Layout Optimization of Microsatellite Components Using Genetic Algorithm

Abstract

Placing the components into a container that is normally known as layout optimization problem belongs to NP-hard Keywords: layout optimization, genetic algorithm, microsatelliteCopyright © 2017 Universitas Ahmad Dahlan. All rights reserved. IntroductionThe problem in making a layout solution is commonly known as a layout optimization problem (LOP), which leads to the non-deterministic polynomial-time hard (NP-hard) in term of computational complexity. LOP is commonly used in engineering applications that pay attention to the physical placement of an instrument or device into a container [1]. Layout optimization is one of the key techniques to improve the performance of a satellite due to a direct impact on the structure, lifetime, cost of assembly, and maintenance of the overall system. Harmonious and reasonable layout is an essential property for the success of the satellite's mission [2]. The layout becomes very critical for microsatellites since it has limitation in space and weight.As part of the configuration synthesis, the layout of the satellite components should consider the design drivers, attitude control requirements and the allocation of satellite mass and inertia [3]. The design is generally driven by a mission payload and spacecraft launcher. So the research question is how to produce the optimal layout of satellite components that match the requirements of mission payloads, launcher and spacecraft attitude control.One of the common algorithms to solve the LOP is a genetic algorithm (GA). Shoukun, et al [4] Distinguishes LOP settlement using GA into two methods. The first method is based on the coordinates of the object with a binary or decimal encoding while the second method is based on the order of placement. According simulations and design experiences, the first method often requires a complicated design of fitness function and may be easily trapped in local optimum solution.The second method combines GA with other algorithms to generate patterns of the placement that overcomes the shortcomings of the first method. GA explores the order of placement to produce the best solution based on a predefined placement algorithm. Recent research generally uses this second method [5][6][7][8][9][10]. Xu, et al [5] combines the order-based positioning technique (OPT) and GA to obtain the optimal layout of the satellite components. For n ccomponents, there exist n! = n × (n − 1) × · · · × 2 × 1 sequences. A genetic algorithm is

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