Global Multiobjective Optimization via Estimation of Distribution Algorithm with Biased Initialization and Crossover

Multiobjective optimization problems with many local Pareto fronts is a big challenge to evolutionary agorithms. In this paper, two operators, biased initialization and biased crossover, axe proposed to improve the global search ability of RM-MEDA, a recently proposed multiobjective estimation of distribution algorithm. Biased initialization inserts several globally Pareto optimal solutions into the initial population; biased crossover combines the location information of some best solutions found so far and globally statistical information extracted from current population. Experiments have been conducted to study the effects of these two operators.