A new distributed particle swarm optimization algorithm for constraint reasoning
KES'10 Proceedings of the 14th international conference on Knowledge-based and intelligent information and engineering systems: Part II
Brief paper: An improved differential evolution algorithm for the task assignment problem
Engineering Applications of Artificial Intelligence
A hybrid discrete particle swarm algorithm for hard binary CSPs
ICNC'06 Proceedings of the Second international conference on Advances in Natural Computation - Volume Part II
A hybrid particle swarm optimization for binary CSPs
ICIC'06 Proceedings of the 2006 international conference on Computational Intelligence and Bioinformatics - Volume Part III
A self-adaptive differential evolution algorithm for binary CSPs
Computers & Mathematics with Applications
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We introduce a discrete particle swarm (PS) algorithm for solving binary constraint satisfaction problems (CSPs). It uses information about the conflicts between the variables to compute the velocity of the individual particles. We tune the parameters of the PS algorithm to a quasi-optimal setting and study the behavior of the algorithm under changes to this setting. The PS algorithm is then empirically compared with ant colonies (which also belong to the swarm intelligence class) and genetic algorithms on a whole range of randomly generated binary CSP instances.