The Directed Multi-Objective Estimation Distribution Algorithm (D-MOEDA)

Abstract

Improvement Direction Mapping (IDM) methods have been applied as a local search strategy to hybridize global search algorithms. A natural question is whether this concept could be applied within a global search scheme, so that the stochastic search operators are directed toward promising regions, promoting a more efficient search. This paper introduces a novel Multi-Objective Evolutionary Algorithm (MOEA) that incorporates the IDM into the reproduction operator of an Estimation of Distribution Algorithm (EDA). In this proposal, the search directions of the IDM based on aggregation functions are used to directly steer the search process of a multi-objective evolutionary algorithm based on decomposition, by orienting a local probability distribution towards a search direction, the proposal intends to steer solutions toward the Pareto front (PF) of the Multi-Objective Optimization Problem (MOP), exploiting the search features of the aggregation functions. The proposal is tested using a set of well-known benchmark MOPs and compared to state of the art MOEAs. Results showed statistical evidence about the importance of the orientation of the search probability distribution to improve the convergence to the Pareto front.