A Combinatorial Optimized Knapsack Linear Space for Information Retrieval

Key information extraction can reduce the dimensional effects while evaluating the correct preferences of users during semantic data analysis. Currently, the classifiers are used to maximize the performance of web-page recommendation in terms of precision and satisfaction. The recent method disambiguates contextual sentiment using conceptual prediction with robustness, however the conceptual prediction method is not able to yield the optimal solution. Context-dependent terms are primarily evaluated by constructing linear space of context features, presuming that if the terms come together in certain consumer-related reviews, they are semantically reliant. Moreover, the more frequently they coexist, the greater the semantic dependency is. However, the influence of the terms that coexist with each other can be part of the frequency of the terms of their semantic dependence, as they are non-integrative and their individual meaning cannot be derived. In this work, we consider the strength of a term and the influence of a term as a combinatorial optimization, called Combinatorial Optimized Linear Space Knapsack for Information Retrieval (COLSK-IR). The COLSK-IR is considered as a knapsack problem with the total weight being the “term influence” or “influence of term” and the total value being the “term frequency” or “frequency of term” for semantic data analysis. The method, by which the term influence and the term frequency are considered to identify the optimal solutions, is called combinatorial optimizations. Thus, we choose the knapsack for performing an integer programming problem and perform multiple experiments using the linear space through combinatorial optimization to identify the possible optimum solutions. It is evident from our experimental results that the COLSK-IR provides better results than previous methods to detect strongly dependent snippets with minimum ambiguity that are related to inter-sentential context during semantic data analysis.     

背包问题的混合粒子群优化算法

经典的粒子群是一个有效的寻找连续函数极值的方法，结合遗传算法的思想提出的混合粒子群算法来解决背包问题，经过比较测试，6种混合粒子群算法的效果都比较好，特别交叉策略A和变异策略C的混合粒子群算法是最好的且简单有效的算法，并成功地运用在投资问题中。对于目前还没有好的解法的组合优化问题，很容易地修改此算法就可解决     

ARCHITECTURAL OPTIMIZATION-A REVIEW

The introduction of numerical methodologies to Architecture is very recent. However, the last ten years has seen the application of optimization techniques to many of the problems of Architecture previously considered to be non-numeric: this paper reviews the panoply of these applications. The optimization problem which appears to have attracted the most attention is that of the layout of spaces or planning. Various techniques have been applied to this combinatorial problem but as yet there appears to be no algorithm available which produces a global optimum. The development of sites in cities has also been treated as an optimization problem with considerable success; various search methods as well as dynamic programming have been applied here. The methods used to control the internal physical environment of buildings (building services) have been examined systematically from an optimizing viewpoint. Detailed services such as aii-conditioning and vertical transportation have been treated. Materials selection has been treated as a multi-factor optimization problem. Dynamic programming has found considerable use in architectural optimization in site development, vertical transportation, house planning and campus planning. Finally, the development of design systems which use optimizing techniques is discussed. An extensive list of references follows the conclusions which summarize some of the difficulties of using optimization in architecture     

Reliability optimization using multiobjective ant colony system approaches

The multiobjective ant colony system (ACS) meta-heuristic has been developed to provide solutions for the reliability optimization problem of series-parallel systems. This type of problems involves selection of components with multiple choices and redundancy levels that produce maximum benefits, and is subject to the cost and weight constraints at the system level. These are very common and realistic problems encountered in conceptual design of many engineering systems. It is becoming increasingly important to develop efficient solutions to these problems because many mechanical and electrical systems are becoming more complex, even as development schedules get shorter and reliability requirements become very stringent. The multiobjective ACS algorithm offers distinct advantages to these problems compared with alternative optimization methods, and can be applied to a more diverse problem domain with respect to the type or size of the problems. Through the combination of probabilistic search, multiobjective formulation of local moves and the dynamic penalty method, the multiobjective ACSRAP, allows us to obtain an optimal design solution very frequently and more quickly than with some other heuristic approaches. The proposed algorithm was successfully applied to an engineering design problem of gearbox with multiple stages.     

A surrogate assisted parallel multiobjective evolutionary algorithm for robust engineering design

A number of multi-objective evolutionary algorithms have been proposed in recent years and many of them have been used to solve engineering design optimization problems. However, designs need to be robust for real-life implementation, i.e. performance should not degrade substantially under expected variations in the variable values or operating conditions. Solutions of constrained robust design optimization problems should not be too close to the constraint boundaries so that they remain feasible under expected variations. A robust design optimization problem is far more computationally expensive than a design optimization problem as neighbourhood assessments of every solution are required to compute the performance variance and to ensure neighbourhood feasibility. A framework for robust design optimization using a surrogate model for neighbourhood assessments is introduced in this article. The robust design optimization problem is modelled as a multi-objective optimization problem with the aim of simultaneously maximizing performance and minimizing performance variance. A modified constraint-handling scheme is implemented to deal with neighbourhood feasibility. A radial basis function (RBF) network is used as a surrogate model and the accuracy of this model is maintained via periodic retraining. In addition to using surrogates to reduce computational time, the algorithm has been implemented on multiple processors using a master–slave topology. The preliminary results of two constrained robust design optimization problems indicate that substantial savings in the actual number of function evaluations are possible while maintaining an acceptable level of solution quality.     

Multi-objective planning of a multiple geostationary spacecraft refuelling mission

The multi-objective optimization of multiple geostationary spacecraft refuelling is investigated in this article. A servicing spacecraft (SSc) and a propellant depot (PD), both parked initially in geostationary Earth orbit (GEO), are utilized to refuel multiple GEO targets of known propellant demand. The capacitated SSc is expected to rendezvous with fuel-deficient GEO targets or the PD for the purpose of refuelling or getting refuelled. The multiple geostationary spacecraft refuelling problem is treated as a multi-variable combinatorial optimization problem with the principal objective of minimizing the propellant consumption and the mission duration. A two-level optimization model is built, and the design variables are the refuelling order X, the refuelling time T and the binary decision variable S. The non-dominated sorting genetic algorithm is employed to solve the up-level optimization problem. For the low-level optimization, an exact algorithm is proposed. Finally, numerical simulations are presented to illustrate the effectiveness and validity of the proposed approach.     

A robust optimization methodology for preliminary aircraft design

This article focuses on a robust optimization of an aircraft preliminary design under operational constraints. According to engineers' know-how, the aircraft preliminary design problem can be modelled as an uncertain optimization problem whose objective (the cost or the fuel consumption) is almost affine, and whose constraints are convex. It is shown that this uncertain optimization problem can be approximated in a conservative manner by an uncertain linear optimization program, which enables the use of the techniques of robust linear programming of Ben-Tal, El Ghaoui, and Nemirovski [Robust Optimization, Princeton University Press, 2009]. This methodology is then applied to two real cases of aircraft design and numerical results are presented.     

A multilevel genetic algorithm for the optimum design of structural control systems

A multilevel genetic algorithm (MLGA) is proposed in this paper for solving the kind of optimization problems which are multilevel structures in nature and have features of mixed‐discrete design variables, multi‐modal and non‐continuous objective functions, etc. Firstly, the formulation of the mixed‐discrete multilevel optimization problems is presented. Secondly, the architecture and implementation of MLGA are described. Thirdly, the algorithm is applied to two multilevel optimization problems. The first one is a three‐level optimization problem in which the optimization of the number of actuators, the positions of actuators and the control parameters are considered in different levels. An actively controlled tall building subjected to strong wind action is considered to investigate the effectiveness of the proposed algorithm. The second application considers a combinatorial optimization problem in which the number and configuration of actuators are optimized simultaneously, an actively controlled building under earthquake excitations is adopted for this case study. Finally, some results and discussions about the application of the proposed algorithm are presented. Copyright © 2002 John Wiley & Sons, Ltd.     

面向批量订单包装的物流箱规格优化问题研究

目的 针对面向仓储物流环境下多型号多批量产品的订单包装问题,提出一种预制物流箱规格优化模型及算法。方法 对产品订单建立订单分包规则,确定分包方案,以订单包装材料总成本最小为优化目标建立物流箱规格优化模型。针对该模型提出一种改进模拟退火算法,通过贪婪策略求解最优分包方案,降低模型计算复杂度,设计一种新型解更新算子,以提高算法寻优能力,设计一种自适应步长策略,以平衡算法前期全局搜索与后期局部搜索的能力。结果 通过实例证明,文中提出的算法相较于其他算法,具有更强的求解能力,与实例企业仓储包装现状相比,同批订单降低了17%的包装材料成本。结论 该方法可用于解决产品种类多、尺寸差异大、动态更新等应用场景下的系列运输包装纸箱规格优化问题,为企业物流运输管理提供了一种有效的包装优化思路和解决方法。     

钢管生产的合同组批优化算法

钢管生产的合同组批优化问题是一个典型的组合优化问题,对钢管生产企业的计划排程、生产效率及市场反应速度等有重要影响。以生产批量、热区分段长度为优化目标,考虑了多种约束条件,提出了一套解决该问题的算法,并已运用于生产实际中,具有推广价值。     

Particle swarm-based structural optimization of laminated composite hydrokinetic turbine blades

Composite blade manufacturing for hydrokinetic turbine application is quite complex and requires extensive optimization studies in terms of material selection, number of layers, stacking sequence, ply thickness and orientation. To avoid a repetitive trial-and-error method process, hydrokinetic turbine blade structural optimization using particle swarm optimization was proposed to perform detailed composite lay-up optimization. Layer numbers, ply thickness and ply orientations were optimized using standard particle swarm optimization to minimize the weight of the composite blade while satisfying failure evaluation. To address the discrete combinatorial optimization problem of blade stacking sequence, a novel permutation discrete particle swarm optimization model was also developed to maximize the out-of-plane load-carrying capability of the composite blade. A composite blade design with significant material saving and satisfactory performance was presented. The proposed methodology offers an alternative and efficient design solution to composite structural optimization which involves complex loading and multiple discrete and combinatorial design parameters.     

A novel minimum weight formulation of topology optimization implemented with reanalysis approach

In this paper, we develop an efficient diagonal quadratic optimization formulation for minimum weight design problem subject to multiple constraints. A high-efficiency computational approach of topology optimization is implemented within the framework of approximate reanalysis. The key point of the formulation is the introduction of the reciprocal-type variables. The topology optimization seeking for minimum weight can be transformed as a sequence of quadratic program with separable and strictly positive definite Hessian matrix, thus can be solved by a sequential quadratic programming approach. A modified sensitivity filtering scheme is suggested to remove undesirable checkerboard patterns and mesh dependence. Several typical examples are provided to validate the presented approach. It is observed that the optimized structure can achieve lighter weight than those from the established method by the demonstrative numerical test. Considerable computational savings can be achieved without loss of accuracy of the final design for 3D structure. Moreover, the effects of multiple constraints and upper bound of the allowable compliance upon the optimized designs are investigated by numerical examples.     

Bi-parental product algorithm for coded waveform design in radar

The problem of obtaining long sequences with finite alphabet and peaky aperiodic auto-correlation is important in the context of radar, sonar and system identification and is called the coded waveform design problem, or simply the signal design problem in this limited context. It is good to remember that there are other signal design problems in coding theory and digital communication. It is viewed as a problem of optimization. An algorithm based on two operational ideas is developed. From the earlier experience of using the eugenic algorithm for the problem of waveform design, it was realised that rather than random but multiple mutations, all the first-order mutations should be examined to pick up the best one. This is called Hamming scan, which has the advantage of being locally complete, rather than random. The conventional genetic algorithm for non-local optimization leaves out the anabolic role of chemistry of allowing quick growth of complexity. Here, the Hamming scan is made to operate on the Kronecker or Chinese product of two sequences with best-known discrimination values, so that one can go to large lengths and yet get good results in affordable time. The details of the ternary pulse compression sequences obtained are given. They suggest the superiority of the ternary sequences.     

Availability allocation to repairable systems with genetic algorithms: a multi-objective formulation

This paper describes a methodology based on genetic algorithms (GA) and experiments plan to optimize the availability and the cost of reparable parallel-series systems. It is a NP-hard problem of multi-objective combinatorial optimization, modeled with continuous and discrete variables. By using the weighting technique, the problem is transformed into a single-objective optimization problem whose constraints are then relaxed by the exterior penalty technique. We then propose a search of solution through GA, whose parameters are adjusted using experiments plan technique. A numerical example is used to assess the method.     

Tailoring materials with prescribed elastic properties

This paper describes a method to design the periodic microstructure of a material to obtain prescribed constitutive properties. The microstructure is modelled as a truss or thin frame structure in 2 and 3 dimensions. The problem of finding the simplest possible microstructure with the prescribed elastic properties can be called an inverse homogenization problem, and is formulated as an optimization problem of finding a microstructure with the lowest possible weight which fulfils the specified behavioral requirements. A full ground structure known from topology optimization of trusses is used as starting guess for the optimization algorithm. This implies that the optimal microstructure of a base cell is found from a truss or frame structure with 120 possible members in the 2-dimensional case and 2016 possible members in the 3-dimensional case. The material parameters are found by a numerical homogenization method, using Finite-Elements to model the representative base cell, and the optimization problem is solved by an optimality criteria method.

Numerical examples in two and three dimensions show that it is possible to design materials with many different properties using base cells modelled as truss or frame works. Hereunder is shown that it is possible to tailor extreme materials, such as isotropic materials with Poisson's ratio close to − 1, 0 and 0.5, by the proposed method. Some of the proposed materials have been tested as macro models which demonstrate the expected behaviour.     

基于骨架提取的拓扑优化最小尺寸精确控制

受到可制造性的约束,拓扑优化技术目前多用于结构的概念设计,因此,研究直接面向加工制造的拓扑优化方法很有必要。该文基于启发式BESO(Bi-directional Evolutionary Structural Optimization)算法,提出了一种高效的可精确控制结构最小尺寸的拓扑优化方法。通过灵敏度插值,细化边界单元,改进BESO算法,解决边界不光滑问题;采用拓扑细化方法,提取拓扑结构的骨架构型;以此为基础,判定结构中不满足最小尺寸约束的部位,基于改进的BESO算法,实现拓扑优化结构的最小尺寸精确控制;此外,在优化过程中,通过松弛施加最小尺寸约束的方法,有效避免优化早熟问题。数值算例表明了该拓扑优化方法的有效性。     

基于蚁群算法的资源均衡优化决策及其MATLAB实现

资源均衡问题已被证明属于组合优化中的NP-hard问题,随着网络计划的复杂化,传统的数学规划法和启发式算法已很难解决该问题。本文以各种资源标准差的加权之和作为衡量资源均衡的评价指标,建立了资源均衡优化决策的数学模型,其次,自行设计蚁群算法步骤,利用Matlab编程进行实现,将蚂蚁随机分布在可行域中,蚂蚁根据转移概率进行全局搜索或局部搜索,经迭代求解资源平衡的全局最优和对应的各工序的开始工作时间,最后使用单资源均衡和多资源均衡两个算例对算法进行了测试,验证了该算法的有效性。     

OPTIMAL STRUCTURAL DESIGN BY ANT COLONY OPTIMIZATION

Ant colony optimization (ACO) is a relatively new heuristic combinatorial optimization algorithm in which the search process is a stochastic procedure that incorporates positive feedback of accumulated information. The positive feedback (;i.e., autocatalysis) facility is a feature of ACO which gives an emergent search procedure such that the (common) problem of algorithm termination at local optima may be avoided and search for a global optimum is possible.

The ACO algorithm is motivated by analogy with natural phenomena, in particular, the ability of a colony of ants to ‘optimize’ their collective endeavours. In this paper the biological background for ACO is explained and its computational implementation is presented in a structural design context. The particular implementation of ACO makes use of a tabu search (TS) local improvement phase to give a computationally enhanced algorithm (ACOTS).

In this paper ACOTS is applied to the optimal structural design, in terms of weight minimization, of a 25-bar space truss. The design variables are the cross-sectional areas of the bars, which take discrete values. Numerical investigation of the 25-bar space truss gave the best (i.e., lowest to-date) minimum weight value. This example provides evidence that ACOTS is a useful and technically viable optimization technique for discrete-variable optimal structural design.     

AN OPTIMIZATION METHOD FOR THE DESIGN OF LARGE,HIGHLY CONSTRAINED COMPLEX SYSTEMS

A practical and efficient optimization method for the rational design of large, highly constrained complex systems is presented. The design of such systems is iterative and requires the repeated formulation and solution of an analysis model, followed by the formulation and solution of a redesign model. The latter constitutes an optimization problem. The versatility and efficiency of the method for solving the optimization problem is of fundamental importance for a successful implementation of any rational design procedure.

In this paper, a method is presented for solving optimization problems formulated in terms of continuous design variables. The objective function may be linear or non-linear, single or multiple. The constraints may be any mix of linear or non-linear functions, and these may be any mix of inequalities and equalities. These features permit the solution of a wide spectrum of optimization problems, ranging from the standard linear and non-linear problems to a non-linear problem with multiple objective functions (goal programming). The algorithm for implementing the method is presented in sufficient detail so that a computer program, in any computing language, can be written.     

Optimal Building Frame Column Design Based on the Genetic Algorithm

Building structure is like the skeleton of the building, it bears the effects of various forces and forms a supporting system, which is the material basis on which the building depends. Hence building structure design is a vital part in architecture design, architects often explore novel applications of their technologies for building structure innovation. However, such searches relied on experiences, expertise or gut feeling. In this paper, a new design method for the optimal building frame column design based on the genetic algorithm is proposed. First of all, in order to construct the optimal model of the building frame column, building units are divided into three categories in general: building bottom, main building and building roof. Secondly, the genetic algorithm is introduced to optimize the building frame column. In the meantime, a PGA-Skeleton based concurrent genetic algorithm design plan is proposed to improve the optimization efficiency of the genetic algorithm. Finally, effectiveness of the mentioned algorithm is verified through the simulation experiment.