Abstract
In the era of artificial intelligence (AI), many industry sectors, including space exploration, have experienced a shift in the way business is conducted due to the widespread use of AI technologies. In the past few years, AI has become a key tool used to explore the universe in space missions. In this paper, a multi-objective optimal design for payload orbital transfer involving space tethers is proposed based on a computational intelligence-assisted design framework with the artificial wolf pack algorithm (AWPA). Enlightened by the social behaviors of a wolf pack and its swarm intelligence, the AWPA is utilized for optimization problems in which a logsig function randomly obtains assignments for parents and offspring. $Swarmwolf$ , a simulation toolbox with given initial conditions. The proposed method effectively performs optimization tasks based on index of evolutionary pathway trends, has been defined to demonstrate the optimizing process. The results show that the proposed approach works expeditiously for the optimization of space tether model and its application.
Highlights
Reflecting one of the most innovative activities, artificial intelligence (AI) has become essential to the global economy, and its positive effects on society in the context of efficiency are immeasurable and emerging in our daily lives
In the past few years, AI has become a key tool in space activities and the exploration of the universe, and it can be utilized for space communications, navigation, big data analysis, autonomy evaluation, decision support for spacecraft system design [3], space mission operations [4], [5], planetary defense, mapping the moon [6], space exploration [7] and
The original space tether concept came from the ‘space elevator’, which was proposed by a Russian scientist in 1895
Summary
Reflecting one of the most innovative activities, artificial intelligence (AI) has become essential to the global economy, and its positive effects on society in the context of efficiency are immeasurable and emerging in our daily lives. THREE PAIRS OF TREND INDICES To evaluate the performance of the optimization process, and benchmarks for the AWPA algorithm, three pairs of trend indices are introduced : 1) the trend index of moving mean of the average precision (mmAP) and the index of moving mean of the standard deviation (mmSTD), as given in equations (13) and (14), respectively; The trend index of mmAP is defined as a moving average score of the MEAN value of a vector fj, as given in equation (13), in which i = 1, 2, · · · , j, · · · , p, p is the size of the dataset’s population, MEAN (·) is the average function. 3) the trend index of moving min of the average precision (mminAP) and the index of moving min of the standard deviation (mminSTD),as given in equations (17) and (18), respectively
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