The convergence analysis and parameter selection of Artificial Physics Optimization algorithm

Artificial Physics Optimization (APO) algorithm is a population-based stochastic algorithm based on Physicomimetics framework. The algorithm utilizes an attraction-repulsion mechanism to move individuals toward optimality. The convergence analysis of APO algorithm is made theoretically. By regarding each individual's position on each evolutionary step as a stochastic vector, APO algorithm determined by non-negative real parameter tuple {w, G} is analyzed using discrete-time linear system theory. The convergent condition of APO algorithm and corresponding parameter selection guidelines are derived. The simulation results show that the convergent condition is effective in guiding the parameter selection of APO algorithm and can help to explain why those parameters work well.