A Distributed Pool Architecture for Highly Constrained Optimization Problems in Complex Systems Design

2011 ◽  
Author(s):  
Vijitashwa Pandey ◽  
Zissimos P. Mourelatos
Keyword(s):  
Complex Systems ◽  
Optimization Problems ◽  
Mechanical Design ◽  
Systems Design ◽  
Optimization Approach ◽  
Dynamic Changes ◽  
Design Problems ◽  
Complete Knowledge ◽  
Design Variables ◽  
Design Requirements

The design of complex systems design is challenging because of the presence of numerous design variables and constraints. Dynamic changes in design requirements and lack of complete knowledge of subsystem requirements add to the complexity. A recently proposed pool architecture has been shown to aide distributed solving of optimization problems. The approach not only saves solution time but also has other benefits like resiliency against failures of some processors. We apply this approach in this paper, to highly constrained design problems, with dynamically changing constraints, where finding a feasible solution is challenging. This task is distributed between the processors in the methodology we propose. We demonstrate the efficacy of our method using an MINLP-class of mechanical design optimization problem. We demonstrate the computational savings and the resistance to partial failures in the processors. In addition, we show how the optimization approach can adapt to dynamic changes in design constraints.

A Distributed Pool Architecture for Highly Constrained Optimization Problems in Complex Systems Design

2013 ◽  
Vol 13 (3) ◽  
Author(s):  
Vijitashwa Pandey ◽  
Zissimos P. Mourelatos
Keyword(s):  
Design Optimization ◽  
Optimization Problems ◽  
Mechanical Design ◽  
Solution Process ◽  
Systems Design ◽  
Mixed Integer ◽  
Engineering Systems ◽  
Design Problems ◽  
Design Variables ◽  
Complex Engineering

Optimal design of complex engineering systems is challenging because numerous design variables and constraints are present. Dynamic changes in design requirements and lack of complete knowledge of subsystem requirements add to the complexity. We propose an enhanced distributed pool architecture to aid distributed solving of design optimization problems. The approach not only saves solution time but is also resilient against failures of some processors. It is best suited to handle highly constrained design problems, with dynamically changing constraints, where finding even a feasible solution (FS) is challenging. In our work, this task is distributed among many processors. Constraints can be easily added or removed without having to restart the solution process. We demonstrate the efficacy of our method in terms of computational savings and resistance to partial failures of some processors, using two mixed integer nonlinear programming (MINLP)-class mechanical design optimization problems.

Handling Multiple Objectives in Decentralized Design

2005 ◽  
Author(s):  
Vincent Chanron ◽  
Kemper Lewis ◽  
Yayoi Murase ◽  
Kazuhiro Izui ◽  
Shinji Nishiwaki ◽  
...  
Keyword(s):  
Complex Systems ◽  
Design Solution ◽  
Distributed Environments ◽  
Engineering Systems ◽  
Design Problems ◽  
Design Requirements ◽  
Decentralized Design ◽  
Set Based Design ◽  
Standard Techniques ◽  
Different Parts

Most complex systems, including engineering systems such as cars, airplanes, and satellites, are the results of the interactions of many distinct entities working on different parts of the design. Decentralized systems constitute a special class of design under distributed environments. They are characterized as large and complex systems divided into several smaller entities that have autonomy in local optimization and decision-making. A primary issue in decentralized design processes is to ensure that the designers that are involved in the process converge to a single design solution that is optimal and meets the design requirements, while being acceptable to all the participants. This is made difficult by the strong interdependencies between the designers, which are usually characteristic of such systems. This paper proposes a critical review of standard techniques to modeling and solving decentralized design problems, and shows mathematically the challenges created by having multiobjective subsystems. A method based on set-based design is then proposed to alleviate some of these challenging issues. An illustration of its applicability is given in the form of the design of a space satellite.

Nonlinear Integer and Discrete Programming in Mechanical Design

1988 ◽  
Author(s):  
E. Sandgren
Keyword(s):  
Mechanical Design ◽  
General Purpose ◽  
Conceptual Level ◽  
Design Problems ◽  
Design Decisions ◽  
Design Variables ◽  
Nonlinear Mathematical Programming ◽  
Discrete Programming ◽  
Mathematical Programming Problems ◽  
Quadratic Programming Method

Abstract A general purpose algorithm for the solution of nonlinear mathematical programming problems containing integer, discrete, zero-one and continuous design variables is described. The algorithm implements a branch and bound procedure in conjunction with both an exterior penalty function and a quadratic programming method. Variable bounds are handled independently from the design constraints which removes the necessity to reformulate the problem at each branching node. Examples are presented to demonstrate the utility of the algorithm for solving design problems. The use of zero-one variables to represent design decisions in order to allow conceptual level design to be performed is demonstrated.

Study on Vibration Reduction Design of Steel-Composite Material Hybrid Mounting for Ship Based on Material Selection Optimization

2013 ◽  
Vol 694-697 ◽  
pp. 415-424
Author(s):  
Wei Wang ◽  
Lu Yun Chen ◽  
Yu Fang Zhang
Keyword(s):  
Power Flow ◽  
Optimization Problems ◽  
Material Selection ◽  
Vibration Reduction ◽  
Optimization Method ◽  
Optimization Approach ◽  
Structural Dynamic ◽  
Level Difference ◽  
Design Variables ◽  
Steel Composite

The material selection optimization for vibration reduction design is studied present article. By introducing the stacking sequence hypothesis of metal material, taking into account the power flow level difference and vibration level difference parameter, the mechanical parameters of the material and plies number are defined as design variables, and the mathematical model of structural dynamic optimization based on material selection optimization approach is established. Finally, a naval hybrid steel-composite mounting structure for example, by introducing genetic algorithm, the optimization problems is solved. The numerical results show that the optimization method is effective and feasible.

Maximizing Flexibility for Complex Systems Design to Compensate Lack-of-Knowledge Uncertainty

2019 ◽  
Vol 5 (4) ◽  
Author(s):  
Marco Daub ◽  
Fabian Duddeck
Keyword(s):  
Complex Systems ◽  
Complete Solution ◽  
Solution Space ◽  
Systems Design ◽  
Rigid Wall ◽  
Total System ◽  
Independent Manner ◽  
Design Variables ◽  
Knowledge Uncertainty ◽  
Lack Of Knowledge

Abstract The consideration of uncertainty is especially important for the design of complex systems. Because of high complexity, the total system is normally divided into subsystems, which are treated in a hierarchical and ideally independent manner. In recent publications, e.g., (Zimmermann, M., and von Hoessle, J. E., 2013, “Computing Solution Spaces for Robust Design,” Int. J. Numer. Methods Eng., 94(3), pp. 290–307; Fender, J., Duddeck, F., and Zimmermann, M., 2017, “Direct Computation of Solution Spaces,” Struct. Multidiscip. Optim., 55(5), pp. 1787–1796), a decoupling strategy is realized via first the identification of the complete solution space (solutions not violating any design constraints) and second via derivation of a subset, a so-called box-shaped solution space, which allows for decoupling and therefore independent development of subsystems. By analyzing types of uncertainties occurring in early design stages, it becomes clear that especially lack-of-knowledge uncertainty dominates. Often, there is missing knowledge about overall manufacturing tolerances like limitations in production or subsystems are not even completely defined. Furthermore, flexibility is required to handle new requirements and shifting preferences concerning single subsystems arising later in the development. Hence, a set-based approach using intervals for design variables (i.e., interaction quantities between subsystems and the total system) is useful. Because in the published approaches, no uncertainty consideration was taken into account for the computation of these intervals, they can possibly have inappropriate size, i.e., being too narrow. The work presented here proposes to include these uncertainties related to design variables. This allows now to consider lack-of-knowledge uncertainty specific for early phase developments in the framework of complex systems design. An example taken from a standard crash load case (frontal impact against a rigid wall) illustrates the proposed methodology.

Isogeometric shape optimization for design dependent loads

2021 ◽  
pp. 1-13
Author(s):  
Arkaprabho Pal ◽  
Sourav Rakshit
Keyword(s):  
Shape Optimization ◽  
Optimization Problems ◽  
Minimum Weight ◽  
Control Point ◽  
Optimization Approach ◽  
Shape Sensitivity ◽  
Body Forces ◽  
Design Variables ◽  
Design Dependent Loads ◽  
Material Layout

Abstract This paper presents a new isogeometric formulation for shape optimization of structures subjected to design dependent loads. This work considers two types of design dependent loads, namely surface loads like pressure where the direction and/or magnitude of force changes with the variation of boundary shape, and body forces that depend on the material layout. These problems have been mostly solved by topology optimization methods which are prone to difficulties in determination of the loading surface for pressure loads and problems associated with non-monotonous behaviour of compliance and low density regions for body forces. This work uses an isogeometric shape optimization approach where the geometry is defined using NURBS and the control point coordinates and control weights of the boundary are chosen as design variables. This approach accommodates the design dependent loads easily, in addition to its other advantages like exact geometry representation, local control, fewer design variables, excellent shape sensitivity, efficient mesh refinement strategies, and smooth results that can be integrated with CAD. Two classes of optimization problems have been discussed, they are minimum compliance problems subject to volume constraint and minimum weight problems subjected to local stress constraints. These problems are solved using convex optimization programs. Hence, expressions for full sensitivities are derived which is new for structural shape optimization problems with design dependent loads. Some representative engineering examples are solved and compared with existing literature to demonstrate the application of the proposed method.

A simple and efficient constrained particle swarm optimization and its application to engineering design problems

2010 ◽  
Vol 224 (2) ◽  
pp. 389-400 ◽  
Author(s):  
T-H Kim ◽  
I Maruta ◽  
T Sugie
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Problems ◽  
Mechanical Design ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Mixed Integer ◽  
Swarm Optimization ◽  
Continuous Type ◽  
Design Variables ◽  
Engineering Design Problems

Engineering optimization problems usually contain various constraints and mixed integer-discrete-continuous type of design variables. This article proposes an efficient particle swarm optimization (PSO) algorithm for such problems. First, the constrained optimization problem is transformed into an unconstrained problem without introducing any problem-dependent or user-defined parameters such as penalty factors or Lagrange multipliers, though such parameters are usually required in general optimization algorithms. Then, the above PSO method is extended to handle integer, discrete, and continuous design variables in a simple manner, yet with a high degree of precision. The proposed PSO scheme is fairly simple and thus it is easy to implement. In order to demonstrate the effectiveness of our method, several mechanical design optimization problems are solved, and the numerical results are compared with those reported in the literature.

Application of Intelligent Optimization Algorithm in Mechanical Design

2015 ◽  
Vol 713-715 ◽  
pp. 2049-2052
Author(s):  
Sha Sha Dou
Keyword(s):  
Design Theory ◽  
Optimization Design ◽  
Mechanical Design ◽  
Design Method ◽  
Computational Effort ◽  
Ant Algorithm ◽  
Design Problems ◽  
Intelligent Optimization Algorithm ◽  
Design Requirements ◽  
Mechanical Optimization

Mechanical optimization design is a new design method in the development foundation of the modern mechanical design theory, the application of optimization design in mechanical design can make the scheme achieve some optimization results in the design requirements specified, without consuming too much computational effort. The corresponding mathematical models of ant algorithm and Cellular ant algorithm are established, according to the actual mechanical design problems, and used to solve the established mathematical model by computer, so as to obtains the optimal design scheme.

Particle Swarm Methodologies for Engineering Design Optimization

2009 ◽  
Author(s):  
Singiresu S. Rao ◽  
Kiran K. Annamdas
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Multiple Objective ◽  
Optimization Approach ◽  
Objective Functions ◽  
Mixed Design ◽  
Swarm Optimization ◽  
Design Variables ◽  
Mixed Design Variables

Particle swarm methodologies are presented for the solution of constrained mechanical and structural system optimization problems involving single or multiple objective functions with continuous or mixed design variables. The particle swarm optimization presented is a modified particle swarm optimization approach, with better computational efficiency and solution accuracy, is based on the use of dynamic maximum velocity function and bounce method. The constraints of the optimization problem are handled using a dynamic penalty function approach. To handle the discrete design variables, the closest discrete approach is used. Multiple objective functions are handled using a modified cooperative game theory approach. The applicability and computational efficiency of the proposed particle swarm optimization approach are demonstrated through illustrate examples involving single and multiple objectives as well as continuous and mixed design variables. The present methodology is expected to be useful for the solution of a variety of practical engineering design optimization problems.

Fuzzy Optimization of Complex Systems Using Approximation Concepts

1997 ◽  
Vol 50 (11S) ◽  
pp. S97-S104 ◽  
Author(s):  
Hector A. Jensen ◽  
Abdon E. Sepulveda
Keyword(s):  
Original Problem ◽  
Optimization Problems ◽  
Fuzzy Optimization ◽  
Optimization Techniques ◽  
System Response ◽  
Optimization Approach ◽  
Design Variables ◽  
Intermediate Response ◽  
Intermediate Variables ◽  
Approximation Concepts

This paper presents a methodology for the efficient solution of fuzzy optimization problems. Design variables, as well as system parameters are modeled as fuzzy numbers characterized by membership functions. An optimization approach based on approximation concepts is introduced. High quality approximations for system response functions are constructed using the concepts of intermediate response quantities and intermediate variables. These approximations are used to replace the solution of the original problem by a sequence of approximate problems. Optimization techniques for non-differentiable problems which arise in fuzzy optimization are used to solve the approximate optimization problems. Example problems are presented to illustrate the ideas set forth.

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