Particle swarm optimization (PSO) is a population-based stochastic optimization technique. It shares many similarities
with evolutionary computation techniques such as Genetic Algorithms (GA). But compared with GA, it has simpler
model, fewer parameters, higher intelligence, faster computation, which makes it attractive to some researchers. This
paper presents a new particle swarm optimization based on uniform design and inertia mutation (UMPSO). It uses
uniform designs (UD) to initialize particles, which makes some particles stay at or near the position where the global
optimal solution stays with more probability. So the new PSO can find global optimal solution with more probability and
more speed. Particles can keep diverse through mutating inertia particle with the probability of 1 in the process of
evolution, which makes the new PSO find more precise solution. The results of simulation verify that the new PSO can
find more precise solution with higher speed than the standard one.
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