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In this paper we are concerned with the simulation of crowds in built environments, where obstacles play a role in the dynamics and in the interactions among pedestrians. First of all, we review the state-of-the-art of the techniques for handling obstacles in numerical simulations. Then, we introduce a new modelling technique which guarantees both impermeability and opacity of the obstacles, and does not require ad hoc runtime interventions to avoid collisions. Most important, we solve a complex optimization problem by means of the Particle Swarm Optimization method in order to exploit the so-called Braess's paradox. More precisely, we reduce the evacuation time from a room by adding in the walking area multiple obstacles optimally placed and shaped.

The aim of this paper is to solve optimal design problems for industrial\ud applications when the objective function value requires the evaluation of expensive\ud simulation codes and its first derivatives are not available. In order to achieve this goal\ud we propose two new algorithms that draw inspiration from two existing approaches:\ud a filled function based algorithm and a Particle Swarm Optimization method. In order\ud to test the efficiency of the two proposed algorithms, we perform a numerical compar-\ud ison both with the methods we drew inspiration from, and with some standard Global\ud Optimization algorithms that are currently adopted in industrial design optimization.\ud Finally, a realistic ship design problem, namely the reduction of the amplitude of the\ud heave motion of a ship advancing in head seas (a problem connected to both safety\ud and comfort), is solved using the new codes and other global and local derivative-free optimization methods. All the numerical results show the effectiveness of the\ud two new algorithms

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