A study on the effect of Vmax in Artificial physics optimization algorithm with high dimension

Velocity threshold v_max is an important parameter of Artificial physics optimization. Different from other parameters, it affects the algorithm performance by restricting the moving size and direction of each particle. Because of the complex optimisation problems, the proper v_max setting may provide a reasonable solution within an allowed generation. However, up to now, there are only few scholars who are concerned in this problem. Therefore, in this paper, the authors investigate two selection principles of v_max (a canstant v_max and an adaptive v_max) with high dimension on numerical optimisation problems. To make a deep insight, the test suit consists of three different type benchmarks: unimodel, multi-modal functions with a few local optima and multi-modal functions with many local optima. Simulation results show an adaptive v_max may generally obtain the satisfied solution within the allowed iterations.