MOEA/D with Baldwinian learning inspired by the regularity property of continuous multiobjective problem

The traditional reproduction operators, which are originally designed for single-objective optimization, are directly adopted in most state-of-the-art multi-objective evolutionary algorithms (MOEAs). However, these reproduction operators might not be suitable for multiobjective optimization problems (MOPs) due to the regularity property of continuous MOP, and that is to say its Pareto optimal set in the decision space is generally piecewise continuous manifold rather than a set of independent points. Few researches have used this regularity property of continuous MOP to help design their algorithms. In this paper, based on the regularity property, a Baldwinian learning operator is incorporated into the framework of MOEA/D (multi-objective evolutionary algorithm based on decomposition) and thereby we propose MOEA/D/BL. The Baldwinian learning operator obtains the evolving information based on the learned distribution model of a current population. It constructs a candidate descent direction based on the learned distribution model and the evolving history of the parent individuals. Experimental results on twenty-three popular test problems show that the proposed algorithm performs better than or as well as four other compared algorithms. It also experimentally demonstrates that the proposed Baldwinian learning operator can accelerate the convergence of solutions.