To solve complex global optimization problems, Artificial Physics Optimization (APO) algorithm is presented based on Physicomimetics framework, which is a population-based stochastic algorithm inspired by physical force. The solutions (particles) sampled from the feasible region of the problems are treated as physical individuals. Each individual has a mass, position and velocity. The mass of each individual corresponds to a user-defined function of the value of an objective function to be optimized. Driven by virtual force, the individuals move towards others with bigger masses, which is an analogy of the particles flying towards the better fitness region. To easily analyze the algorithm, a vector model of APO algorithm is constructed. Based on the vector model, APO algorithm can performs well in diversity if some conditions can be satisfied.