Abstract: Inspired by artificial physics (AP) approach, a framework of artificial physics optimization (APO) algorithm is presented to solve global optimization problem. Comparing the similarities and differences of physical individual and ideal particle, we construct a mapping between AP approach and a population-based optimization algorithm. APO algorithm is a population-based stochastic search method. In the framework, each sample point can be treated as a physical individual with the properties of mass, velocity and position. The mass of each individual corresponds to a user-defined function of the value of an objective function to be optimized. The better the objective function value, the bigger the mass, and then the higher the magnitude of attraction. The virtual forces among individuals are defined by Newtons gravity law and an attraction-repulsion rule is established among them, which makes the individual attract others with the worse fitness and repel others with the better fitness, and the individual with the best fitness attracts all the others, whereas it is never repelled or attracted by others. The attractive-repulsive rule can be treated as the search strategy in optimization algorithm which will lead the population to search the better fitness region of the problem. The simulation results indicate the validity of the approach.