A multi-agent based evolutionary algorithm in non-stationary environments.
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.
Citation : Yan, Y. et al. (2008) A multi-agent based evolutionary algorithm in non-stationary environments. In: Proceedings of the 2008 IEEE Congress on Evolutionary Computation, Hong Kong, 1-6 June. New York: IEEE, pp. 2967-2974.
ISBN : 978-1-4244-1822-0
Research Group : Centre for Computational Intelligence
Research Institute : Institute of Artificial Intelligence (IAI)
Peer Reviewed : Yes