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Hard computing based optimization algorithms usually require a lot of computational resources and generally do not have the ability to arrive at the global optimum solution. Soft computing algorithms on the other hand negate these deficiencies, by allowing for reduced computational loads and the ability to find global optimal solutions, even for complex cost surfaces. This paper presents two numerical case studies where a particle swarm optimization (PSO) algorithm is applied to biomedical problems. In particular, the problem of identifying the rupture force for leukocyte adhesion molecules and the problem of finding the correct control parameters of a robotic hand, are addressed. Simulation results indicate that PSO is a feasible alternative to the computational expensive hard computing algorithms.