# Optimization of Multi-Mission CubeSat Constellations with a Multi-Objective Genetic Algorithm
## 1 Introduction  
The demand for space-based Earth observation data has increased in recent years. Earth observation missions are vital for understanding and monitoring global change. Small satellites, especially CubeSats [1; 2; 3], have gained most attention for performing these missions. In the past decade, the space industry has shifted towards developing small satellites, with CubeSats leading the way due to modular and standardized design. CubeSats are becoming a popular option for implementing large constellation systems and are a great starting point for developing countries looking to establish a space industry. The low cost and fast development cycle of CubeSats make them particularly attractive for Earth observation applications. Several missions have proven the capability of CubeSat for Earth observation missions [4; 5; 6]. Recently, disaster monitoring and management systems have become a necessity with the frequent occurrence of natural disasters. Early forecasting is critical in disaster management because many climate-related disasters can be predicted ahead of time. The early forecast allows an action to be taken ahead of time, reducing the risks and consequences of the disaster. Rapid and continuous data collection is essential for disaster monitoring and management systems. For regions with limited infrastructure, a CubeSat constellation can be used as an alternative to increasing the data collection rate for remote places spread across a large area [7; 8]. This study proposes a multi-mission CubeSat constellation system for Earth observation and climate data collection from regional data centers. Multi-mission refers to implementing a single system to perform optimized multiple mission objectives. Based on the literature review, there have been few studies on the design and optimization of CubeSat constellation systems. [PERSON] et al. [7] conducted a case study fora CubeSat constellation system for disaster management in remote areas. Furthermore, [PERSON] et al. [1] studied the analysis and design of a CubeSat constellation for continuous monitoring of the South Mediterranean Sea. [PERSON] et al. [9] developed a CubeSat constellation to observe the temporal variability of the atmosphere. The study compares different orbital configurations for a CubeSat constellation to minimize revisit time for global coverage. However, none of these studies used optimization techniques in the design of CubeSat constellations. [PERSON] et al. [8] developed an optimization-based framework to design a CubeSat constellation to monitor hurricanes. The study used a two-step optimization method to design a constellation for a selected region of interest. Other studies proposed multi-objective optimization for satellite constellation optimization. [PERSON] et al. [10] studied the optimization of small satellite constellations using a multi-objective genetic algorithm (MOGA) for continuous mutual regional coverage. However, the optimization focused on optimizing a constellation for a single purpose, whereas our study proposes a new approach for multi-mission optimization. [PERSON] et al. [11] employed multi-objective optimization approaches to design low Earth orbit (LEO) navigation augmentation constellations. The study modeled the LEO constellation and used the multi-objective particle swarm optimization (MOPSO) algorithm. [PERSON] et al. [12] studied the multi-objective optimization design of an extended Walker constellation for global coverage. The main focus of the research was on the optimization design of a Walker constellation configuration by using multi-objective evolutionary algorithms. However, to the best of the authors' knowledge, there have been no studies to optimize a CubeSat constellation system for a multi-mission design. Therefore, in this study, we propose an optimized CubeSat constellation system to satisfy global Earth observation and regional disaster monitoring goals. A single system in the CubeSat constellation will perform both Earth observation and disaster monitoring missions.