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Title: A multi-agent based evolutionary algorithm in non-stationary environments
Authors: Yan, Y
Wang, H
Wang, D
Yang, S
Wang, DZ
Keywords: Constraint optimization;Diversity methods;Evolutionary computation;Feedback;Lattices;Multiagent systems;Organisms;Space stations;Statistics;Testing
Issue Date: 2008
Publisher: IEEE
Citation: The 2008 IEEE Congress on Evolutionary Computation, Hong Kong: 2967 - 2974, 01 - 06 Jun 2008
Abstract: In this paper, a multi-agent based evolutionary algorithm (MAEA) is introduced to solve dynamic optimization problems. The agents simulate living organism features and co-evolve to find optimum. All agents live in a lattice like environment, where each agent is fixed on a lattice point. In order to increase the energy, agents can compete with their neighbors and can also acquire knowledge based on statistic information. In order to maintain the diversity of the population, the random immigrants and adaptive primal dual mapping schemes are used. Simulation experiments on a set of dynamic benchmark problems show that MAEA can obtain a better performance in non-stationary environments in comparison with several peer genetic algorithms.
Description: This article is posted here with permission of IEEE - Copyright @ 2008 IEEE
ISBN: 978-1-4244-1822-0
Appears in Collections:Publications
Computer Science
Dept of Computer Science Research Papers

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