Automated Component-Selection of Design Synthesis for Physical Architecture with Model-Based Systems Engineering using Evolutionary Trade-off Habibi Husain Arifin No Magic Asia +62 – 2130052646 hharifin@bmaptech.com Ho Kit Robert Ong No Magic Asia +66 - 27171117 robert_ong@nomagic.com Nasis Chimplee No Magic Asia +66 – 868881782 nasis.c@nomagic.com Jirapun Daengdej Assumption University of Thailand +66 – 818288523 jirapun@au.edu Thotsapon Sortrakul Assumption University of Thailand +66 – 818286111 thotsapon@scitech.au.edu Copyright © 2018 by Author Name. Published and used by INCOSE with permission. Abstract. Component-Selection is an important task in design synthesis of MBSE. A trade study is commonly used to help systems engineers and stakeholders selecting the components of a systems design. A simple analysis may be sufficient when it involves only two parameters. However, when the components and their integration become more complex, the trade study also becomes harder, time- and cost-consuming, and error-prone. This paper aims to propose a method to automatically generate the solution by performing an evolutionary search. Sample components of a hybrid car which consists of an engine, an electric motor, and a battery are used in our initial prototype. The logical architecture is represented in the OMG SysML TM via CSM TM . Through the experimental result, this paper shows that the proposed technique allowed the system design to be efficiently selected. Introduction In Model-Based Systems Engineering (MBSE), design synthesis is a fundamental engineering process that includes the generation of physical architecture specifications that satisfy the logical design and desired functional specifications (Kerzhner & Paredis 2009). One of the tasks in design synthesis is the component selection. A simple analysis with trade study generally may overcome the problem when a few parameters are involved, despite it may be insufficient to solve today’s systems engineering problems as the number of components and their integration in the systems are becoming more complex, e.g., aircraft systems and passenger car systems. In the current practice, the systems engineering process requires activities to establish the main goals of a system, specify the system requirements, synthesis a solution space with the possible alternative designs, and evaluate the alternatives to find a set of solution from the solution space (Friedenthal, Moore & Steiner 2015). Meanwhile, the systems complexity complicates the process to drag out the best alternatives from the solution space. Searching through large number of possibilities is often time- and cost-consuming (Dinger 1998), and error-prone (Branscomb et al. 2013). Thus, an optimization of the searching method is needed to overcome this issue (Dinger 1998). Heuristic algorithms, e.g., Genetic Algorithms (GAs) (Goldberg 1989) have been applied successfully to many engineering problems, e.g., electromagnetic systems design and aircraft control/ aerodynamics (Winter et al. 1996) (Harman & Jones 2001). These potentials facts have triggered some scholars to complement GAs and design synthesis together for the success of systems engineering (Cagan et al. 2005). In the book Engineering Design Synthesis, (Chakrabarti 2002) presents a survey and detailed investigation of the potential applications of Genetic Programming in a design synthesis. One of them is to generate a pattern of solution which is represented in the Unified Modeling Language by Object Management Group (OMG UML TM ) (Chakrabarti 2002). OMG