Multi-objective evolutionary algorithm with non-stationary search space

Existing multi-objective (MO) evolutionary algorithms apply a fixed search space in the parameter domain. This approach needs a good guess or a-prior knowledge of a promising search area since a wrongly specified range of search space often leads to poor solutions. To address the issue, this paper proposes a novel approach of adaptive search space for MO optimization. Through the method of shrinking and expanding, the technique is capable of directing the evolution to reach more promising search regions even if it is not covered in the initial search space. The role of the inductive learning process is also introduced, which is performed by an exploratory multi-objective evolutionary algorithm to enhance the search from being trapped in local optima as well as to promote the population diversity along the discovered Pareto-optimal front. Features of the proposed approach are experimented and investigated upon benchmark MO optimization problems