NDLI: Multi Agent Collaborative Search based on Tchebycheff decomposition

Content Provider	Springer Nature Link
Author	Zuiani, Federico Vasile, Massimilia
Copyright Year	2013
Abstract	This paper presents a novel formulation of Multi Agent Collaborative Search, for multi-objective optimization, based on Tchebycheff decomposition. A population of agents combines heuristics that aim at exploring the search space both globally (social moves) and in a neighborhood of each agent (individualistic moves). In this novel formulation the selection process is based on a combination of Tchebycheff scalarization and Pareto dominance. Furthermore, while in the previous implementation, social actions were applied to the whole population of agents and individualistic actions only to an elite subpopulation, in this novel formulation this mechanism is inverted. The novel agent-based algorithm is tested at first on a standard benchmark of difficult problems and then on two specific problems in space trajectory design. Its performance is compared against a number of state-of-the-art multi-objective optimization algorithms. The results demonstrate that this novel agent-based search has better performance with respect to its predecessor in a number of cases and converges better than the other state-of-the-art algorithms with a better spreading of the solutions.
Starting Page	189
Ending Page	208
Page Count	20
File Format	PDF
ISSN	09266003
Journal	Computational Optimization and Applications
Volume Number	56
Issue Number	1
e-ISSN	15732894
Language	English
Publisher	Springer US
Publisher Date	2013-03-28
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Agent-based optimization Multi-objective optimization Memetic strategies Optimization Operations Research, Management Science Operations Research/Decision Theory Statistics Convex and Discrete Geometry
Content Type	Text
Resource Type	Article
Subject	Applied Mathematics Control and Optimization Computational Mathematics

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