A new framework of change response for dynamic multi-objective optimization

Combining response strategies into multi -objective evolutionary algorithms (MOEAs) for dynamic multiobjective optimization problems (DMOPs) is very popular. However, most of them hardly focus on DMOPs via enhancing the operator's searching ability of MOEAs. We present a new framework of change response called MOEA/D-HSS. When a change is detected, MOEA/D-HSS updates and assesses saved historical information, computing the intensity of change on the decision variables and the similarity between the current environment and historical ones. Hybrid search strategies (HSS) adaptively adjust the searching range of the population in each generational cycle based on the knowledge above, which has a great chance of discovering new promising regions. HSS is integrated into the variation operator of MOEA based on decomposition (MOEA/D-DE) to enhance its search ability. We take into account that the historical information may be useless references in the later stage of the evolution. Thus, the frequency of HSS usage is gradually decreased in every time interval to balance the population's convergence and diversity. Experimental results demonstrate that MOEA/S-HSS is very competitive on most benchmark problems compared with other state-of-the-art algorithms.