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

M. Vasile, F. Zuiani

Research output: Contribution to journalArticle

30 Citations (Scopus)

Abstract

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.

LanguageEnglish
Pages1211-1227
Number of pages17
JournalProceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering
Volume225
Issue number11
Early online date5 Sep 2011
DOIs
Publication statusPublished - Nov 2011

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Multiobjective optimization
Multi-objective Optimization
Optimization Algorithm
Trajectories
Trajectory
Heuristics
Pareto
Multiple Objective Optimization
Pareto Set
NSGA-II
Evolution Strategies
Sorting algorithm
Sorting
Particle swarm optimization (PSO)
Search Space
Particle Swarm Optimization
Genetic algorithms
Genetic Algorithm
Design
Demonstrate

Keywords

  • multiobjective optimization
  • agent-based approach
  • trajectory optimisation

Cite this

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