矿物质粉体对砂浆及混凝土Cl-渗透性的影响

研究了不同水胶比、不同矿物质粉体掺量的砂浆和混凝土，经标准养护至56天、90天时的导电量。在相同水胶比和相同矿物质粉体掺量下，混凝土的导电量远低于砂浆的导电量。含矿物质粉体的砂浆及混凝土的导电量均低于基准砂浆及混凝土的导电量。导电量随水胶比的降低而降低，也随龄期的增长而降低。     

A Decision Support System for Solving Multi‐Objective Redundancy Allocation Problems

The Redundancy Allocation Problem (RAP) is a reliability optimization problem in designing series‐parallel systems. The reliability optimization process is intended to select multiple components with appropriate levels of redundancy by maximizing the system reliability under some predefined constraints. Several methods have been proposed to solve the RAPs. However, most of these methods often treat RAP as a single objective problem of maximizing the system reliability (or minimizing the system design cost). We propose a Decision Support System for solving Multi‐Objective RAPs. Initially, we use the Technique for Order Performance by Similarity to Ideal Solution method to reduce the multiple objective dimensions of the problem. We then propose an efficient ε‐constraint method to generate non‐dominated solutions on the Pareto front. Finally, we use a Data Envelopment Analysis model to prune the non‐dominated solutions. A benchmark case is presented to assess the performance of the proposed system, demonstrate the applicability of the proposed framework, and exhibit the efficacy of the procedures and algorithms. Copyright © 2013 John Wiley & Sons, Ltd.     

Evolutionary Algorithm Integrating Stress Heuristics for Truss Optimization

The optimal truss design using problem-oriented evolutionary algorithm is presented in the paper. The minimum weight structures subjected to stress and displacement constraints are searched. The discrete design variables are areas of members, selected from catalogues of available sections. The integration of the problem specific knowledge into the optimization procedure is proposed. The heuristic rules based on the concept of fully stressed design are introduced through special genetic operators, which use the information concerning the stress distribution of structural members. Moreover, approximated solutions obtained by deterministic, sequential discrete optimization methods are inserted into the initial population. The obtained hybrid evolutionary algorithm is specialized for truss design. Benchmark problems are calculated in numerical examples. The knowledge about the problem integrated into the evolutionary algorithm can enhance considerably the effectiveness of the approach and improve significantly the convergence rate and the quality of the results. The advantages and drawbacks of the proposed method are discussed.     

Parallel Optimization of Program Instructions Using Genetic Algorithms

This paper describes an efficient solution to parallelize software program instructions, regardless of the programming language in which they are written. We solve the problem of the optimal distribution of a set of instructions on available processors. We propose a genetic algorithm to parallelize computations, using evolution to search the solution space. The stages of our proposed genetic algorithm are: The choice of the initial population and its representation in chromosomes, the crossover, and the mutation operations customized to the problem being dealt with. In this paper, genetic algorithms are applied to the entire search space of the parallelization of the program instructions problem. This problem is NP-complete, so there are no polynomial algorithms that can scan the solution space and solve the problem. The genetic algorithm-based method is general and it is simple and efficient to implement because it can be scaled to a larger or smaller number of instructions that must be parallelized. The parallelization technique proposed in this paper was developed in the C# programming language, and our results confirm the effectiveness of our parallelization method. Experimental results obtained and presented for different working scenarios confirm the theoretical results, and they provide insight on how to improve the exploration of a search space that is too large to be searched exhaustively.     

基于空间收缩的并行演化算法

提出了一种基于空间收缩的求解MINLP问题的新算法。算法应用了快速有效的不完全演化搜索较优解的分布信息，通过分布信息定位最优解的可能分布，再由精英个体信息决定下次搜索空间。仿真结果表明该算法在搜索效率、应用范围、解的精确性和鲁棒性上都优于其他现存演化算法。     

Quantum-Inspired Evolutionary Algorithm for Topology Optimization of Modular Cabled-Trusses

This article proposes an alternative optimization framework applied to topology optimization of modular lightweight cabled-truss structures. These structures are described as a system of intrinsically positioned cables and triangular bar formations jointed at their ends by hinged connections to form a rigid framework. The optimized topologies are determined through a stochastic discrete optimization procedure that uses ground structure approach, nonlinear finite element analysis, and quantum-inspired evolutionary algorithms. The optimization searches for optimal mass reduction with minimal losses in stiffness, such relation, is expressed by the stiffness-to-mass ratio parameter. Nonlinear finite element analysis is used to evaluate the static structural response. In order to decrease computation time, kinematically instable and structurally invalid individuals are filtered before evaluation. Modular design approach is taken into consideration to reduce the number of design variables and increase the productibility of cabled-trusses. Symmetric structural response is desired since in several mechanical applications forces can assume different directions during the working cycle. A modular ground structure with 300 elements is optimized, and optimal truss and cabled-truss topologies are compared. Complementary analyses comprise the investigation of the structural performance under different number of modules and slenderness ratios. The results indicate that the proposed optimization framework leads to optimized structures. In addition, it was observed that cabled trusses presented significant improvements in structural performance when compared with trusses.     

一种混合优化策略在工步排序中的应用

结合创成式ＣＡＰＰ系统中工步优化问题，介绍了由遗传算法（ＧｅｎｅｔｉｃＡｌｇｏｒｉｔｈｍｓ，ＧＡ）和模拟退火算法（ＳｉｍｕｌａｔｅｄＡｎｎｅａｌｉｎｇＡｌｇｏｒｉｔｈｍｓ，ＳＡ）构成的混合寻优策略。最后以一箱体的工艺规划过程为例，将基于混合寻优策略的工步排序融入以加工中心为主要加工设备的ＣＡＰＰ工艺决策过程。     

Evolutionary algorithms for solving the automatic cell planning problem: a survey

Automatic cell planning (ACP) is an optimization problem from the mobile telecommunications domain that addresses finding the location of the network antennae as well as their parameter settings in order to satisfy several cellular operator requirements. Due to its NP-hard complexity, evolutionary techniques have become popular for solving ACP instances. This article presents a survey of evolutionary algorithms (EAs) engineered for addressing ACP problems, analysing both the features of the considered ACP problem and the main aspects of the EAs used to solve them. The survey provides an up-to-date overview that is not limited to any particular kind of evolutionary approach, and comprises advanced algorithmic enhancements like hybridization and parallelization. The article ends by addressing some important issues and open questions that can be the subject of future research.     

Regulating Crown and Flatness During Hot Rolling: A Multiobjective Optimization Study Using Genetic Algorithms

A genetic algorithms-based multioptimization study has been carried out for the hot rolling practice in an integrated steel plant. The aim is to identify the parameter settings and rolling schedules that would result in the optimum values of crown and flatness--two major parameters related to the geometric tolerances in the rolled sheet. Two objective functions and some appropriate constraints have been formulated for this purpose, and two different evolutionary algorithms are tried out on them. The optimized results are presented in the forms of Pareto fronts and discussed in the context of the actual process.     

Feasibility-Guided Constraint-Handling Techniques for Engineering Optimization Problems

The particle swarm optimization (PSO) algorithm is an established nature-inspired population-based meta-heuristic that replicates the synchronizing movements of birds and fish. PSO is essentially an unconstrained algorithm and requires constraint handling techniques (CHTs) to solve constrained optimization problems (COPs). For this purpose, we integrate two CHTs, the superiority of feasibility (SF) and the violation constraint-handling (VCH), with a PSO. These CHTs distinguish feasible solutions from infeasible ones. Moreover, in SF, the selection of infeasible solutions is based on their degree of constraint violations, whereas in VCH, the number of constraint violations by an infeasible solution is of more importance. Therefore, a PSO is adapted for constrained optimization, yielding two constrained variants, denoted SF-PSO and VCH-PSO. Both SF-PSO and VCH-PSO are evaluated with respect to five engineering problems: the Himmelblau’s nonlinear optimization, the welded beam design, the spring design, the pressure vessel design, and the three-bar truss design. The simulation results show that both algorithms are consistent in terms of their solutions to these problems, including their different available versions. Comparison of the SF-PSO and the VCH-PSO with other existing algorithms on the tested problems shows that the proposed algorithms have lower computational cost in terms of the number of function evaluations used. We also report our disagreement with some unjust comparisons made by other researchers regarding the tested problems and their different variants.     

一种基于蜜蜂进化选择算子的布局遗传算法

三维矩形布局问题属于NP难问题,对于三维矩形布局问题的求解大多依赖于各种启发式算法.该文以布局物体体积递减为定序规则,结合布局物体在布局空间中的几何可行域,以吸引子法为定位规则,利用蜜蜂进化型遗传算法优化吸引子函数中的参数来求解三维矩形布局问题（BEGA）,得到新型布局遗传算法.最后对不同的算例进行了计算,并与以标准比例选择作为选择算子的传统布局遗传算法（SPGA）等对比证明了该算法的有效性.     

Evolutionary optimization for passive gamma tomography reconstruction of circular inclusions in nuclear waste vaults

In this study we investigate numerically the possibility of reconstructing the interior details of a nuclear waste vault using photon flux measurements. The passive gamma ray tomography method is employed in order to simultaneously retrieve the intensity and the location of strongly radioactive components. After reformulating the problem as an optimization problem the objective functional obtained is found to have a multi‐modal non‐smooth structure. Therefore, evolutionary algorithms are used in order to minimize the objective functional in order to determine the radioactive components. Circular inclusions are considered and detailed numerical results are presented for both exact and noisy data. Copyright © 2007 John Wiley & Sons, Ltd.     

应用蜜蜂繁殖进化型粒子群算法求解车辆路径问题

为了提高粒子群算法求解车辆路径问题时收敛速度和全局搜索能力,将蜜蜂繁殖进化机制与粒子群算法相结合,应用到CVRP问题的求解。该算法中,最优的个体作为蜂王与通过选择机制选择的雄蜂以随机概率进行交叉,增强了最优个体信息的应用能力;同时,随机产生一部分雄蜂种群,并将其与蜂王交叉增加了算法的多样性。实例分析表明该算法具有较好的全局搜索能力,验证了该算法的可行性。     

Application of Genetic Algorithms to Geometry Optimization of Microclusters: A Comparative Study of Empirical Potential Energy Functions for Silicon

Evolutionary computation techniques (in particular, genetic algorithms) have been applied to optimize the structure of microclusters. Various empirical potential energy functions have been used to describe the interactions among the atoms in the clusters. A comparative study of silicon microclusters has been performed.     

现代优化计算方法在材料最优化设计中的应用

简要介绍三种现代优化算法 ,综合利用主成分降维技术、人工神经网络技术和遗传算法技术 ,在V PTC材料介电性能和五个影响因素之间建立神经网络模型 ,然后应用遗传算法搜索最高电阻值和相应的配方。结果表明 :现代优化算法在分析合理选择的样本数据 ,总结其中的数值规律 ,进而对材料性能进行优化设计方面 ,具有重要的应用价值。     

Evolutionary Design of Nickel-Based Superalloys Using Data-Driven Genetic Algorithms and Related Strategies

Data-driven models were constructed for the mechanical properties of multi-component Ni-based superalloys, based on systematically planned, limited experimental data using a number of evolutionary approaches. Novel alloy design was carried out by optimizing two conflicting requirements of maximizing tensile stress and time-to-rupture using a genetic algorithm-based multi-objective optimization method. The procedure resulted in a number of optimized alloys having superior properties. The results were corroborated by a rigorous thermodynamic analysis and the alloys found were further classified in terms of their expected levels of hardenabilty, creep, and corrosion resistances along with the two original objectives that were optimized. A number of hitherto unknown alloys with potential superior properties in terms of all the attributes ultimately emerged through these analyses. This work is focused on providing the experimentalists with linear correlations among the design variables and between the design variables and the desired properties, non-linear correlations (qualitative) between the design variables and the desired properties, and a quantitative measure of the effect of design variables on the desired properties. Pareto-optimized predictions obtained from various data-driven approaches were screened for thermodynamic equilibrium. The results were further classified for additional properties.     

遗传算法用于电子枪发射系统的全局最优化设计

常用的优化设计方法 ,如单纯形法、Powell法等 ,易陷入局部最优解。而遗传算法是一种新兴的直接搜索最优化算法 ,它模拟达尔文遗传选择与自然进化的理论 ,根据“适者生存”和“优胜劣汰”的原则 ,借助“复制”、“交换”、“突变”等操作可以得到全局最优解。本文将遗传算法运用于电子枪发射系统的最优化设计 ,得到了使交叠点半径尽可能小的发射系统的最佳结构和相应电参量     

A Comparison of Evolutionary Algorithms for Mechanical Design Components

Two evolutionary algorithms - the genetic algorithm and the evolution strategy - are compared in respect of mechanical design problems. Mechanical design problems are real world problems, characterized by a number of inequality constraints, nonlinear equations, mixed discrete-continuous variables and the presence of interdependent discrete parameters whose values are taken from standardized tables. The selection, recombination and mutation operators, and the chosen constraint-handling method are presented for both the genetic algorithm and the evolution strategy. In order to find the best combination of operators for each algorithm which will solve mechanical design problems, a number of selection and recombination operators are compared in respect of these problems. A comparison of these two algorithms with regard to three mechanical design problems extends the results of comparisons presented in the literature for unimodal and multimodal test functions with continuous variables only, and without constraints.     

Use of the Constrained Optimization Algorithms in Some Problems of Fracture Mechanics

In this paper we consider an algorithm of constrained optimization which arises from boundary variational principles of elastodynamics for bodies with cracks and unilateral constraints on the cracks edges. Variational formulation of unilateral contact problems with friction was considered, boundary variational functionals used with boundary integral equations were obtained and algorithm for solution of the unilateral contact problem with friction was developed. Some numerical results for 3-D elastodynamic unilateral contact problem for bodies with cracks are presented.