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

Abstract:

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 mC r 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. © 2010 TSI Press.

Año de publicación:

2010

Keywords:

• Pareto partial dominance
• MOEA
• Interval generation
• many-objective optimization

Fuente:

scopus