Different force laws driving artificial physics optimisation algorithm for constrained optimisation problem

Inspired by physical force, Artificial Physics Optimisation APO algorithm is a novel stochastic based on a physicomimetics framework. Driven by virtual force, a population of sample individuals searches for a global optimum in the problem space. The force law is a key problem associated with the performance of APO algorithm significantly. In the paper, an APO algorithm with the Feasibility and Dominance FAD method FAD-APO is employed to solve constrained optimisation problems. Three different force laws are constructed between the feasible individuals and infeasible individuals, which drive all individuals to search for a global optimum in the constrained problem space. Simulation results show that FAD3-APO algorithm may generally perform better than FAD1-APO and FAD2-APO; it is the most stable and effective among the three versions of FAD-APO algorithms. Meanwhile, a comparison with other population-based heuristics shows that the FAD-APO algorithm is competitive on some test function.