An Inflationary PSwarm Algorithm for Space Trajectory Design

Author(s):  
Luo Zhiqing ◽  
Dai Guangming ◽  
Zhan Wei ◽  
Peng Lei
Author(s):  
M Vasile ◽  
F Zuiani

This article presents an algorithm for multi-objective optimization that blends together a number of heuristics. A population of agents combines heuristics that aim at exploring the search space both globally and in a neighbourhood of each agent. These heuristics are complemented with a combination of a local and global archive. The novel agent-based algorithm is tested at first on a set of standard problems and then on three specific problems in space trajectory design. Its performance is compared against a number of state-of-the-art multi-objective optimization algorithms that use the Pareto dominance as selection criterion: non-dominated sorting genetic algorithm (NSGA-II), Pareto archived evolution strategy (PAES), multiple objective particle swarm optimization (MOPSO), and multiple trajectory search (MTS). The results demonstrate that the agent-based search can identify parts of the Pareto set that the other algorithms were not able to capture. Furthermore, convergence is statistically better although the variance of the results is in some cases higher.


2004 ◽  
Vol 52 (2) ◽  
pp. 329-336 ◽  
Author(s):  
Roberto Mir ◽  
Andres Guesalaga ◽  
Juan Spiniak ◽  
Marcelo Guarini ◽  
Pablo Irarrazaval

2003 ◽  
Vol 21 (7) ◽  
pp. 755-764 ◽  
Author(s):  
Sebastian Sabat ◽  
Roberto Mir ◽  
Marcelo Guarini ◽  
Andres Guesalaga ◽  
Pablo Irarrazaval

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