scholarly journals Modeling, Design and Optimization of Flexible Mechanical Systems

2021 ◽  
Vol 11 (15) ◽  
pp. 7124
Author(s):  
Erich Wehrle ◽  
Ilaria Palomba ◽  
Renato Vidoni
Keyword(s):  
Engineering Design ◽  
Mechanical Systems ◽  
Design And Optimization ◽  
Lightweight Engineering

Performance, efficiency and economy drive the design of mechanical systems and structures and has led lightweight engineering design to prominence [...]

A Multi-Agent Framework for Collaborative Engineering Design and Optimization

2004 ◽  
Author(s):  
Qi Hao ◽  
Weiming Shen ◽  
Zhan Zhang ◽  
Seong-Whan Park ◽  
Jai-Kyung Lee
Keyword(s):  
Engineering Design ◽  
Rapid Development ◽  
Autonomous Agent ◽  
Multi Agent Systems ◽  
Engineering Project ◽  
Development Environment ◽  
Collaborative Engineering ◽  
Heterogeneous Agent ◽  
Design And Optimization ◽  
Multi Agent

Agent technology is playing an increasingly important role in developing intelligent, distributed and collaborative applications. The innate difficulties of interoperation between heterogeneous agent communities and rapid construction of multi-agent systems have motivated the emergence of FIPA specifications and the proliferation of multi-agent system platforms or toolkits that implement FIPA specifications. In this paper, a FIPA compliant multi-agent framework called AADE (Autonomous Agent Development Environment) is presented. This framework, originating from the engineering fields, can facilitate the rapid development of collaborative engineering applications (especially in engineering design and manufacturing fields) through the provision of reusable packages of agent-level components and programming tools. An agent oriented engineering project on the development of an e-engineering design and optimization environment is designed and developed based on the facilities provided by the AADE framework.

Comparison of Gene Expression Programming and Common Metamodeling Techniques in Engineering Design

2011 ◽  
Author(s):  
Mi Xiao ◽  
Liang Gao ◽  
Xinyu Shao ◽  
Haobo Qiu ◽  
Li Nie
Keyword(s):  
Gene Expression ◽  
Engineering Design ◽  
Small Sample Size ◽  
Gene Expression Programming ◽  
Small Sample ◽  
Mathematical Functions ◽  
Approximation Accuracy ◽  
Design And Optimization ◽  
Metamodeling Technique ◽  
Approximation Models

To reduce the tremendous computational expense of implementing complex simulation and analysis in engineering design, more and more researchers pay attention to the construction of approximation models. The approximation models, also called surrogate models and metamodels, can be utilized to replace simulation and analysis codes for design and optimization. Commonly used metamodeling techniques include response surface methodology (RSM), kriging and radial basis functions (RBF). In this paper, gene expression programming (GEP) algorithm in evolutionary computing is investigated as an alternative technique for approximation. The performance of GEP is examined by its innovative applications to the approximation of mathematical functions and engineering analyses. Compared to RSM, kriging and RBF, GEP is demonstrated to be more accurate for the small sample size. For large sample sets, GEP also shows good approximation accuracy. Additionally, GEP has the best transparency since it can provide explicit and compact function relationships and clear factor contributions. Overall, as a novel metamodeling technique, GEP exhibits great capabilities to provide the accurate approximation of a design space and will have wide applications in engineering design, especially when only a few sample points are selected for approximation.

Computational methods in engineering design and optimization

2013 ◽  
Vol 30 (4) ◽  
Author(s):  
Massimiliano Vasile ◽  
Edmondo Minisci ◽  
Domenico Quagliarella
Keyword(s):  
Engineering Design ◽  
Computational Methods ◽  
Design And Optimization

A New Approach to Probability in Engineering Design and Optimization

1984 ◽  
Vol 106 (1) ◽  
pp. 5-10 ◽  
Author(s):  
J. N. Siddall
Keyword(s):  
Probability Density ◽  
Engineering Design ◽  
Probability Distributions ◽  
Probability Density Functions ◽  
Density Functions ◽  
New Approach ◽  
Design And Optimization ◽  
Evolutionary Technique ◽  
Probability And Statistics

The anomalous position of probability and statistics in both mathematics and engineering is discussed, showing that there is little consensus on concepts and methods. For application in engineering design, probability is defined as strictly subjective in nature. It is argued that the use of classical methods of statistics to generate probability density functions by estimating parameters for assumed theoretical distributions should be used with caution, and that the use of confidence limits is not really meaningful in a design context. Preferred methods are described, and a new evolutionary technique for developing probability distributions of new random variables is proposed. Although Bayesian methods are commonly considered to be subjective, it is argued that, in the engineering sense, they are really not. A general formulation of the probabilistic optimization problem is described, including the role of subjective probability density functions.

Evolutionary Multi-Agent Systems: An Adaptive Approach to Optimization in Dynamic Environments

2008 ◽  
Author(s):  
Lindsay Hanna ◽  
Jonathan Cagan
Keyword(s):  
Organizational Structure ◽  
Engineering Design ◽  
Dynamic Environments ◽  
Optimization Process ◽  
Multi Agent Systems ◽  
Design And Optimization ◽  
Solution Strategies ◽  
Engineering Design Problems ◽  
Potential Applications ◽  
Multi Agent

This paper explores the ability of a team of autonomous software agents to be effective in unknown and changing optimization environments by evolving to use the most successful algorithms at the points in the optimization process where they will be the most effective. We present the core framework and methodology which has potential applications in layout, scheduling, manufacturing, and other engineering design areas. The communal agent team organizational structure employed allows cooperation of agents through the products of their work and creates an ever changing set of individual solutions for the agents to work on. In addition, the organizational structure allows the framework to be adaptive to changes in the design space that occur during the optimization process — making our approach extremely flexible to the kinds of dynamic environments encountered in engineering design problems. An evolutionary approach is used, but evolution occurs at the strategic, rather than solution level — where the strategies of agents in the team (the decisions for picking, altering, and inserting a solution) evolve over time. As an application of this approach, individual solutions are tours in the familiar combinatorial optimization problem of the traveling salesman. With a constantly changing set of these tours, the team, each agent running a different solution strategy, must evolve to apply the solution strategies which are most useful given the set at any point in the process. As a team, the evolutionary agents produce better solutions than any individual algorithm. We discuss the extensions to our preliminary work that will make our framework highly useful to the design and optimization community.

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