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.

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 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.

A Comprehensive Robust Design Approach for Decision Trade-Offs in Complex Systems Design

1999 ◽  
Vol 123 (1) ◽  
pp. 1-10 ◽  
Author(s):  
Monu Kalsi ◽  
Kurt Hacker ◽  
Kemper Lewis
Keyword(s):  
Robust Design ◽  
Systems Design ◽  
Decision Makers ◽  
Engineering Systems ◽  
Trade Offs ◽  
Design Variables ◽  
Complex Engineering ◽  
The Cost ◽  
Passenger Aircraft

In this paper we introduce a technique to reduce the effects of uncertainty and incorporate flexibility in the design of complex engineering systems involving multiple decision-makers. We focus on the uncertainty that is created when a disciplinary designer or design team must try to predict or model the behavior of other disciplinary subsystems. The design of a complex system is performed by many different designers and design teams, each of which may only have control over a portion of the total set of system design variables. Modeling the interaction among these decision-makers and reducing the effect caused by lack of global control by any one designer is the focus of this paper. We use concepts from robust design to reduce the effects of decisions made during the design of one subsystem on the performance of the rest of the system. Thus, in a situation where the cost of uncertainty is high, these tools can be used to increase the robustness, or independence, of the subsystems, enabling designers to make more effective decisions. To demonstrate the usefulness of this approach, we consider a case study involving the design of a passenger aircraft.

Optimal Design of Mechanical Engineering Systems

1995 ◽  
Vol 117 (B) ◽  
pp. 55-62 ◽  
Author(s):  
P. Y. Papalambros
Keyword(s):  
Design Optimization ◽  
Large Scale ◽  
Mechanical Design ◽  
Mathematical Optimization ◽  
Structural Systems ◽  
Engineering Systems ◽  
Design Problems ◽  
Future Design ◽  
And Topology ◽  
Key Issues

The capability of mathematical optimization to support the mechanical design process is finally reaching wide recognition, as evidenced by significant applications in industry. The article reviews key issues and challenges for design optimization theorists and practitioners. Large scale system design and topology or configuration design are identified as the most important areas of future design optimization research. Some new approaches for partitioning large design problems and for topology optimization of multi-component structural systems are introduced.

A Flexible Optimization Procedure for Mechanical Component Design Based on Genetic Adaptive Search

1998 ◽  
Vol 120 (2) ◽  
pp. 162-164 ◽  
Author(s):  
K. Deb ◽  
M. Goyal
Keyword(s):  
Engineering Design ◽  
Optimization Problems ◽  
Mechanical Design ◽  
Optimization Procedure ◽  
Continuous Variables ◽  
Design Problems ◽  
Component Design ◽  
Engineering Design Problems ◽  
Complex Engineering ◽  
Optimum Solution

A flexible algorithm for solving nonlinear engineering design optimization problems involving zero-one, discrete, and continuous variables is presented. The algorithm restricts its search only to the permissible values of the variables, thereby reducing the search effort in converging near the optimum solution. The efficiency and ease of application of the proposed method is demonstrated by solving four different mechanical design problems chosen from the optimization literature. These results are encouraging and suggest the use of the technique to other more complex engineering design problems.

Optimal Design of Mechanical Engineering Systems

1995 ◽  
Vol 117 (B) ◽  
pp. 55-62
Author(s):  
P. Y. Papalambros
Keyword(s):  
Design Optimization ◽  
Large Scale ◽  
Mechanical Design ◽  
Mathematical Optimization ◽  
Structural Systems ◽  
Engineering Systems ◽  
Design Problems ◽  
Future Design ◽  
And Topology ◽  
Key Issues

The capability of mathematical optimization to support the mechanical design process is finally reaching wide recognition, as evidenced by significant applications in industry. The article reviews key issues and challenges for design optimization theorists and practitioners. Large scale system design and topology or configuration design are identified as the most important areas of future design optimization research. Some new approaches for partitioning large design problems and for topology optimization of multi-component structural systems are introduced.

An Augmented Lagrange Multiplier Based Method for Mixed Integer Discrete Continuous Optimization and its Applications to Mechanical Design

1993 ◽  
Author(s):  
B. K. Kannan ◽  
Steven N. Kramer
Keyword(s):  
Lagrange Multiplier ◽  
Conjugate Gradient Method ◽  
Conjugate Gradient ◽  
Gradient Method ◽  
Optimization Problems ◽  
Mechanical Design ◽  
Continuous Optimization ◽  
Mixed Integer ◽  
Continuous Variables ◽  
Augmented Lagrange Multiplier

Abstract An algorithm for solving nonlinear optimization problems involving discrete, integer, zero-one and continuous variables is presented. The augmented Lagrange multiplier method combined with Powell’s method and Fletcher & Reeves Conjugate Gradient method are used to solve the optimization problem where penalties are imposed on the constraints for integer / discrete violations. The use of zero-one variables as a tool for conceptual design optimization is also described with an example. Several case studies have been presented to illustrate the practical use of this algorithm. The results obtained are compared with those obtained by the Branch and Bound algorithm. Also, a comparison is made between the use of Powell’s method (zeroth order) and the Conjugate Gradient method (first order) in the solution of these mixed variable optimization problems.

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