This paper combines Artificial Physics and Base Optimization Algorithms to propose a Modified Artificial Physics Optimization for multi-parameter function minimization. Prominent features of Base Optimization lies in using standard arithmetic operators with displacement parameters to reach the optimal solution. Artificial Physics benefit from the success of physicomimetics and shows an evident predominance in search of global optima. This study uses distinctive advantages of two algorithms to propose an efficient optimization for multi-parameter functions. In order to reveal effects of mass function approach of Artificial Physics and cost functions of the optimization process, the proposed method executes various mass-cost function combinations synchronously. The effectiveness of the proposed algorithm is demonstrated on integer-order and fractional-order controller tunings for integer- and fractional-order models. Priority of the proposed optimization method is presented by comparing with known optimization algorithms.