A multi-objective optimization algorithm based on artificial physics optimization (MOAPO) is presented to
solve multi-objective optimization problems. According to the trait of multi-objective problems, by drawing lessons from
aggregating functions method, searching for Pareto optimal set of multi-objective optimization problems is implemented by
using APO algorithm. The inertia weight and gravitation coefficient are dynamic changing to explore the search space more
efficiently. The experimental simulations show that MOAPO is effective for multi-objective problems with a better diversity
compared with NSGA-II algorithm and multi-objective optimization algorithms based on praticle swarm optimization (PSO).