Abstract
With the intrinsic properties of constraint satisfaction problems (CSPs) in mind, several behaviors are designed for agents by making use of the ability of agents to sense and act on the environment. These behaviors are controlled by means of evolution, so that multiagent evolutionary algorithm for constraint satisfaction problems (MAEA-CSPs) results. To overcome the disadvantages of the general encoding methods, the minimum conflict encoding is also proposed. The experiments use 250 benchmark CSPs to test the performance of MAEA-CSPs, and compare it with four well-defined algorithms. The results show that MAEA-CSPs outperforms the other methods. In addition, the effect of the parameters is analyzed systematically.
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Zhong, W., Liu, J., Jiao, L. (2005). An Agent Model for Binary Constraint Satisfaction Problems. In: Raidl, G.R., Gottlieb, J. (eds) Evolutionary Computation in Combinatorial Optimization. EvoCOP 2005. Lecture Notes in Computer Science, vol 3448. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-31996-2_24
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DOI: https://doi.org/10.1007/978-3-540-31996-2_24
Publisher Name: Springer, Berlin, Heidelberg
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