A study on interval to swtich combination of objectives considered in pareto partial dominance moea

Pareto partial dominance MOEA (PPD-MOEA) ranks individuals in the population by using only r (<;m) objectives selected from m objective functions, which makes easier to discriminate solutions than conventional Pareto dominance MOEAs even in many-objective optimization problems (MaOP). In PPD-MOEA, r objective functions are temporally switched among mCr combinations of objectives every Ig generations to optimize all of the objective functions throughout the entire evolution process. To induce maximum search performance of PPD-MOEA, in this work, we study on appropriate setting for the interval parameter Ig that switches combination of objectives. We analyze the search performance of PPD-MOEA by focusing the exhaustivity of mCr kinds of combination in the entire evolution process. Simulation results show that maximum search performance of PPD-MOEA using the optimal r* is achieved when we set the boundary interval Igb, i.e., the exhaustivity is 1.0, in which all the mCr combinations are once utilized in the entire evolution process of PPD-MOEA.