Consumer's behavior can be modeled using a utility function that allows for measuring the \emph{success} of an individual's decision, which consists of a tuple of goods an individual would like to buy and the hours of work necessary to pay for this purchase and consumption. The success of such a decision is measured by a utility function which incorporates not only the purchase and consumption of goods, but also leisure, which additionally increases the utility of an individual. In this paper, we present a new agent based social simulation in which the decision finding process of consumers is performed by Particle Swarm Optimization (PSO), a well-known swarm intelligence method. PSO appears to be suitable for the underlying problem as it is based on previous \emph{and} current information, but also contains a stochastic part which allows for modeling the uncertainty usually involved in the human decision making process. We investigate the adequacy of different bounding strategies that map particles violating the underlying budget constraints to a feasible region. Experiments indicate that one of these bounding strategies is able to achieve very fast and stable convergence for the given optimization problem. However, an even more interesting question refers to adequacy of these bounding strategies for the underlying social simulation task.

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