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Comparison of Selection Methods of Genetic Algorithms for Automated Component-Selection of Design Synthesis with Model-Based Systems Engineering
... When carrying out complex multi-domain trade-offs, it is becoming increasingly common to formulate the decision as a mathematical problem. This allows the use of optimization algorithms such as examples of meta-heuristics like genetic algorithms to come to a decision, generally in less time and for less cost [7]. Several examples of functions used for benchmarking such optimisation algorithms exist. ...
Model Based Systems Engineering (MBSE) is an interesting alternative to traditional systems engineering methods. Instead of using electronic documents to record system information, MBSE uses a unified and coherent system model. Trade-offs are a major element of a space systems engineer's role in early system design. This can be a particularly challenging process in the domain of spacecraft, as the system designs are often very complex and the constraints can be difficult to characterize. There has been little previous research on the use of MBSE as a design space exploration tool or in support of trade-offs. This paper investigates the potential to use MBSE for design exploration and to understand trade-offs, through the creation of a new toolset including a SysML profile. The tool draws on generative design (allowing automatic guided generation of a multitude of design alternatives) and system optimization to rapidly generate and assess new designs using interactive analysis and visualizations. Techniques such as surrogate modelling, genetic algorithms and robustness measurements will be available in the toolset. The toolset was applied to a design scenario aiming to improve the trade off and design selection process for LEO Earth observation satellites. The upcoming ESA TRUTHS space mission was used as a case study and the design process was recorded and compared to a manual design exploration approach. The toolset was found to reduce the design exploration time by 38% to 96% , allow exploration of more designs in an equivalent time and provide better quantification of the relationships present in the design space, all without drops in selected design quality. For now, the toolset can only perform parameter variation in the design exploration and future work is expected to extend this to higher levels of variability. The study also discusses how the MBSE toolset could be applied to other missions, offering the same advantages to all early phase spacecraft designers.
... To conduct a trade study, system engineers usually need to design a number of alternatives 100%-logical-architecture model of a system and manually analyze them to find the best design for component level [23] [26]. This process is often time and cost consuming [15] and error-prone [16]. ...
Today's MBSE tools and environments are highly varied and therefore present a challenge for organizations looking to implement MBSE. Furthermore, while MBSE environments are highly capable of supporting the description of design baselines, the current capabilities within these environments could be further refined for exploring alternative designs. As a result it is important to gain an understanding of the limitations of current MBSE tooling in performing the valuable activity of design space exploration, and identify a set of candidate techniques to combat these. This paper reviews the various options available to MBSE practitioners by comparing some of the most common MBSE languages, tools and methods. The possible issues that can be encountered when exploring different designs have been identified and assigned a severity rating. A set of design space exploration techniques are presented, and where possible these have been sourced from existing literature. A knowledge graph has been constructed to collect all this data into a structured format, containing all the MBSE languages, tools, methods, design space exploration-related issues and techniques, as well as the relationships between each of these. This knowledge graph, implemented as a Neo4j graph database, allowed deeper insights to be drawn from the collected information. By defining a selected MBSE environment, including language, tool and method, the knowledge graph could be used to identify the least troublesome sequence (with minimum number of related issues) to arrive at a desired design artifact, for example a set of optimized system parameters. Beside this, the knowledge graph could be used to display the relationships and clusters of MBSE languages, tools and methods, to assist organizations with selecting suitable MBSE environment elements. Future work will bring greater depth to the analysis available with the knowledge graph, for instance, differentiation between different types of design space exploration issues and techniques.
This paper explains genetic algorithm for novice in this field. Basic
philosophy of genetic algorithm and its flowchart are described. Step by step
numerical computation of genetic algorithm for solving simple mathematical
equality problem will be briefly explained
The field of computational design synthesis has been an active area of research for almost half a century. Research advances in this field have increased the sophistication and complexity of the designs that can be synthesized, and advances in the speed and power of computers have increased the efficiency with which those designs can be generated. Some of the results of this research have begun to be used in industrial practice, yet many open issues and research challenges remain. This paper provides a model of the automated synthesis process as a context to discuss research in the area. The varied works of the authors are discussed as representative of the breadth of methods and results that exist under the field of computational design synthesis. Furthermore, some guidelines are presented to help researchers and designers find approaches to solving their particular design problems using computational design synthesis.
A genetic algorithm (GA) has several genetic operators that can be modified to improve the performance of particular implementations. These operators include parent selection, crossover and mutation. Selection is one of the important operations in the GA process. There are several ways for selection. This paper presents the comparison of GA performance in solving travelling salesman problem (TSP) using different parent selection strategy. Several TSP instances were tested and the results show that tournament selection strategy outperformed proportional roulette wheel and rank-based roulette wheel selections, achieving best solution quality with low computing times. Results also reveal that tournament and proportional roulette wheel can be superior to the rank-based roulette wheel selection for smaller problems only and become susceptible to premature convergence as problem size increases.
This paper presents the implementation of a number of elitist schemes for a low cost printed circuit board (PCB) inspection system. This strategy also aims to explore the role of tournament and roulette-wheel in improving the existing system when using a deterministic selection scheme. In this system, GA is used to detect rotation angle and displacement of PCB placed arbitrarily on a conveyor belt passing under the camera. Deterministic, tournament and roulette-wheel selection scheme have been compared in terms of maximum fitness, rate of accuracy and computation time. The finding shows that deterministic outperformed the other two schemes in all categories and still proves to be an ideal candidate for GA-based PCB inspection system. The modifications on population size and implementation of center block image matching technique also contributed to the improvement of computational time of the system.
The purpose of this paper is to influence the researchers working in the field of algorithms for large scale systems about the efficiency of evolution algorithms as optimization techniques to explore robust large search spaces and find near-global optima. These evolutionary algorithms (EAs) can be an alternative to numerical methods in difficult optimization problems like complex systems where the phenomena are difficult to model due to uncertainty, noise or even too little knowledge of the real problem. In such cases EAs are robust procedures to overcome these difficulties. In general, these algorithms are not just an alternative to traditional methods, nowadays they are used hybridised form to complement and extend numerical methods. The convergence of heuristic optimization techniques is not affected by the continuity or differentiability of the functions to be optimized in the applications. These algorithms only require evaluation of the function in search space points. Applications of these evolutionary algorithms have been more convincing than their theory, which is still weak, though under progress. This paper is divided in two parts. A large number of references are included to enhance the presentation of the material. Finally, we describe the main aspects for solving an optimization problem of interest in Aerospace Industry by Genetic Algorithms. The problem considered is the optimum design of an airfoil shape, which is an inverse problem that consists of finding the shape for a given pressure distribution on the airfoil.
Metaheuristic search algorithms have been widely applied to almost all engineering disciplines with the exception of software engineering. It is surprising that these essentially software driven technologies have not yet fully penetrated the software engineering research community and are not widely applied when compared to the more traditional engineering disciplines. This workshop aims to build the embryonic research community interested in the application of metaheuristic algorithms to software engineering problems.
Engineers design systems by searching through the large number of
possible solutions to discover the best specific solution. The search
process is often time consuming and expensive. But by exploiting the
natural processes that biological systems use to evolve and adapt,
design engineers can often quickly solve otherwise difficult design
problems that resist solution by traditional optimization methods. This
paper explains the basic technique of the genetic algorithm and shows
how design engineers can use a genetic algorithm to solve real design
engineering problems. This paper focuses on explaining how genetic
algorithms work. A brief example at the end demonstrates how the
practicing engineer can use this powerful technique to solve real world
problems in engineering design. The example of a structural design
problem uses a genetic algorithm to minimize the weight of a pin jointed
frame, but the genetic algorithm can be applied to almost any type of
design problem
Using Domain Specific Languages to Capture Design Synthesis Knowledge for Model-Based Systems Engineering
- A A K A Kerzhner
- C J Paredis
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Supporting Multidisciplinary Vehicle Analysis Using a Vehicle Reference Architecture Model in SysML
- J M Branscomb
- C J Paredis
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- M J Jennings
J. M. Branscomb, C. J. Paredis, J. Che and M. J. Jennings, "Supporting
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Genetic Algorithms & Modeling
- R Chakraborty
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JENETICS: Library User's Manual
- F Wilhelmstotter
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