Abstract
The pursuit of excellent performance in meta-heuristic algorithms has led to a myriad of extensive and profound research and achievements. Notably, many space mission planning problems are solved with the help of meta-heuristic algorithms, and relevant studies continue to appear. This paper introduces a hierarchical optimization frame in which two types of particles—B-particles and S-particles—synergistically search for the optima. Global exploration relies on B-particles, whose motional direction and step length are designed independently. S-particles are for fine local exploitation near the current best B-particle. Two specific algorithms are designed according to this frame. New variants of classical benchmark functions are used to better test the proposed algorithms. Furthermore, two spacecraft trajectory optimization problems, spacecraft multi-impulse orbit transfer and the pursuit-evasion game of two spacecraft, are employed to examine the applicability of the proposed algorithms. The simulation results indicate that the hierarchical optimization algorithms perform well on given trials and have great potential for space mission planning.
Highlights
In the last twenty years, the rapid development of meta-heuristic optimization algorithms has promoted the extraordinary progress of widespread engineering applications, including variable selection in chemical modeling [1], pattern recognition [2], path planning of UAVs [3], feature selection [4] and data clustering [5]
The results show that HOA-2 found the best solutions for the three cases, which reflects the superiority of the proposed methods
Because the number of S-particles of HOA-1 should be equal to the problem dimension N, and that of HOA-2 can be properly set to 4N, the amount of computation of HOA-1 is noticeably less than other algorithms, which explains the performance degradation of HOA-1
Summary
In the last twenty years, the rapid development of meta-heuristic optimization algorithms has promoted the extraordinary progress of widespread engineering applications, including variable selection in chemical modeling [1], pattern recognition [2], path planning of UAVs [3], feature selection [4] and data clustering [5]. In order to effectively apply the meta-heuristic algorithms to the space mission planning, on the one hand, constructing an appropriate optimization problem model is of vital importance. It is valuable to improve the performance of algorithms, which is the focus of this paper. Compared with traditional mathematical programming methods, meta-heuristic algorithms attract the attention of a large number of scholars due to four characteristics: simplicity, flexibility, derivation-free mechanism and local optima avoidance [10]. New algorithms are most noteworthy for their new mechanisms to provide inspiration for other methods
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