A Meta‐Heuristic Algorithm for Multi‐Objective Optimal Design of Hybrid Laminate Composite Structures

Abstract: In this article, we propose a meta‐heuristic algorithm for solving multi‐objective combinatorial optimization problems. The proposed multi‐objective combinatorial optimization algorithm is developed by combining the good features of popular guided local search algorithms like simulated annealing (SA) and tabu search (TS). It has been organized as a multiple start algorithm to maintain a good balance between intensification and diversification. The proposed meta‐heuristic algorithm is evaluated by solving the stacking sequence optimization of hybrid fiber‐reinforced composite plate, cylindrical shell, and pressure vessel problems. The standard performance metrics for evaluating multi‐objective optimization algorithms are used to demonstrate the effectiveness of the proposed algorithm over other popular evolutionary algorithms like Nondominated Sorting Genetic Algorithms (NSGA‐II), Pareto Archived Evolutionary Strategy (PAES), micro‐GA, and Multi‐Objective Particle Swarm Optimization (MOPSO).     

Optimization of reinforcement layout of scissor‐type bridge using differential evolution algorithm

Scissors mechanisms are commonly used in safety engineering during the construction of temporary structures, owing to their inherent advantages of foldability, transformability, and reusability. We effectively utilized these scissors mechanism features to develop a lightweight, deployable emergency Mobile Bridge (MB) based on optimization, and control of the folding structure. Here, we discuss the problems of optimal reinforcement layout for the MB by formulating and solving three optimization problems, namely: (a) the load capacity maximization problem, (b) the weight minimization problem, and (c) coupling the load capacity maximization problem and the weight minimization problem. The potential benefits resulting from the application of reinforcement were evaluated using a combination of finite element analysis and an optimization algorithm based on the differential evolution method. The results demonstrate the significant positive influence of the additional reinforcing members. In particular, the limit load was increased by over 10 times, while the weight was decreased to approximately half. The proposed methodology enabled the development of a substantially improved version of the MB characterized by a higher load capacity and lower weight in comparison to the initial bridge design.     

A Hybrid Particle Swarm—Gradient Algorithm for Global Structural Optimization

Abstract: The particle swarm optimization (PSO) method is an instance of a successful application of the philosophy of bounded rationality and decentralized decision making for solving global optimization problems. A number of advantages with respect to other evolutionary algorithms are attributed to PSO making it a prospective candidate for optimum structural design. The PSO‐based algorithm is robust and well suited to handle nonlinear, nonconvex design spaces with discontinuities, exhibiting fast convergence characteristics. Furthermore, hybrid algorithms can exploit the advantages of the PSO and gradient methods. This article presents in detail the basic concepts and implementation of an enhanced PSO algorithm combined with a gradient‐based quasi‐Newton sequential quadratic programming (SQP) method for handling structural optimization problems. The proposed PSO is shown to explore the design space thoroughly and to detect the neighborhood of the global optimum. Then the mathematical optimizer, starting from the best estimate of the PSO and using gradient information, accelerates convergence toward the global optimum. A nonlinear weight update rule for PSO and a simple, yet effective, constraint handling technique for structural optimization are also proposed. The performance, the functionality, and the effect of different setting parameters are studied. The effectiveness of the approach is illustrated in some benchmark structural optimization problems. The numerical results confirm the ability of the proposed methodology to find better optimal solutions for structural optimization problems than other optimization algorithms.     

Application of evolutionary global optimization techniques in the design of RC water tanks

This paper presents evolutionary-based optimization procedures for designing conical reinforced concrete water tanks. The material cost of the tank that includes concrete, reinforcement, and formwork required for walls and floor was chosen as the objective function in the nonlinear optimization problem formulation. The wall thicknesses (at the bottom and at the top), base thickness, depth of water tank, and wall inclination were considered as design variables.Three advanced optimization techniques to solve the nonlinear constrained structural optimization problems were investigated. These methods are: (1) Shuffled Complex Evolution (SCE), (2) Simulated Annealing (SA) and Genetic Algorithm (GA). Several tests were performed to illustrate the robustness of these techniques and results were encouraging for SCE Method. The SCE method proved to be superior to the SA and GA methods in obtaining the best discovered solutions. The paper concludes that the robust search capability of SCE algorithm technique is well suited for solving the structural problem in hand.     

边坡稳定极限平衡分析中的优化问题

为更好地理解、归纳边坡极限平衡稳定分析的步骤和过程,将目前边坡稳定分析中常规的极限平衡条分法、基于潘家铮极大值原理的局部安全系数法以及边坡临界滑动场方法等三大类方法进行系统比较分析和归类,并对每类双重优化问题予以表述、算例分析验证等,对极限平衡方法在边坡稳定分析中的应用予以总结。研究结果表明,归纳的三类双重优化问题有助于极限平衡方法在边坡稳定分析中的进一步应用。     

Multimode Project Scheduling Based on Particle Swarm Optimization

Abstract:  The multimode resource-constrained project scheduling problem (MRCPSP) considers both renewable and nonrenewable resources that have not been addressed efficiently in the construction field. This article introduces a methodology for solving the MRCPSP based on particle swarm optimization (PSO) that has not been utilized for this and other construction-related problems. The framework of the PSO-based methodology is developed. A particle representation formulation is proposed to represent the potential solution to the MRCPSP in terms of priority combination and mode combination for activities. Each particle-represented solution should be checked against the nonrenewable resource infeasibility and will be handled by adjusting the mode combination. The feasible particle-represented solution is transformed to a schedule through a serial generation scheme. Experimental analyses are presented to investigate the performance of the proposed methodology.     

Optimal placement of metallic dampers for seismic upgrading of multistory buildings based on a cost‐effectiveness criterion using genetic algorithm

In this paper, an optimal placement methodology for metallic dampers is proposed to upgrade the seismic performance of multistory buildings. Most previous studies on optimal damper placement (ODP) problems have been focused on minimizing the seismic responses, whereas the present study aims to utilize the minimum total cost of dampers to achieve a prescribed level of seismic response. To this end, the optimization objective is constructed based on a cost‐effectiveness criterion, and the optimization constraint is defined based on a desired level of seismic response. An improved integer‐coded genetic algorithm is presented for solving the ODP problem. A 16‐story shear building is illustrated to verify the proposed optimal placement methodology. It is shown that the proposed methodology can be used to achieve the predetermined performance level while minimizing the retrofitting cost. Moreover, different algorithms, objective functions, and levels of accuracy on the optimization are also compared. Finally, a two‐step optimization approach is proposed for achieving better placement schemes with less computational efforts.     

市政排水管网规划和优化设计探讨

分析了市政排水管网规划和优化设计的几个核心问题,把整个排水管网布局的优化问题转变为求网络图的最小费用问题,给出了数学模型的建立与求解步聚,并通过具体工程实例进行了说明,指出该文提出的修正逐步生成法能保证;n-Ⅳ步内获得系统最优布局。     

遗传算法在约束优化问题中的应用

介绍了利用遗传算法求解约束优化问题的一般方法,在分析传统方法的基础上提出一种遗传算法求解约束优化问题的新方法,还对该方法在不同问题下作了分析,证明了该方法对求解有约束优化问题有良好性能。     

工程结构优化设计的新方法

阐述了遗传算法求解工程结构非线性优化问题的方法 ,实例计算表明 ,具有全局优化和并行计算特点的遗传算法是求解工程结构非线性优化问题可行有效的方法。     

A global search procedure for parameter estimation in neural spatial interaction modelling

Parameter estimation is one of the central issues in neural spatial interaction modelling. Current practice is dominated by gradient based local minimization techniques. They find local minima efficiently and work best in unimodal minimization problems, but can get trapped in multimodal problems. Global search procedures provide an alternative optimization scheme that allows to escape from local minima. Differential evolution has been recently introduced as an efficient direct search method for optimizing real-valued multi-modal objective functions (Storn and Price 1997). The method is conceptually simple and attractive, but little is known about its behavior in real world applications. This article explores this method as an alternative to current practice for solving the parameter estimation task, and attempts to assess its robustness, measured in terms of in-sample and out-of-sample performance. A benchmark comparison against backpropagation of conjugate gradients is based on Austrian interregional telecommunication traffic data. Received: 11 November 1998 / Accepted: 14 January 1999     

On the potential of genetic algorithms in urban drainage modeling

A genetic algorithm (GA) is a stochastic search algorithm that applies the biological concept of survival of the fittest in order to search for the optimal solution to a problem. In this paper we explore the potential and the benefit of using GAs for solving problems in urban drainage modeling. The main problem areas where such methods are assumed to have some benefit as compared to traditional procedures are identified from the literature as model calibration and model predictive control. The use of GAs for multi-criteria decision analysis is not reported in the context of urban drainage modeling but believed to be an interesting field of application. The methodology is discussed by means of benchmark problem sets for each of the applications.     

Constrained mean‐variance mapping optimization for truss optimization problems

Truss optimization is a complex structural problem that involves geometric and mechanical constraints. In the present study, constrained mean‐variance mapping optimization (MVMO) algorithms have been introduced for solving truss optimization problems. Single‐solution and population‐based variants of MVMO are coupled with an adaptive exterior penalty scheme to handle geometric and mechanical constraints. These tools are explained and tuned for weight minimization of trusses with 10 to 200 members and up to 1,200 nonlinear constraints. The results are compared with those obtained from the literature and classical genetic algorithm. The results show that a MVMO algorithm has a rapid rate of convergence and its final solution can obviously outperform those of other algorithms described in the literature. The observed results suggest that a constrained MVMO is an attractive tool for engineering‐based optimization, particularly for computationally expensive problems in which the rate of convergence and global convergence are important.     

Compound Scaling Algorithm for Multiobjective Functions

Abstract: In this paper the possibility of using compound scaling algorithm for solving the multiobjective optimization problem is demonstrated. The algorithm treats the objective functions similar to behavior constraints during the intermediate iterations of optimization. The algorithm generates a partial Pareto curve while solving the problem. Truss and plate structures are used as example problems.     

浅谈政府投资建设项目代建制度

分析以往工程管理模式存在的问题,指出"代建制"管理模式与传统工程管理模式的区别,并对此种管理模式在运行过程中可能出现的问题做进一步探讨,从而完善代建制管理的方法及管理程序的进一步优化。     

Water Distribution System Computer‐Aided Design by Agent Swarm Optimization

Optimal design of water distribution systems (WDSs), including the sizing of components, quality control, reliability, renewal, and rehabilitation strategies, etc., is a complex problem in water engineering that requires robust methods of optimization. Classical methods of optimization are not well suited for analyzing highly dimensional, multimodal, nonlinear problems, especially given inaccurate, noisy, discrete, and complex data. Agent Swarm Optimization (ASO) is a novel paradigm that exploits swarm intelligence and borrows some ideas from multiagent‐based systems. It is aimed at supporting decision‐making processes by solving multiobjective optimization problems. ASO offers robustness through a framework where various population‐based algorithms coexist. The ASO framework is described and used to solve the optimal design of WDS. The approach allows engineers to work in parallel with the computational algorithms to force the recruitment of new searching elements, thus contributing to the solution process with expert‐based proposals.     

Optimal Layout of Bridge Trusses by Genetic Algorithms

In this paper we present an approach to the layout and shape-optimization problem of bridge truss structures using genetic algorithms. The objective is to find an optimal layout design that will have minimum weight or material volume, subject to performance constraints related to member stresses, joint displacements, and member buckling. An automated two-stage optimization search process, which integrates structural analysis by finite-element method, genetic algorithms, and cognitive topology patterns (domain knowledge), is developed to solve the optimal problem. Two examples concerning bridge truss structure are investigated to demonstrate the effectiveness of the proposed method in solving these layout-optimization problems.     

The Observability Problem in Traffic Network Models

Abstract:   This article deals with the problem of observability of traffic networks, understanding as such the problem of identifying which is the subset of OD-pair and link flows that can be calculated based on a subset of observed OD-pair and link flows and related problems. Two algebraic methods for solving the observability problems are given, one global approach based on null-spaces and a step by step procedure allowing updating the information once each item of information (OD-pair or link flow) becomes available. In particular, seven different observability problems are stated and solved using the proposed methods, which are illustrated by their application to the Nguyen-Dupuis network problem. The results show that the proposed methods provide useful information on which OD-pair or link flows are informative on other OD-pair and link flows, and that the methods are applicable to large networks .     

基于身份转换组织教学的改革与研究

依据目前大学教与学中存在的问题,提出基于身份转换的教学改革,以项目管理团队的身份,以完成任务和解决问题的方式重新设计教学环节,解决目前教学中存在的问题,以期为提高教学质量提供一种创新的视角。     

Novel decoupled framework for reliability-based design optimization of structures using a robust shifting technique

In a reliability-based design optimization (RBDO), computation of the failure probability (P_f) at all design points through the process may suitably be avoided at the early stages. Thus, to reduce extensive computations of RBDO, one could decouple the optimization and reliability analysis. The present work proposes a new methodology for such a decoupled approach that separates optimization and reliability analysis into two procedures which significantly improve the computational efficiency of the RBDO. This technique is based on the probabilistic sensitivity approach (PSA) on the shifted probability density function. Stochastic variables are separated into two groups of desired and non-desired variables. The three-phase procedure may be summarized as: Phase 1, apply deterministic design optimization based on mean values of random variables; Phase 2, move designs toward a reliable space using PSA and finding a primary reliable optimum point; Phase 3, applying an intelligent self-adaptive procedure based on cubic B-spline interpolation functions until the targeted failure probability is reached. An improved response surface method is used for computation of failure probability. The proposed RBDO approach could significantly reduce the number of analyses required to less than 10% of conventional methods. The computational efficacy of this approach is demonstrated by solving four benchmark truss design problems published in the structural optimization literature.