A Swarm Intelligence Method Applied to Resources Allocation Problem

This paper presents a suggested method to solve an allocation problem with a swarm intelligence method. The application of swarm intelligence has to be discrete. This allocation problem can be modelled like a multiobjective optimization problem where we want to minimize the time and the distance of the total travel in a logistic context. To treat such a problem we are presenting a Discrete Particle Swarm Optimization (DPSO) method in which we adapt the movement of the particles according to the constraints of our application. To test this algorithm, we create a problem whose solution is already known. The aim of this study is to check whether this adapted DPSO method succeeds in providing an optimal solution for general allocation problems and to evaluate the efficiency of convergence towards the solution. By the way, for comparison purpose, we also applied evolutionary game techniques on the same example. Tentative allocation plans are strategies. Evolutionary game theory studies the behavior of large populations of agents who repeatedly engage in strategic interactions. Changes in behavior in these populations are driven by natural selection via differences in birth and death rates. We focused on replicator dynamic which is a fundamental deterministic evolutionary dynamic for games.

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