A methodology for solid modelling in a virtual reality environment

This paper presents a methodology for solid modelling in a virtual reality (VR) environment. Solid modelling in the VR environment is precisely performed in an intuitive manner through constraint-based manipulations. A hierarchically structured and constraint-based data model is developed to support solid modelling in the VR environment. Constraint-based manipulations are realized by allowable motions for precise 3D interactions in the VR environment. A mathematical matrix is presented for representing allowable motions. A procedure-based degree-of-freedom combination method for 3D constraint solving is presented for deriving the allowable motions from constraints. A rule-based constraint recognition engine is developed for both constraint-based manipulations and implicitly incorporating constraints into the VR environment. A prototype system has been implemented to testify the feasibility of the presented methodology.     

Progressive design methodology for complex engineering systems based on multiobjective genetic algorithms and linguistic decision making

This work focuses on a design methodology that aids in design and development of complex engineering systems. This design methodology consists of simulation, optimization and decision making. Within this work a framework is presented in which modelling, multi-objective optimization and multi criteria decision making techniques are used to design an engineering system. Due to the complexity of the designed system a three-step design process is suggested. In the first step multi-objective optimization using genetic algorithm is used. In the second step a multi attribute decision making process based on linguistic variables is suggested in order to facilitate the designer to express the preferences. In the last step the fine tuning of selected few variants are performed. This methodology is named as progressive design methodology. The method is applied as a case study to design a permanent magnet brushless DC motor drive and the results are compared with experimental values.     

Simulated annealing for the design of structures with time-varying constraints

The optimal design of structural systems with conventional members or systems with conventional as well as passive or active members is presented. The optimal sizes of the conventional members of structural systems are obtained for dynamic loads. A modified simulated annealing algorithm is presented which is used to solve the optimization problem with dynamic constraints. The present algorithm differs from existing simulated annealing algorithms in two respects; first, an automatic reduction of the search range is performed, and second, a sensitivity analysis of the design variables is utilized. The present method converges to the minimum in less iterations when compared to existing simulated annealing algorithms. The algorithm is advantageous over classical methods for optimization of structural systems with constraints arising from dynamic loads. For certain initial values of the design variables, classical optimization methods either fail to converge or produce designs which are local minima; the present algorithm seems to be successful in yielding the global minimum design.     

Direct 3D Manipulation Techniques for Interactive Constraint-based Solid Modelling

This paper presents a novel constraint-based 3D manipulation approach to interactive constraint-based solid modelling. This approach employs a constraint recognition process to automatically recognise assembly relationships and geometric constraints between entities from 3D manipulation. A technique referred to as allowable motion is used to achieve accurate 3D positioning of a solid model by automatically constraining its 3D manipulation without menu interaction. A set of virtual design tools, which can be used to construct constraint-based solid models within a virtual environment, are also supported. These tools have been implemented as functional 3D objects associated with several pre-defined modelling functions to simulate physical tools such as a drilling tool and T-square. They can be directly manipulated by the user, and precisely positioned relative to other solid models through the constraint-based 3D manipulation approach. Their modelling functions can be automatically triggered, depending upon their associated constraints and the user's manipulation manner. A prototype system has been implemented to demonstrate the feasibility of these techniques for model construction and assembly operations.     

Attribute grammars in constraint-based graphics systems

A constraint-based graphics system provides a flexible, intuitive framework for describing relationships among graphical objects in applications such as document preparation, fount design and solid modelling. This paper describes two constraint-based graphics systems, micro-COSM and the IDEAL Synthesizer, and their implementation in terms of attribute grammars. Our experiences with attribute grammars suggest that they provide a powerful framework for representing constraints and extracting important semantic information such as the equations to be solved by the constraint solver. We discuss the advantages of using attribute grammars in constraint-based graphics and from our experiences make several observations about the way attribute grammars should be used.     

Constraint-based Very Large-Scale Neighborhood search

Very Large-Scale Neighborhood (VLSN) search is the idea of using neighborhoods of exponential size to find high-quality solutions to complex optimization problems efficiently. However, so far, VLSN algorithms are essentially described and implemented in terms of low-level implementation concepts, preventing code reuse and extensibility which are trademarks of constraint-programming systems. This paper aims at remedying this limitation and proposes a constraint-based VLSN (CBVLSN) framework to describe VLSNs declaratively and compositionally. Its main innovations are the concepts of cycle-consistent MoveGraphs and compositional moves which make it possible to specify an application in terms of constraints and objectives and to derive a dedicated VLSN algorithm automatically. The constraint-based VLSN framework has been prototyped in COMET and its efficiency is shown to be comparable to dedicated implementations.     

Constrained structural design optimization via a parallel augmented Lagrangian particle swarm optimization approach

This paper presents an extension to the basic particle swarm optimization approach for the solution of constrained engineering design optimization problems. The approach takes advantage of the PSO ability to find global optimum in problems with complex design spaces while directly enforcing feasibility of constraints using an augmented Lagrange multiplier method. Details in the algorithm implementation and properties are presented and the effectiveness of the approach is illustrated in different benchmark structural optimization test cases. Results show the ability of the proposed methodology to find better solutions for structural optimization tasks as compared to other optimization algorithms.     

Hybrid differential evolution and Nelder�CMead algorithm with re-optimization

Nonlinear optimization algorithms could be divided into local exploitation methods such as Nelder–Mead (NM) algorithm and global exploration ones, such as differential evolution (DE). The former searches fast yet could be easily trapped by local optimum, whereas the latter possesses better convergence quality. This paper proposes hybrid differential evolution and NM algorithm with re-optimization, called as DE-NMR. At first a modified NM, called NMR is presented. It re-optimizes from the optimum point at the first time and thus being able to jump out of local optimum, exhibits better properties than NM. Then, NMR is combined with DE. To deal with equal constraints, adaptive penalty function method is adopted in DE-NMR, which relaxes equal constraints into unequal constrained functions with an adaptive relaxation parameter that varies with iteration. Benchmark optimization problems as well as engineering design problems are used to experiment the performance of DE-NMR, with the number of function evaluation times being employed as the main index of measuring convergence speed, and objective function values as the main index of optimum’s quality. Non-parametric tests are employed in comparing results with other global optimization algorithms. Results illustrate the fast convergence speed of DE-NMR.     

Algorithms of discrete optimization and their application to problems with fuzzy coefficients

An approach to solving optimization problems with fuzzy coefficients in objective functions and constraints is described. It consists in formulating and solving one and the same problem within the framework of mutually related models with constructing equivalent analogs with fuzzy coefficients in objective functions alone. It enables one to maximally cut off dominated alternatives “from below” as well as “from above”. Since the approach is applied within the context of fuzzy discrete optimization problems, several modified algorithms of discrete optimization are discussed. These algorithms are associated with the method of normalized functions, are based on a combination of formal and heuristic procedures, and allow one to obtain quasi-optimal solutions after a small number of steps, thus overcoming the computational complexity posed the NP-completeness of discrete optimization problems. The subsequent contraction of the decision uncertainty regions is associated with reduction of the problem to multiobjective decision making in a fuzzy environment with using techniques based on fuzzy preference relations. The techniques are also directly applicable to situations in which the decision maker is required to choose alternatives from a set of explicitly available alternatives. The results of the paper are of a universal character and can be applied to the design and control of systems and processes of different purposes as well as the enhancement of corresponding CAD/CAM systems and intelligent decision making systems. The results of the paper are already being used to solve problems of power engineering.     

Data driven surrogate-based optimization in the problem solving environment WBCSim

Large scale, multidisciplinary, engineering designs are always difficult due to the complexity and dimensionality of these problems. Direct coupling between the analysis codes and the optimization routines can be prohibitively time consuming due to the complexity of the underlying simulation codes. One way of tackling this problem is by constructing computationally cheap(er) approximations of the expensive simulations that mimic the behavior of the simulation model as closely as possible. This paper presents a data driven, surrogate-based optimization algorithm that uses a trust region-based sequential approximate optimization (SAO) framework and a statistical sampling approach based on design of experiment (DOE) arrays. The algorithm is implemented using techniques from two packages—SURFPACK and SHEPPACK that provide a collection of approximation algorithms to build the surrogates and three different DOE techniques—full factorial (FF), Latin hypercube sampling, and central composite design—are used to train the surrogates. The results are compared with the optimization results obtained by directly coupling an optimizer with the simulation code. The biggest concern in using the SAO framework based on statistical sampling is the generation of the required database. As the number of design variables grows, the computational cost of generating the required database grows rapidly. A data driven approach is proposed to tackle this situation, where the trick is to run the expensive simulation if and only if a nearby data point does not exist in the cumulatively growing database. Over time the database matures and is enriched as more and more optimizations are performed. Results show that the proposed methodology dramatically reduces the total number of calls to the expensive simulation runs during the optimization process.     

Multidisciplinary Optimization of Multibody Systems with Application to the Design of Rail Vehicles

A methodology for the design optimization of multibody systems is presented. The methodology has the following features: (1) multibody dynamics is employed to model and simulate complex systems; (2) multidisciplinary optimization (MDO) methods are used to combine multibody systems and additional systems in a synergistic manner; (3) using genetic algorithms (GAs) and other effective search algorithms, the mechanical and other design variables are optimized simultaneously. The methodology is shown to handle the conflicting requirements of rail vehicle design, i.e., lateral stability, curving performance, and ride quality, in an effective manner. By coordinating these conflicting requirements at the system level, three multibody models corresponding to each of these requirements for a rail vehicle are optimized simultaneously.     

Improving constrained clustering with active query selection

In this article, we address the problem of automatic constraint selection to improve the performance of constraint-based clustering algorithms. To this aim we propose a novel active learning algorithm that relies on a k-nearest neighbors graph and a new constraint utility function to generate queries to the human expert. This mechanism is paired with propagation and refinement processes that limit the number of constraint candidates and introduce a minimal diversity in the proposed constraints. Existing constraint selection heuristics are based on a random selection or on a min–max criterion and thus are either inefficient or more adapted to spherical clusters. Contrary to these approaches, our method is designed to be beneficial for all constraint-based clustering algorithms. Comparative experiments conducted on real datasets and with two distinct representative constraint-based clustering algorithms show that our approach significantly improves clustering quality while minimizing the number of human expert solicitations.     

Object-Oriented Data Modelling for Graphics Databases: a Declarative Approach

This paper presents a new scheme to integrate the declarative approach to graphics and object-oriented data modelling techniques to form a fruitful symbiosis for constraint-based graphics database systems. It has rich modelling constructs to describe graphics data and allows sharing of representation. It also provides useful mechanisms for management of integrity constraints. We have also identified important classes of constraints in the context of object-oriented graphics database systems. Examples are given for maintenance of constraints at the time of insertion, deletion and modification.     

Interdigitation for effective design space exploration using iSIGHT

Optimization studies for nonlinear constrained problems (i.e. most complex engineering design problems) have repeatedly shown that (i) no single optimization technique performs best for all design problems, and (ii) in most cases, a mix of techniques perform better than a single technique for a given design problem. iSIGHT^TM is a generic software framework for integration, automation, and optimization of design processes that has been developed on the foundation of interdigitation: the strategy of combining multiple optimization algorithms to exploit their desirable aspects for solving complex problems. With the recent paradigm shift from traditional optimization to design space exploration for evaluating “what-if” scenarios and trade-off studies, iSIGHT has grown from an optimization software system to a complete design exploration environment, providing a suite of design exploration tools including a collection of optimization techniques, design of experiments techniques, approximation methods, and probabilistic quality engineering methods. Likewise, the interdigitation design methodology embodied in iSIGHT has grown to support the interdigitation of all design exploration tools for effective design space exploration. In this paper we present an overview of iSIGHT, past and present, of the interdigitation design methodology and its implementation for multiple design exploration tools, and of an industrial case study for which elements of this methodology have been applied. Received December 30, 2000     

An efficient multiobjective differential evolution algorithm for engineering design

Solving engineering design and resources optimization via multiobjective evolutionary algorithms (MOEAs) has attracted much attention in the last few years. In this paper, an efficient multiobjective differential evolution algorithm is presented for engineering design. Our proposed approach adopts the orthogonal design method with quantization technique to generate the initial archive and evolutionary population. An archive (or secondary population) is employed to keep the nondominated solutions found and it is updated by a new relaxed form of Pareto dominance, called Pareto-adaptive ϵ-dominance (paϵ-dominance), at each generation. In addition, in order to guarantee to be the best performance produced, we propose a new hybrid selection mechanism to allow the archive solutions to take part in the generating process. To handle the constraints, a new constraint-handling method is employed, which does not need any parameters to be tuned for constraint handling. The proposed approach is tested on seven benchmark constrained problems to illustrate the capabilities of the algorithm in handling mathematically complex problems. Furthermore, four well-studied engineering design optimization problems are solved to illustrate the efficiency and applicability of the algorithm for multiobjective design optimization. Compared with Nondominated Sorting Genetic Algorithm II, one of the best MOEAs available at present, the results demonstrate that our approach is found to be statistically competitive. Moreover, the proposed approach is very efficient and is capable of yielding a wide spread of solutions with good coverage and convergence to true Pareto-optimal fronts.     

A Genetic Algorithm for Multiobjective Robust Design

The goal of robust design is to develop stable products that exhibit minimum sensitivity to uncontrollable variations. The main drawback of many quality engineering approaches, including Taguchi's ideology, is that they cannot efficiently handle presence of several often conflicting objectives and constraints that occur in various design environments.Classical vector optimization and multiobjective genetic algorithms offer numerous techniques for simultaneous optimization of multiple responses, but they have not addressed the central quality control activities of tolerance design and parameter optimization. Due to their ability to search populations of candidate designs in parallel without assumptions of continuity, unimodality or convexity of underlying objectives, genetic algorithms are an especially viable tool for off-line quality control.In this paper we introduce a new methodology which integrates key concepts from diverse fields of robust design, multiobjective optimization and genetic algorithms. The genetic algorithm developed in this work applies natural genetic operators of reproduction, crossover and mutation to evolve populations of hyper-rectangular design regions while simultaneously reducing the sensitivity of the generated designs to uncontrollable variations. The improvement in quality of successive generations of designs is achieved by conducting orthogonal array experiments as to increase the average signal-to-noise ratio of a pool of candidate designs from one generation to the next.     

Density-based semi-supervised clustering

Semi-supervised clustering methods guide the data partitioning and grouping process by exploiting background knowledge, among else in the form of constraints. In this study, we propose a semi-supervised density-based clustering method. Density-based algorithms are traditionally used in applications, where the anticipated groups are expected to assume non-spherical shapes and/or differ in cardinality or density. Many such applications, among else those on GIS, lend themselves to constraint-based clustering, because there is a priori knowledge on the group membership of some records. In fact, constraints might be the only way to prevent the formation of clusters that do not conform to the applications’ semantics. For example, geographical objects, e.g. houses, separated by a borderline or a river may not be assigned to the same cluster, independently of their physical proximity. We first provide an overview of constraint-based clustering for different families of clustering algorithms. Then, we concentrate on the density-based algorithms’ family and select the algorithm DBSCAN, which we enhance with Must-Link and Cannot-Link constraints. Our enhancement is seamless: we allow DBSCAN to build temporary clusters, which we then split or merge according to the constraints. Our experiments on synthetic and real datasets show that our approach improves the performance of the algorithm.     

Optimal design of truss structures using parallel computing

Parallel design optimization of large structural systems calls for a multilevel approach to the optimization problem. The general optimization problem is decomposed into a number of non-interacting suboptimization problems on the first level. They are controlled from the second level through coordination variables. Thus, the solutions of the independent first-level subsystems are directed towards the overall system optimum. In the present paper, optimal design of truss structures using parallel computing technique is described. In this method, optimization of a large truss structure has been carried out by decomposing the structure into sub-domains and suboptimization tasks. Each sub-domain has independent design variables and a small number of behaviour constraints. The two-level sub-domain optimum design approach is summarized by several numerical examples with speedups and efficiencies of algorithms on message passing systems. It has been noticed that the efficiency of the algorithm for design optimization increases with the size of the structure.