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Satellite constellation design using the q-G global optimization method

机译:使用q-G全局最优化方法的卫星星座设计

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The field of optimization is interdisciplinary in nature, and it is an indispensable tool in various fields of science and engineering. Computational intelligence-based techniques, such as neural networks, simulated annealing, stochastic machines, genetic algorithms and others, have been proven to be effective in solving global optimization problems. In this work, the q-Gradient (q-G) method is applied for solving a satellite constellation design problem for two regional navigation satellite systems, one for covering the Brazilian territory and the other for South America. The q-G method is a global optimization algorithm based on the concepts of q-calculus and simulated annealing. The main idea behind the q-G method is the use of the q-gradient vector of the objective function as the search direction. The q-gradient vector is a generalization of the classical gradient vector based on the concept of Jackson's derivative and its use provides the algorithm an effective mechanism for escaping from local minima. The algorithm has three free parameters, and it is implemented so that the search process gradually shifts from global exploration in the beginning to almost local search in the end. The q-G method was applied on six 10-D multimodal test functions, and its performance was compared with eleven Evolutionary Algorithms (EAs). The q-G method performed well against the EAs arriving in forth position in a direct comparison with them. For the satellite constellation design problem, we are using the basic design assumption of using four satellites in geosynchronous orbits for covering both Brazil and South America. This optimization problem has 12 design variables (12-D) and includes calculations of the Geometric Dilution of Precision (GDOP) as the main metric for the design of the satellite constellations. Comparison of the results with a previous study for the Brazilian constellation indicates that the q-G method is able to solve this problem and that there is consi- erable room for improvement with the use of an additional satellite for the South America case study.
机译:优化领域本质上是跨学科的,是科学和工程学各个领域必不可少的工具。神经网络,模拟退火,随机机器,遗传算法等基于计算智能的技术已被证明可有效解决全局优化问题。在这项工作中,q梯度(q-G)方法用于解决两个区域导航卫星系统的卫星星座设计问题,一个覆盖巴西领土,另一个覆盖南美。 q-G方法是基于q演算和模拟退火概念的全局优化算法。 q-G方法背后的主要思想是使用目标函数的q梯度向量作为搜索方向。 q梯度向量是基于Jackson导数概念的经典梯度向量的推广,它的使用为算法提供了一种避免局部极小值的有效机制。该算法具有三个自由参数,并且实现了该算法,以使搜索过程从最初的全局搜索逐渐过渡到最后的几乎本地搜索。将q-G方法应用于六个10-D多峰测试函数,并将其性能与十一个进化算法(EA)进行比较。与直接比较的EA相比,q-G方法在EA到达第四位置时表现良好。对于卫星星座设计问题,我们使用的基本设计假设是在地球同步轨道上使用四颗卫星来覆盖巴西和南美。该优化问题具有12个设计变量(12-D),并且包括对精度几何稀释度(GDOP)的计算,这是设计卫星星座图的主要指标。将结果与先前对巴西星座的研究相比较表明,q-G方法能够解决此问题,并且在南美的案例研究中使用额外的卫星还有很大的改进空间。

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