnyLogic® software. Analysis and statistical measurement of the results indicate that the genetic fuzzy system with optimised membership functions produces more accurate and stable prediction of heterogeneous pedestrians’ walking trajectories than those from the original fuzzy model.     

Efficient computation of evacuation routes on a three-dimensional geometric network

We consider a real-time emergency evacuation problem that seeks to compute a set of rapid evacuation routes in a building. Given a three-dimensional geometric structure of the evacuation network, an emergency evacuation route is a sequence of movements of people away from the threat or actual occurrence of a hazard (such as a fire, a hidden bomb) to a safe exit in the network. In such a network each room/crossing/exit in the building is designated as a node and the corridors/staircases/links between the rooms are edges. The evacuation times assigned to the edges are normally distributed random variables. This stochastic routing problem subject to deadline constraints is NP-hard. We provide a new pseudo-polynomial-time dynamic programming algorithm to solve this problem. Based on this algorithm, we construct two types of approximation algorithm, namely a fully polynomial-time approximation scheme providing “almost-optimal” solutions and a fully polynomial-time approximately feasible scheme yielding a best “almost feasible” solution. We present a case study and results of computational experiments to illustrate the working and efficacy of the proposed solution methods, respectively.     

优化皮革裁剪加工空行程路径的混合算法

将皮革裁剪多轮廓加工空行程路径优化问题归结为广义旅行商问题,提出了一种求解问题的混合智能优化算法。用改进了的遗传模拟退火算法优化多轮廓排列序列,结合机床特征将问题转化为多段图最短路径问题,采用动态规划算法求解。对传统的Bolt zmann更新准则进行改进,增加搜索记忆功能并设置双阂值,以在尽量保持最优性的前提下减少计算量;根据多段图最优子结构性质设计了个体适应度评价函数。实际应用效果和对标准问题的测试表明,新算法求解质量和收敛速度均有很大的提高。     

无线传感器网络数据融合路由算法的改进

无线传感器网络能量有限,数据融合能通过合并冗余数据减少传输数据量,但其本身的代价不可忽略。针对该问题,研究数据融合代价和数据传输代价对数据融合路由的影响,在基于决策数据融合技术AFST中,对直传数据采用动态最短路径(DSPT)算法,动态识别网络环境和数据特征变化,以最小的代价调整路由。实验与分析结果表明,当网络结构发生变化时,DSPT算法比SPT算法效率更高、更节能。     

Optimization of dynamic response using a monolithic-time formulation

The design of systems for dynamic response may involve constraints that need to be satisfied over an entire time interval or objective functions evaluated over the interval. Efficiently performing the constrained optimization is challenging, since the typical response is implicitly linked to the design variables through a numerical integration of the governing differential equations. Evaluating constraints is costly, as is the determination of sensitivities to variations in the design variables. In this paper, we investigate the application of a temporal spectral element method to the optimization of transient and time-periodic responses of fundamental engineering systems. Through the spectral discretization, the response is computed globally, thereby enabling a more explicit connection between the response and design variables and facilitating the efficient computation of response sensitivities. Furthermore, the response is captured in a higher order manner to increase analysis accuracy. Two applications of the coupling of dynamic response optimization with the temporal spectral element method are demonstrated. The first application, a one-degree-of-freedom, linear, impact absorber, is selected from the auto industry, and tests the ability of the method to treat transient constraints over a large-time interval. The second application, a related mass-spring-damper system, shows how the method can be used to obtain work and amplitude optimal time-periodic control force subject to constraints over a periodic time interval. This research was performed while the first author held a National Research Council Research Associateship Award at the Air Force Research Laboratory. An early version of this paper was presented at the 46th AIAA Aerospace Sciences Meeting and Exhibit, Jan 7–10, 2008, Reno, Nevada.     

Transport network design of integrated networks with real-time dynamic routing

Integrated real-time dynamic routing (IRR) networks provide dynamic routing features for multiple classes-of-service on an integrated transport network. In a previous Journal paper it is shown that IRR networks allow reduced network management costs since with real-time dynamic routing a number of network operations are simplified or eliminated, leading to savings in operations costs and expenses. In this paper a new algorithm is described for the transport design of IRR networks which achieves near-optimal capacity engineering. In particular, a Karmarkar Algorithm optimal solution to the linear programming flow model achieves approximately a 5 to 8 percentage point reduction in network design cost in comparison to the designs of pre-planned dynamic networks solved with heuristic design techniques. The optimization techniques described in this and the previous Journal paper attain significant capital cost reductions and network performance improvements by properly modeling the more efficient operation of IRR networks.     

Selection of a dynamic supply portfolio in make-to-order environment withrisks

The problem of a multi-period supplier selection and order allocation in make-to-order environment in the presence of supply chain disruption and delay risks is considered. Given a set of customer orders for finished products, the decision maker needs to decide from which supplier and when to purchase product-specific parts required for each customer order to meet customer requested due date at a low cost and to mitigate the impact of supply chain risks. The selection of suppliers and the allocation of orders over time is based on price and quality of purchased parts and reliability of supplies. For selection of dynamic supply portfolio a mixed integer programming approach is proposed to incorporate risk that uses conditional value-at-risk via scenario analysis. In the scenario analysis, the low-probability and high-impact supply disruptions are combined with the high probability and low impact supply delays. The proposed approach is capable of optimizing the dynamic supply portfolio by calculating value-at-risk of cost per part and minimizing expected worst-case cost per part simultaneously. Numerical examples are presented and some computational results are reported.     

物流配送车辆路径问题的优化研究

物流中的车辆路径问题(VRP)是目前组合优化领域的研究热点问题,VRP为NP-hard问题。本文在对VRP分析的基础上,建立数学模型,提出了一种适合求解该问题的蚁群遗传融合优化算法。提出的优化算法首先采用蚁群算法在局部阶段产生最好解,然后利用遗传算法的优良基因在全局阶段对优化解进一步优化,以获取最好路径解。实验结果表明,提出的融合算法能高效解决VRP问题,且优化效果比单算法好。     

To be fair or efficient or a bit of both

Introducing a new concept of (α,β)-fairness, which allows for a bounded fairness compromise, so that a source is allocated a rate neither less than 0α1, nor more than β1, times its fair share, this paper provides a framework to optimize efficiency (utilization, throughput or revenue) subject to fairness constraints in a general telecommunications network for an arbitrary fairness criterion and cost functions. We formulate a non-linear program (NLP) that finds the optimal bandwidth allocation by maximizing efficiency subject to (α,β)-fairness constraints. This leads to what we call an efficiency–fairness function, which shows the benefit in efficiency as a function of the extent to which fairness is compromised. To solve the NLP we use two algorithms. The first is a well-known branch-and-bound-based algorithm called Lipschitz Global Optimization and the second is a recently developed algorithm called Algorithm for Global Optimization Problems (AGOP).We demonstrate the applicability of the framework to a range of examples from sharing a single link to efficiency fairness issues associated with serving customers in remote communities.     

改进的蚁群-遗传算法在优化航线中的应用

航线优化是一个重要问题。提出的改进算法是先通过限制、选择和更新信息素、控制周游次数,找出航线的满意解,大大缩短了搜索时间;再用所得较好的航线表示作为初始种群,指定为父体,直接进行分组定界操作,将已得航线进行优化改良,求得最佳航线。实验结果表明,该算法应用于求解航线优化问题行之有效。     

A dynamic binary translation system in a client/server environment

With rapid advances in mobile computing, multi-core processors and expanded memory resources are being made available in new mobile devices. This trend will allow a wider range of existing applications to be migrated to mobile devices, for example, running desktop applications in IA-32 (x86) binaries on ARM-based mobile devices transparently using dynamic binary translation (DBT). However, the overall performance could significantly affect the energy consumption of the mobile devices because it is directly linked to the number of instructions executed and the overall execution time of the translated code. Hence, even though the capability of today’s mobile devices will continue to grow, the concern over translation efficiency and energy consumption will put more constraints on a DBT for mobile devices, in particular, for thin mobile clients than that for severs. With increasing network accessibility and bandwidth in various environments, it makes many network servers highly accessible to thin mobile clients. Those network servers are usually equipped with a substantial amount of resources. This provides an opportunity for DBT on thin clients to leverage such powerful servers. However, designing such a DBT for a client/server environment requires many critical considerations.In this work, we looked at those design issues and developed a distributed DBT system based on a client/server model. It consists of two dynamic binary translators. An aggressive dynamic binary translator/optimizer on the server to service the translation/optimization requests from thin clients, and a thin DBT on each thin client to perform lightweight binary translation and basic emulation functions for its own. With such a two-translator client/server approach, we successfully off-load the DBT overhead of the thin client to the server and achieve a significant performance improvement over the non-client/server model. Experimental results show that the DBT of the client/server model could achieve 37% and 17% improvement over that of non-client/server model for x86/32-to-ARM emulation using MiBench and SPEC CINT2006 benchmarks with test inputs, respectively, and 84% improvement using SPLASH-2 benchmarks running two emulation threads.     

Application of evolutionary algorithms for optimum layout of Truss-Z linkage in an environment with obstacles

Truss-Z (TZ) is a concept of a modular system for creating free-form links and ramp networks. It is intended as a universal transportation system for cyclists and pedestrians, especially ones with strollers or carts, and in particular – by persons on wheelchairs, the elders, etc. In other words, TZ is for people who have difficulties using regular stairs or escalators. With only two types of modules, TZ can be designed for nearly any situation and therefore is particularity suited for retrofitting to improve the mobility, comfort and safety of the users. This paper presents an application of evolution strategy (ES) and genetic algorithm (GA) for optimization of the planar layout of a TZ linkage connecting two terminals in a given environment. The elements of the environment, called obstacles, constrain the possible locations of the TZ modules. Criteria of this multi-objective optimization are: the number of modules to be the smallest, which can be regarded as quantitative economical optimization, and the condition that none of the modules collides with any other objects, which can be regarded as qualitative satisfaction of the geometrical constraints. Since TZ is modular, the optimization of its layout is discrete and therefore has combinatorial characteristic. Encoding of a planar TZ path, selection method, objective (cost) function and genetic operations are introduced. A number of trials have been performed; the results generated by ES and GA are compared and evaluated against backtracking-based algorithm and random search. The convergence of solutions is discussed and interpreted. A visualization of a realistic implementation of the best solution is presented. Further evaluation of the method on three other representative layouts is presented and the results are briefly discussed.     

Necessary and sufficient optimality conditions of a design decision on the structure of a local computer network

A problem of nonlinear programming with Boolean variables is considered, for which the possibility of extension of the optimality conditions of solving a special problem of nonlinear programming (with continuous variables) is shown by statistical estimation of possible design decisions. This contributed to the solution of the initial problem by simpler methods. Translated from Kibernetika i Sistemnyi Analiz, No. 5, pp. 133–137, September–October, 1999.     

非线性动态优化仿真在间歇过程中的应用

为实现求解大规模非线性系统最优控制问题的自动设计及算法自动生成问题,开发基于非线性规划的动态优化软件集成系统.首先自定义基于XML的标准化非线性动态优化标记语言NDOML,采用NDOML模型设计模型编辑器;结合联立法和多点打靶法对NDOML模型进行自动离散化处理;最后利用内点算法解题器IPOPT设计了解题器接口程序生成器,实现模型录入通用化、模型离散自动化及优化控制算法代码的自动生成等多种应用功能.最后通过一个间歇反应过程实例验证动态优化软件系统的有效性,结果显示,系统能够实现最优控制算法的自动生成,仿真结果为大型非线性系统求解优化提供了依据.     

Representation of the non-dominated set in biobjective discrete optimization

This paper introduces several algorithms for finding a representative subset of the non-dominated point set of a biobjective discrete optimization problem with respect to uniformity, coverage and the ϵ-indicator. We consider the representation problem itself as multiobjective, trying to find a good compromise between these quality measures. These representation problems are formulated as particular facility location problems with a special location structure, which allows for polynomial-time algorithms in the biobjective case based on the principles of dynamic programming and threshold approaches. In addition, we show that several multiobjective variants of these representation problems are also solvable in polynomial time. Computational results obtained by these approaches on a wide range of randomly generated point sets are presented and discussed.     

Data Mining using Genetic Programming for Construction of a Semiconductor Manufacturing Yield Rate Prediction System

The complexity of semiconductor manufacturing is increasing due to the smaller feature sizes, greater number of layers, and existing process reentry characteristics. As a result, it is difficult to manage and clarify responsibility for low yields in specific products. This paper presents a comprehensive data mining method for predicting and classifying the product yields in semiconductor manufacturing processes. A genetic programming (GP) approach, capable of constructing a yield prediction system and performing automatic discovery of the significant factors that might cause low yield, is presented. Comparison with the results then is performed using a decision tree induction algorithm. Moreover, this research illustrates the robustness and effectiveness of this method using a well-known DRAM fab’s real data set, with discussion of the results. Received: November 2004 / Accepted: September 2005     

Copying of dynamic structures in a pascal environment

Two problems involving the use of dynamic lists in Pascal are (1) the limited size of the heap and (2) the difficulty in creating copies of complex multi-linked structures. Both these problems can be solved by writing the appropriate segment of memory to a file on disk. The heap segment may then be released. To recover the structure during a later phase of processing, the contents of the file can be transferred back onto the heap. This paper presents an algorithm to implement this solution.     

Energy Consumption Prediction of a CNC Machining Process With Incomplete Data

Energy consumption prediction of a CNC machining process is important for energy efficiency optimization strategies.To improve the generalization abilities,more and more parameters are acquired for energy prediction modeling.While the data collected from workshops may be incomplete because of misoperation,unstable network connections,and frequent transfers,etc.This work proposes a framework for energy modeling based on incomplete data to address this issue.First,some necessary preliminary operations are used for incomplete data sets.Then,missing values are estimated to generate a new complete data set based on generative adversarial imputation nets(GAIN).Next,the gene expression programming(GEP)algorithm is utilized to train the energy model based on the generated data sets.Finally,we test the predictive accuracy of the obtained model.Computational experiments are designed to investigate the performance of the proposed framework with different rates of missing data.Experimental results demonstrate that even when the missing data rate increases to 30%,the proposed framework can still make efficient predictions,with the corresponding RMSE and MAE 0.903 k J and 0.739 k J,respectively.     

基于聚类的数据加权优化在犯罪预测中的应用

近年来，我国传统暴力犯罪与成年人犯罪呈下降态势，但是，犯罪案由层出不穷。为有效提升公安实践工作中犯罪预测能力，打击各类违法犯罪事件,本文针对犯罪数据，提出一种新型犯罪预测模型。利用密度聚类分析方法将犯罪数据分类，然后进行数据降维提取关键属性生成特征数据，继而对特征数据进行加权优化并采用机器学习的方式对特征数据进行学习，从而预测犯罪案由。实验结果表明，与传统方法相比，本文方法具有更好的预测效果，为公安实践工作中类似案件的侦破和预防，提供新的路径支撑。     

Yard crane scheduling in a container terminal for the trade-off between efficiency and energy consumption

Green transportation has recently been the focus of the transportation industry to sustain the development of global economy. Container terminals are key nodes in the global transportation network and energy-saving is a main goal for them. Yard crane (YC), as one type of handling equipment, plays an important role in the service efficiency and energy-saving of container terminals. However, traditional methods of YC scheduling solely aim to improve the efficiency of container terminals and do not refer to energy-saving. Therefore, it is imperative to seek an appropriate approach for YC scheduling that considers the trade-off between efficiency and energy consumption. In this paper, the YC scheduling problem is firstly converted into a vehicle routing problem with soft time windows (VRPSTW). This problem is formulated as a mixed integer programming (MIP) model, whose two objectives minimize the total completion delay of all task groups and the total energy consumption of all YCs. Subsequently, an integrated simulation optimization method is developed for solving the problem, where the simulation is designed for evaluating solutions and the optimization algorithm is designed for exploring the solution space. The optimization algorithm integrates the genetic algorithm (GA) and the particle swarm optimization (PSO) algorithm, where the GA is used for global search and the PSO is used for local search. Finally, computational experiments are conducted to validate the performance of the proposed method.