scholarly journals Multi-objective global optimization for interplanetary space trajectory design

2019 ◽  
Author(s):  
Martin Schlueter ◽  
Masaharu Munetomo
Keyword(s):  
Global Optimization ◽  
Interplanetary Space ◽  
Trajectory Design ◽  
Multi Objective ◽  
Space Trajectory Design

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.

scholarly journals A Kriging-Assisted Multi-Objective Constrained Global Optimization Method for Expensive Black-Box Functions

Mathematics ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 149
Author(s):  
Yaohui Li ◽  
Jingfang Shen ◽  
Ziliang Cai ◽  
Yizhong Wu ◽  
Shuting Wang
Keyword(s):  
Global Optimization ◽  
Pareto Frontier ◽  
Kriging Model ◽  
Optimization Method ◽  
Function Evaluation ◽  
Sampling Point ◽  
Constrained Global Optimization ◽  
Multi Objective ◽  
Sample Data ◽  
Sampling Points

The kriging optimization method that can only obtain one sampling point per cycle has encountered a bottleneck in practical engineering applications. How to find a suitable optimization method to generate multiple sampling points at a time while improving the accuracy of convergence and reducing the number of expensive evaluations has been a wide concern. For this reason, a kriging-assisted multi-objective constrained global optimization (KMCGO) method has been proposed. The sample data obtained from the expensive function evaluation is first used to construct or update the kriging model in each cycle. Then, kriging-based estimated target, RMSE (root mean square error), and feasibility probability are used to form three objectives, which are optimized to generate the Pareto frontier set through multi-objective optimization. Finally, the sample data from the Pareto frontier set is further screened to obtain more promising and valuable sampling points. The test results of five benchmark functions, four design problems, and a fuel economy simulation optimization prove the effectiveness of the proposed algorithm.

scholarly journals Multi-Objective Bayesian Global Optimization using expected hypervolume improvement gradient

2019 ◽  
Vol 44 ◽  
pp. 945-956 ◽  
Author(s):  
Kaifeng Yang ◽  
Michael Emmerich ◽  
André Deutz ◽  
Thomas Bäck
Keyword(s):  
Global Optimization ◽  
Multi Objective ◽  
Bayesian Global Optimization
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