Robust seismic design optimization of steel structures

Stochastic performance measures can be taken into account, in structural optimization, using two distinct formulations: robust design optimization (RDO) and reliability-based design optimization (RBDO). According to a RDO formulation, it is desired to obtain solutions insensitive to the uncontrollable parameter variation. In the present study, the solution of a structural robust design problem formulated as a two-objective optimization problem is addressed, where cross-sectional dimensions, material properties and earthquake loading are considered as random variables. Additionally, a two-objective deterministic-based optimization (DBO) problem is also considered. In particular, the DBO and RDO formulations are employed for assessing the Greek national seismic design code for steel structural buildings with respect to the behavioral factor considered. The limit-state-dependent cost is used as a measure of assessment. The stochastic finite element problem is solved using the Monte Carlo Simulation method, while a modified NSGA-II algorithm is employed for solving the two-objective optimization problem.     

Improving Combinatorial Optimization Algorithms through Record Keeping in Constructive Multistart Search

Constructive multistart search algorithms are commonly used to address combinatorial optimization problems; however, constructive multistart search algorithm performance is fundamentally affected by two factors: (i) The choice of construction algorithm utilized and (ii) the rate of state space search redundancy. Construction algorithms are typically specific to a particular combinatorial optimization problem; therefore, we first investigate construction algorithms for iterative hill climbing applied to the traveling salesman problem and experimentally determine the best performing algorithms. We then investigate the more general problem of utilizing record‐keeping mechanisms to mitigate state space search redundancy. Our research shows that a good choice of construction algorithm paired with effective record keeping significantly improves the quality of traveling salesmen problem solutions in a constant number of state explorations. Particularly, we show that Bloom filters considerably improve time performance and solution quality for iterative hill climbing approaches to the traveling salesman problem.     

遗传算法在风光互补发电系统优化设计中的应用

风光互补发电系统的优化配置是一个多目标优化问题,优化目标为系统安装成本,约束条件为供电可靠性。如何合理的匹配设计是充分发挥风光互补发电优越性的关键。在成本(目标)函数的最小化计算中,采用改进的遗传算法进行优化,随机搜索并采用选择、交叉、变异三种基本算子在全部组合中搜索最优化的配置。结果表明在满足负荷用电的前提下,其经济性能优于单独的光伏系统和单独的风电系统。     

集卡分批到达模式下的进口箱场桥作业调度优化

在进口箱疏港过程中, 服务于相同客户的若干集卡组成集卡组, 具有相同的抵港时间, 因此, 外部集卡抵港提箱呈现分批到达的特点. 集卡组内作业指派的优劣直接影响场桥的作业效率, 存在较大的优化空间. 对此, 基于翻箱作业不能跨贝进行的现实约束, 将场桥作业调度解构为场桥作业路径优化问题和贝内翻箱作业优化问题两部分并分别建立动态优化模型. 针对场桥作业路径优化问题, 提出一种多项式时间的精确算法并给以证明; 针对贝内翻箱作业优化问题, 设计一种基于MSA的双层启发式算法进行求解. 一系列数值实验的结果显示了所提出优化模型及算法的有效性和鲁棒性.

     

Simplified algorithms for rate-distortion optimization in high efficiency video coding

HEVC is the latest coding standard to improve the coding efficiency by a factor of two over the previous H.264/AVC standard at the cost of the increased complexity of computation rate-distortion optimization (RDO) is one of the computationally demanding operations in HEVC and makes it difficult to process the HEVC compression in real time with a reasonable computing power. This paper aims to present various simplified RDO algorithms with the evaluation of their RD performance and computational complexity. The algorithms for the simplified estimation of the sum of squared error (SSE) and context-adaptive binary arithmetic coding (CABAC) proposed for H.264/AVC are reviewed and then they are applied to the simplification of HEVC RDO. By modifying the previous algorithm for H.264/AVC, a new simplified RDO algorithm is proposed for modifying the previous algorithm for H.264/AVC to be optimized for the hierarchical coding structure of HEVC. Further simplification is attempted to avoid the transforms operations in RDO. The effectiveness of the existing H.264/AVC algorithms as well as the proposed algorithms targeted for HEVC is evaluated and the trade-off relationship between the RD performance and computational complexity is presented for various simplification algorithms. Experimental results show that reasonable combinations of RDO algorithms reduce the computation by 80–85% at the sacrifice of the BD-BR by 3.46–5.93% for low-delay configuration.     

一种基于离散Morse理论的优化模型及其应用

根据Forman 的离散Morse 理论的特点, 提出一种基于离散Morse 理论的优化模型. 该模型在3 维及以上空间点构建离散Morse 函数进行最优化, 得到了问题的最优解或近似最优解. 同时, 证明了所构建的函数确实是复形上的离散Morse 函数. 利用4 个典型的测试函数进行仿真实验, 结果表明了该模型的有效性, 且该模型尤其适用于解决大数据量的优化问题. 从聚类的过程即目标函数的优化过程这一角度考虑, 尝试将优化模型应用于聚类分析. 仿真实验结果表明, 所提出的算法能较好地划分数据点重叠区域的聚类形状, 验证了所提出算法的可行性和有效性.     

Multi-objective list scheduling of workflow applications in distributed computing infrastructures

Executing large-scale applications in distributed computing infrastructures (DCI), for example modern Cloud environments, involves optimization of several conflicting objectives such as makespan, reliability, energy, or economic cost. Despite this trend, scheduling in heterogeneous DCIs has been traditionally approached as a single or bi-criteria optimization problem. In this paper, we propose a generic multi-objective optimization framework supported by a list scheduling heuristic for scientific workflows in heterogeneous DCIs. The algorithm approximates the optimal solution by considering user-specified constraints on objectives in a dual strategy: maximizing the distance to the user’s constraints for dominant solutions and minimizing it otherwise. We instantiate the framework and algorithm for a four-objective case study comprising makespan, economic cost, energy consumption, and reliability as optimization goals. We implemented our method as part of the ASKALON environment (Fahringer et al., 2007) for Grid and Cloud computing and demonstrate through extensive real and synthetic simulation experiments that our algorithm outperforms related bi-criteria heuristics while meeting the user constraints most of the time.     

基于环境经济调度问题的空间自适应划分多目标优化

提出一种基于空间自适应划分的多目标优化算法. 为了增强种群的收敛性和多样性, 多维搜索空间被划分成多个网格, 网格内的粒子通过共享“引导”粒子的经验信息调整自身的速度和位置, 并引入年龄观测器实时记录引导粒子对Pareto 解集所做的贡献, 及时更新引导粒子, 以增强算法的全局搜索能力. 对多目标测试函数以及环境经济调度问题进行了仿真实验, 实验结果表明, 所提出算法能对解空间进行更加全面、充分的探索, 快速找到一组分布具有较好的逼近性、宽广性和均匀性的最优解集合.

     

Analytical moment based approximation for robust design optimization

The role of robust design optimization (RDO) has been eminent, ascertaining optimal configuration of engineering systems in the presence of uncertainties. However, computational aspect of RDO can often get tediously intensive in dealing with large scale systems. To address this issue, hybrid polynomial correlated function expansion (H-PCFE) based RDO framework has been developed for solving computationally expensive problems. H-PCFE performs as a bi-level approximation tool, handling the global model behavior and local functional variation. Analytical formula for the mean and standard deviation of the responses have been proposed, which reduces significant level of computations as no further simulations are required for evaluating the statistical moments within the optimization routine. Implementation of the proposed approaches have been demonstrated with two benchmark examples and two practical engineering problems. The performance of H-PCFE and its analytical version have been assessed by comparison with direct Monte Carlo simulation (MCS). Comparison with popular state-of-the-art techniques has also been presented. Excellent results in terms of accuracy and computational effort obtained makes the proposed methodology potential for further large scale industrial applications.     

Countering the negative search bias of ant colony optimization in subset selection problems

In their quest to find a good solution to a given optimization problem, metaheuristic search algorithms intend to explore the search space in a useful and efficient manner. Starting from an initial state or solution(s), they are supposed to evolve towards high-quality solutions. For some types of genetic algorithms (GAs), it has been shown that the population of chromosomes can converge to very bad solutions, even for trivial problems. These so-called deceptive effects have been studied intensively in the field of GAs and several solutions to these problems have been proposed. Recently, similar problems have been noticed for ant colony optimization (ACO) as well. As for GAs, ACO's search can get biased towards low-quality regions in the search space, probably resulting in bad solutions. Some methods have been proposed to investigate the presence and strength of this negative bias in ACO. We present a framework that is capable of eliminating the negative bias in subset selection problems. The basic Ant System algorithm is modified to make it more robust to the presence of negative bias. A profound simulation study indicates that the modified Ant System outperforms the original version in problems that are susceptible to bias. Additionally, the proposed methodology is incorporated in the Max–Min AS and applied to a real-life subset selection problem.     

A hybrid cuckoo search and genetic algorithm for reliability–redundancy allocation problems

Solving reliability and redundancy allocation problems via meta-heuristic algorithms has attracted increasing attention in recent years. In this study, a recently developed meta-heuristic optimization algorithm cuckoo search (CS) is hybridized with well-known genetic algorithm (GA) called CS–GA is proposed to solve the reliability and redundancy allocation problem. By embedding the genetic operators in standard CS, the balance between the exploration and exploitation ability further improved and more search space are observed during the algorithms’ performance. The computational results carried out on four classical reliability–redundancy allocation problems taken from the literature confirm the validity of the proposed algorithm. Experimental results are presented and compared with the best known solutions. The comparison results with other evolutionary optimization methods demonstrate that the proposed CS–GA algorithm proves to be extremely effective and efficient at locating optimal solutions.     

Evolutionary algorithms,simulated annealing and tabu search: a comparative study

Evolutionary algorithms, simulated annealing (SA), and tabu search (TS) are general iterative algorithms for combinatorial optimization. The term evolutionary algorithm is used to refer to any probabilistic algorithm whose design is inspired by evolutionary mechanisms found in biological species. Most widely known algorithms of this category are genetic algorithms (GA). GA, SA, and TS have been found to be very effective and robust in solving numerous problems from a wide range of application domains. Furthermore, they are even suitable for ill-posed problems where some of the parameters are not known before hand. These properties are lacking in all traditional optimization techniques. In this paper we perform a comparative study among GA, SA, and TS. These algorithms have many similarities, but they also possess distinctive features, mainly in their strategies for searching the solution state space. The three heuristics are applied on the same optimization problem and compared with respect to (1) quality of the best solution identified by each heuristic, (2) progress of the search from initial solution(s) until stopping criteria are met, (3) the progress of the cost of the best solution as a function of time (iteration count), and (4) the number of solutions found at successive intervals of the cost function. The benchmark problem used is the floorplanning of very large scale integrated (VLSI) circuits. This is a hard multi-criteria optimization problem. Fuzzy logic is used to combine all objective criteria into a single fuzzy evaluation (cost) function, which is then used to rate competing solutions.     

改进差分进化算法在可靠性冗余分配问题中的应用

提出一种改进差分进化算法(IDE),以解决系统可靠性冗余分配问题.在罚函数法的基础上,对约束处理方法进行改进. 新约束处理方法在搜索过程中不需要在每一步都计算惩罚函数值,加快了寻优速度.具有良好的通用性,可以引入到其他智能优化算法中.将改进的算法用于求解4类典型的系统可靠性冗余分配问题,实验结果表明了所提出的改进算法具有很好的寻优精度和收敛速度.     

Enhancing CLP branch and bound techniques for scheduling problems

In this paper, we propose a constraint logic programming (CLP) approach to the solution of a job shop scheduling problem in the field of production planning in orthopaedic hospital departments. A pure CLP on finite domain (CLP(FD)) approach to the problem has been developed, leading to disappointing results. In fact, although CLP(FD) has been recognized as a suitable tool for solving combinatorial problems, it presents some drawbacks for optimization problems. The main reason concerns the fact that CLP(FD) solvers do not effectively handle the objective function and cost‐based reasoning through the simple branch and bound scheme they embed. Therefore, we have proposed an improvement of the standard CLP branch and bound algorithm by exploiting some well‐known operations research results. The branch and bound we integrate in a CLP environment is based on the optimal solution of a relaxation of the original problem. In particular, the relaxation used for the job shop scheduling problem considered is the well‐known shifted bottleneck procedure considering single machine problems. The idea is to decompose the original problem into subproblems and solve each of them independently. Clearly, the solutions of each subproblem may violate constraints among different subproblems which are not taken into account. However, these solutions can be exploited in order to improve the pruning of the search space and to guide the search by defining cost‐based heuristics. The resulting algorithm achieves a significant improvement with respect to the pure CLP(FD) approach that enables the solution of problems which are one order of magnitude greater than those solved by a pure CLP(FD) algorithm. In addition, the resulting code is less dependent on the input data configuration. Copyright © 2001 John Wiley & Sons, Ltd.     

Improved rate-distortion optimized video coding using non-integer bit estimation and multiple Lambda search

Many modern video encoders use the Lagrangian rate-distortion optimization (RDO) algorithm for mode decisions during the compression procedure. For each encoding stage, this approach involves minimizing a cost, which is a function of rate, distortion and a multiplier called Lambda. This paper proposes to improve the RDO process by applying two modifications. The first modification is to increase the accuracy of rate estimation, which is achieved by computing a non-integer number of bits for arithmetic coding of the syntax elements. This leads to a more accurate cost computation and therefore a better mode decision. The second modification is to search and adjust the value of Lambda based on the characteristics of each coding stage. For the encoder used, this paper proposes to search multiple values of Lambda for the intra-4×4mode decision. Moreover, a simple shift in Lambda value is proposed for motion estimation. Each of these modifications offers a certain gain in RDO performance, and, when all are combined, an average bit-rate saving of up to 7.0% can be achieved for the H.264/AVC codec while the same concept is applicable to the H.265/HEVC codec as well. The extra added complexity is contained to a certain level, and is also adjustable according to the processing resources available.     

A framework for the application of metaheuristics to?tasks-to-processors assignation problems

The optimization of the execution time of a parallel algorithm can be achieved through the use of an analytical cost model function representing the running time. Typically the cost function includes a set of parameters that model the behavior of the system and the algorithm. In order to reach an optimal execution, some of these parameters must be fitted according to the input problem and to the target architecture. An optimization problem can be stated where the modeled execution time for the algorithm is used to estimate the parameters. Due to the large number of variable parameters in the model, analytical minimization techniques are discarded. Exhaustive search techniques can be used to solve the optimization problem, but when the number of parameters or the size of the computational system increases, the method is impracticable due to time restrictions. The use of approximation methods to guide the search is also an alternative. However, the dependence on the algorithm modeled and the bad quality of the solutions as a result of the presence of many local optima values in the objective functions are also drawbacks to these techniques. The problem becomes particularly difficult in complex systems hosting a large number of heterogeneous processors solving non-trivial scientific applications. The use of metaheuristics allows for the development of valid approaches to solve general problems with a large number of parameters. A well-known advantage of metaheuristic methods is the ability to obtain high-quality solutions at low running times while maintaining generality. We propose combining the parameterized analytical cost model function and metaheuristic minimization methods, which contributes to a novel real alternative to minimize the parallel execution time in complex systems. The success of the proposed approach is shown with two different algorithmic schemes on parallel heterogeneous systems. Furthermore, the development of a general framework allows us to easily develop and experiment with different metaheuristics to adjust them to particular problems.     

基于径向空间划分的昂贵多目标进化算法

为了解决难以建立精确数学模型或者真实评估实验成本高昂的多目标优化问题,提出了一种基于径向空间划分的昂贵多目标进化算法.首先算法使用高斯回归作为代理模型逼近目标函数;然后将目标空间的个体投影到径向空间,结合目标空间和径向空间信息保留对种群贡献更高的个体;之后由径向空间中个体的位置分布决定下一步应该选择哪些个体进行真实评估;最后,采用一种双档案管理策略维护代理模型的质量.数值实验和现实问题上的结果表明,与5种先进算法相比,该算法在解决昂贵多目标优化问题时能够提供更高质量的解.     

Evolutionary Minimum Cost Trajectory Planning for Industrial Robots

A general method for computing minimum cost trajectory planning for industrial robot manipulators is presented. The aim is minimization of a cost function with constraints namely joint positions, velocities, jerks and torques by considering dynamic equations of motion. A clamped cubic spline curve is used to represent the trajectory. This is a non-linear constrained optimization problem with five objective functions, 30 constraints and 144 variables. The cost function is a weighted balance of transfer time, mean average of actuators efforts and power, singularity avoidance, joint jerks and joint accelerations. The problem is solved by two evolutionary techniques such as Elitist Non-dominated Sorting Genetic Algorithm (NSGA-II) and Differential Evolution (DE). Numerical applications for a six link robotic manipulator – STANFORD robot (pick and place operation) and a two link planar manipulator (motion in the presence of obstacles) are illustrated. The results obtained from the Proposed techniques (NSGA-II and DE) are compared for different values of weighting coefficients. The influences of the algorithm parameters and weight factors on algorithm performance are analyzed. The DE algorithm converges quickly than NSGA-II. Also DE algorithm gives better results than NSGA-II in majority of cases. A comprehensive user-friendly general-purpose software package has been developed using VC++ to obtain the optimal solutions of any complex problem using DE algorithm.     

一个基于填充函数变换的对称TSP问题的局部搜索算法

该文提出了求对称TSP问题近优解的填充函数算法。首先，在用局部搜索算法求得对称TSP问题的一个局部极小解后，对该问题作填充函数变换得到一新的组合优化问题，新问题的局部极小解和最优解分别是原问题的局部极小解和最优解，而且在对称TSP问题的目标函数值大于或等于其目标函数当前极小值的区域中，新问题只有一个已知的局部极小解。随后用局部搜索算法求新问题的一个局部极小解，它或者是已知的局部极小解，或者是对称TSP问题的更好的局部极小解。对多个标准实例的计算试验表明，该文所构造的算法优于直接求解对称TSP问题的局部搜索算法。     

一个约束离散优化问题的粒子群算法研究

针对一个离散变量齿轮系优化设计问题搜索空间大、可行域狭小的特点,基于粒子群算法提出了新的约束与离散变量处理策略。另外,修改粒子群算法的速度更新公式以减少算法参数数目。与有关文献相比,所采用的算法应用于该优化问题时,不但发现可行解的成功率高,而且获得了更好的“最优”可行解和平均结果。与此同时,该算法不要求对该问题进行任何转化,也不依赖于人机交互。结果表明,该算法简单、易行、有效,对于类似优化设计问题的求解很有参考价值。