Multiobjectivization via Helper Objectives (MHO) is the method for reformulating the original problem into a single optimization problem with more objectives by adding helper objectives. This paper proposes a method for solving multi-objective optimization problems inspired by MHO by transforming the problem by adding objective functions. The helper objective employed in the proposed is of three types: a function that adds up all the objectives of the original problem, a periodic function with global optima at equal space, and a function that partially simplifies the objectives of the original problem. The function that adds up all the objectives of the original problem aims to create new search pressure in the objective function space. The function with equally spaced global optima aims to keep the solution set diverse in the objective function space. The function that partially simplifies the original problem aim to escape from the local optima contained in the original problem. These helper objectives are added to the objective variables of the original problem. Adding helper objectives is expected to improve convergence to the global optima and diversity of the set of solutions. Through comparative experiments on the proposed method using the WFG problems as benchmark problems, the experimental results show the improvement of (1) the diversity of the solution set in the objective function space and (2) the convergence speed to the Pareto front. Multiobjectivization via Helper Objectives (MHO) is the method for reformulating the original problem into a single optimization problem with more objectives by adding helper objectives.