Methods and practice in cantilever retaining wall design

The purpose of this paper is to present information that may aid a user in the selection of a computer program for the cantilever reinforced concrete retaining wall and, to present the state-of-the-art in computerized retaining wall design. The programs discussed are available for general use by lease, time-sharing, purchasing or for the asking. Most emphasis is placed on the method of design as well as the methods for computing soil and surcharge pressures. This tends to have more permanent value than an evaluation of specific computer programs. Twelve programs are presented and compared in written and tabular form. The general information appears in Table 1 and the capabilities of each in Table 2.     

A discrete shuffled frog optimization algorithm

The shuffled frog leaping (SFL) optimization algorithm has been successful in solving a wide range of real-valued optimization problems. In this paper we present a discrete version of this algorithm and compare its performance with a SFL algorithm, a binary genetic algorithm (BGA), and a discrete particle swarm optimization (DPSO) algorithm on seven low dimensional and five high dimensional benchmark problems. The obtained results demonstrate that our proposed algorithm, i.e. the DSFL, outperforms the BGA and the DPSO in terms of both success rate and speed. On low dimensional functions and for large values of tolerance the DSFL is slower than the SFL, but their success rates are equal. Part of this slowness could be attributed to the extra bits used for data coding. By increasing number of variables and the required precision of answer, the DSFL performs very well in terms of both speed and success rate. For high dimensional problems, for intrinsically discrete problems, also when the required precision of answer is high, the DSFL is the most efficient method.     

An enhanced shuffled Shepherd Optimization Algorithm for optimal design of large-scale space structures

Shuffled Shepherd Optimization Algorithm (SSAO) is a swarm intelligence-based optimizer inspired by the herding behavior of shepherds in nature. SSOA may suffer from some shortcomings, including being trapped in a local optimum and starting from a random population without prior knowledge. This study aims to enhance the performance of the SSOA by incorporating two efficient devices. The first device is utilized from the Opposition-Based Learning (OBL) approach to improve the initialization phase of the algorithm. The second device is incorporated a solution generator in the cyclic body of the SSOA based on the statistical results of the solutions. This feature is the so-called statistically regenerated stepsize. The proposed devices provide a good balance between exploration and exploitation capability of the algorithm and reduce the probability of getting tapped in a local optimum. The viability of the proposed Enhanced Shuffled Shepherd Optimization Algorithm (ESSOA) is demonstrated through three large-scale design examples. ESSOA is compared to the standard SSOA and some other existing metaheuristic algorithms. The optimization results reveal the competence and robustness of the ESSOA for optimal design of the large-scale space structures.

     

Optimization of weight and cost of cantilever retaining wall by a hybrid metaheuristic algorithm

Engineering with Computers - A retaining wall is a structure used to resist the lateral pressure of soil or any backfill material. Cantilever retaining walls provide resistance to overturning and...     

An active set rqp algorithm for engineering design optimization

Optimum design of complex engineering systems needs a globally and superlinearly convergent (robust and efficient) algorithm using active set strategy. Such an algorithm based on extensions of Pshenichny's linearization method is derived in the paper. In the original method, a linearized subproblem with a quadratic step-size constraint is used to compute a direction of design change. No second-order information is computed or approximated for use in the direction finding problem. Therefore, the rate of convergence is only linear. In the paper, we propose to incorporate a variable metric W which is a positive-definite approximation to the Hessian of the Lagrange function. This gives local superlinear rate of convergence to the algorithm. Some other computational improvements are discussed and incorporated. The proposed improvements appear to be quite simple. They are, however, quite significant for applications to engineering design problems. This is explained and several small-scale problems are solved using a program based on the modified algorithm. Results are compared to Pshenichny's original algorithm. The modified algorithm is considerably more efficient compared to the previous algorithm. It also appears to be quite robust, though more extensive testing is needed on a wider range of problems.     

A multi-objective shuffled frog leaping algorithm for in-core fuel management optimization

The efficient operation and in-core fuel management of PWRs are of utmost importance. In the present work, a core reload optimization using Shuffled Frog Leaping (SFL) algorithm is addressed and mapped on nuclear fuel loading pattern optimization. SFL is one of the latest meta-heuristic optimization algorithms which is used for solving the discrete optimization problems and inspired from social behavior of frogs. The algorithm initiates the search from an initial population and carries forward to draw out an optimum result. This algorithm employs the use of memetic evolution by exchanging ideas between the members of the population in each local search. The local search of SFL is similar to particle swarm optimization (PSO) and applying shuffling process accomplishes the information exchange between several local searches to obtain an overall optimum result. To evaluate the proposed technique, Shekel’s Foxholes and a VVER-1000 reactor are used as test cases to illustrate performance of SFL. Among numerous neutronic and thermal-hydraulic objectives necessary for a fuel management problem to reach an overall optimum, this paper deals with two neutronic objectives, i.e., maximizing effective multiplication factor and flattening power distribution in the core, to evaluate the capability of applying SFL algorithm for a fuel management problem. The results, convergence rate and reliability of the method are quite promising and show the potential and efficiency of the technique for other optimization applications in the nuclear engineering field.     

Enhanced shuffled frog-leaping algorithm for solving numerical function optimization problems

The shuffled frog-leaping algorithm (SFLA) is a relatively new meta-heuristic optimization algorithm that can be applied to a wide range of problems. After analyzing the weakness of traditional SFLA, this paper presents an enhanced shuffled frog-leaping algorithm (MS-SFLA) for solving numerical function optimization problems. As the first extension, a new population initialization scheme based on chaotic opposition-based learning is employed to speed up the global convergence. In addition, to maintain efficiently the balance between exploration and exploitation, an adaptive nonlinear inertia weight is introduced into the SFLA algorithm. Further, a perturbation operator strategy based on Gaussian mutation is designed for local evolutionary, so as to help the best frog to jump out of any possible local optima and/or to refine its accuracy. In order to illustrate the efficiency of the proposed method (MS-SFLA), 23 well-known numerical function optimization problems and 25 benchmark functions of CEC2005 are selected as testing functions. The experimental results show that the enhanced SFLA has a faster convergence speed and better search ability than other relevant methods for almost all functions.     

改进的反向蛙跳算法求解函数优化问题

针对混洗蛙跳算法在求解连续函数优化问题中出现的收敛速度慢、求解精度低的缺点,提出了一种基于反向学习策略的改进算法,在种群初始化和进化过程中分别加入反向操作,产生更靠近优质解的种群,从而提高了算法的全局寻优能力,促进了算法收敛。实验仿真表明,新算法在寻优效率、计算精度等方面均优于原算法。     

批量无等待调度问题的微粒群蛙跳混合优化算法*

将离散微粒群与蛙跳算法相结合解决以最大完工时间为指标的批量无等待流水线调度问题.结合微粒群算法较强的全局收敛能力和蛙跳算法较强的深度搜索能力,设计了三种混合算法,平衡了算法的全局开发能力和局部探索能力.对随机生成不同规模的实例进行了广泛的实验,仿真实验结果的比较表明了所得混合算法的有效性和高效性.     

A revised discrete Lagrangian-based search algorithm for the optimal design of skeletal structures using available sections

Issues relating to the application of the discrete Lagrangian method (DLM) to the discrete sizing optimal design of skeletal structures are addressed. The resultant structure, whether truss or rigid frame, is subjected to stress and displacement constraints under multiple load cases. The members’ sections are selected from an available set of profiles. A table that contains sectional properties for all the available profiles is used directly in structural optimization. Each profile in the table is assigned by a unique profile number, which is used as the integer design variable for each of the structural members. It is proposed that we use a revised DLM search algorithm with static weighting to design trusses and rigid frames for minimum weight. Five examples are used to demonstrate the feasibility of the method. It is shown that, for monotonic as well as nonmonotonic constraint functions, the DLM is effective and robust for the discrete sizing design of skeletal structures.     

A level set approach for optimal design of smart energy harvesters

The proliferation of Micro-Electro-Mechanical Systems (MEMS), portable electronics and wireless sensing networks has raised the need for a new class of devices with self-powering capabilities. Vibration-based piezoelectric energy harvesters provide a very promising solution, as a result of their capability of converting mechanical energy into electrical energy through the direct piezoelectric effect. However, the identification of fast, accurate methods and rational criteria for the design of piezoelectric energy harvesting devices still poses a challenge. In this work, a level set-based topology optimization approach is proposed to synthesize mechanical energy harvesting devices for self-powered micro systems. The energy harvester design problem is reformulated as a variational problem based on the concept of topology optimization, where the optimal geometry is sought by maximizing the energy conversion efficiency of the device. To ensure computational efficiency, the shape gradient of the energy conversion efficiency is analytically derived using the material time derivative approach and the adjoint variable method. A design velocity field is then constructed using the steepest descent method, which is further integrated into level set methods. The reconciled level set (RLS) method is employed to solve multi-material shape and topology optimization problems, using the Merriman–Bence–Osher (MBO) operator. Designs with both single and multiple materials are presented, which constitute improvements with respect to existing energy harvesting designs.     

A systematic topology optimization approach for optimal stiffener design

A systematic topology optimization based approach is proposed to design the optimal stiffener of three-dimensional shell/plate structures for static and eigenvalue problems. Optimal stiffener design involves the determination of the best location and orientation. In this paper, the stiffener location problem is solved by a microstructure-based design domain method and the orientation problem is modelled as an optimization orientation problem of equivalent orthotropic materials, which is solved by a newly developed energy-based method. Examples are presented to demonstrate the application of the proposed approach.     

A separable augmented Lagrangian algorithm for optimal structural design

Structural and Multidisciplinary Optimization - We propose an iterative separable augmented Lagrangian algorithm (SALA) for optimal structural design, with SALA being a subset of the alternating...     

A hybrid self-adaptive particle swarm optimization and modified shuffled frog leaping algorithm for distribution feeder reconfiguration

One of the very important way to save the electrical energy in distribution system is network reconfiguration for loss reduction. This paper proposes a new hybrid evolutionary algorithm for solving the distribution feeder reconfiguration (DFR) problem. The proposed hybrid evolutionary algorithm is the combination of SAPSO (self-adaptive particle swarm optimization) and MSFLA (modified shuffled frog leaping algorithm), called SAPSO–MSFLA, which can find optimal configuration of distribution network. In the PSO algorithm, appropriate adjustment of the parameters is cumbersome and usually requires a lot of time and effort. Therefore, a self-adaptive framework is proposed to improve the robustness of the PSO, also in the modified shuffled frog leaping algorithm (MSFLA) to improve the performance of algorithm a new frog leaping rule is proposed to improve the local exploration of the SFLA. The main idea of integrating SAPSO and MSFLA is to use their advantages and avoid their disadvantages. The proposed algorithm is tested on two distribution test feeders. The results of simulation show that the proposed method is very powerful and guarantees to obtain the global optimization in minimum time.     

A surrogate-based optimization algorithm for network design problems

Network design problems (NDPs) have long been regarded as one of the most challenging problems in the field of transportation planning due to the intrinsic non-convexity of their bi-level programming form. Furthermore, a mixture of continuous/discrete decision variables makes the mixed network design problem (MNDP) more complicated and difficult to solve. We adopt a surrogate-based optimization (SBO) framework to solve three featured categories of NDPs (continuous, discrete, and mixed-integer). We prove that the method is asymptotically completely convergent when solving continuous NDPs, guaranteeing a global optimum with probability one through an indefinitely long run. To demonstrate the practical performance of the proposed framework, numerical examples are provided to compare SBO with some existing solving algorithms and other heuristics in the literature for NDP. The results show that SBO is one of the best algorithms in terms of both accuracy and efficiency, and it is efficient for solving large-scale problems with more than 20 decision variables. The SBO approach presented in this paper is a general algorithm of solving other optimization problems in the transportation field.     

Optimization of retaining wall design using evolutionary algorithms

This paper explores the performance of three evolutionary optimization methods, differential evolution (DE), evolutionary strategy (ES) and biogeography based optimization algorithm (BBO), for nonlinear constrained optimum design of a cantilever retaining wall. These algorithms are based on biological contests for survival and reproduction. The retaining wall optimization problem consists of two criteria, geotechnical stability and structural strength, while the final design minimizes an objective function. The objective function is defined in terms of both cost and weight. Constraints are applied using the penalty function method. The efficiency of the proposed method is examined by means of two numerical retaining wall design examples, one with a base shear key and one without a base shear key. The final designs are compared to the ones determined by genetic algorithms as classical metaheuristic optimization methods. The design results and convergence rate of the BBO algorithm show a significantly better performance than the other algorithms in both design cases.     

A level set topology optimization method for the buckling of shell structures

Structural and Multidisciplinary Optimization - Shell structures are some of the most widely used in engineering applications. Flat plates, stiffened panels, and wing ribs are each examples of...     

多目标分解随机粒子群优化算法及其在直线电机优化设计中的应用

本文提出了一种多目标分解随机粒子群优化算法(MDSPSO).该算法优化过程中,所有粒子按各自固定的权重向量,采用改进Tchebycheff分解方法,将求解多目标非支配解问题转化为求解多个单目标最优解问题;而后每个粒子在以自身位置、个体历史最优参考位置及群体最优参考位置的几何中心为中心,以中心到自身位置为半径的区域内,随机生成一个新的起始位置,并参考当前的速度更新下一时刻的位置.通过对测试函数多次计算得到的数据进行统计分析,表明MDSPSO的收敛性和多样性均优于另外3种对比算法.最后针对直线电机磁路复杂、有限元计算费时的问题,使用神经网络拟合直线电机结构参数与性能的关系作为优化设计的模型,应用MDSPSO算法,优化结构参数.实际测试结果表明,优化后的直线电机推力大、效率高,同时有效控制了其推力波动和生产成本.     

An optimal volume ellipsoid algorithm for parameter set estimation

A recursive ellipsoid algorithm is derived for estimating the parameter set of a single-input single-output linear time-invariant system with bounded noise. The algorithm's objective is to seek the minimal volume ellipsoid bounding the feasible parameter set. Cast in a recursive framework, where a minimal volume ellipsoid results at each recursion, the algorithm extends a result due to Khachian (see Aspvall and Stone, 1980) in which a technique was developed to solve a class of linear programming problems. This extension and application to the parameter set estimation problem have intuitive geometric appeal and are easy to implement. Comparisons are made to the optimal bounding ellipsoid algorithm of Fogel and Huang (1982), and the results are demonstrated through computer simulations