An Evolution Path-Based Reproduction Operator for Many-Objective Optimization

The many-objective evolutionary algorithms generally make use of a set of well-spread reference vectors to increase the selection pressure toward the Pareto front in high-dimensional objective space. However, few studies have been reported on how to generate new solutions toward the Pareto set (PS) in the decision space with the help of these reference vectors. To fill this gap, we develop a novel reproduction operator based on the differential evolution. The main idea is using the evolution paths to depict the population movement and predict its tendency. These evolution paths are used to create potential solutions, and thus, accelerate the convergence toward the PS. Furthermore, a self-adaptive mechanism is introduced to adapt related parameters automatically. This operator is implemented in two well-known many-objective evolutionary algorithm frameworks. The experimental results on 20 widely used benchmark problems show that the proposed operator is able to strengthen the performance of the original algorithms in handling many-objective optimization problems.