scholarly journals Analysis of Some Global Optimization Algorithms for Space Trajectory Design

2010 ◽  
Vol 47 (2) ◽  
pp. 334-344 ◽  
Author(s):  
M. Vasile ◽  
E. Minisci ◽  
M. Locatelli
Keyword(s):  
Global Optimization ◽  
Optimization Algorithms ◽  
Trajectory Design ◽  
Space Trajectory Design

On hybridization of stochastic global optimization algorithms taking into account the features of their architecture

2021 ◽  
Vol 1 ◽  
pp. 113-117
Author(s):  
Dmitry Syedin ◽  
Keyword(s):  
Global Optimization ◽  
Stochastic Optimization ◽  
Optimization Algorithms ◽  
Stochastic Global Optimization ◽  
Stochastic Optimization Algorithms ◽  
Pca Algorithm

The work is devoted to the hybridization of stochastic global optimization algorithms depending on their architecture. The main methods of hybridization of stochastic optimization algorithms are listed. An example of hybridization of the algorithm is given, the modification of which became possible due to taking into account the characteristic architecture of the M-PCA algorithm.

Results of two global optimization algorithms applied to a problem in biomechanics

2010 ◽  
Author(s):  
Nicholas R. Radcliffe ◽  
David R. Easterling ◽  
Layne T. Watson ◽  
Michael L. Madigan ◽  
Kathleen A. Bieryla
Keyword(s):  
Global Optimization ◽  
Optimization Algorithms

scholarly journals Multi-agent collaborative search: an agent-based memetic multi-objective optimization algorithm applied to space trajectory design

2011 ◽  
Vol 225 (11) ◽  
pp. 1211-1227 ◽  
Author(s):  
M Vasile ◽  
F Zuiani
Keyword(s):  
Selection Criterion ◽  
Search Space ◽  
Trajectory Design ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Agent Based ◽  
Multi Objective ◽  
Collaborative Search ◽  
Multi Agent ◽  
Space Trajectory Design

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.

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