A memetic algorithm with one-step local search to guide diversity increase in Dynamic Multiobjective problems

The Multiobjective Evolutionary Algorithms (MOEAs) are often applied to solve difficult optimization problems, but the dynamic case is even more special. During the optimization, if the environment is changed, a dynamic algorithm must temporarily increase the exploration and decrease the exploitation to generate genetic diversity and then be capable of handling the new behavior of the environment. A technique to increase the diversity may impose an extra delay to such an algorithm that needs to be fast because the new changes may arrive at any time. This paper proposes a model that adds a mutation operator based on gradient, which has the purpose of generating guided diversity to respond to changes in the environment, hence it can accelerate the convergence of the algorithm as a whole. The memetic mutation operator was inserted in the SPEA2 to respond more efficiently to the modifications. Simulations of the proposed model (called Gradient Guided SPEA2, GSPEA2) were carried out for the benchmarks FDA1, FDA3, and DIMP1. Considering the metrics VD_weighted and MS_weighted, performance of SPEA2 with GSPEA2 was compared with other four dynamic MOEAs. Results suggest that this is a promising approach.