Dynamic multiobjective optimization via an improved r-dominance relation and a novel prediction approach

When decision makers are only interested in a portion of the Pareto optimal front (PF) in dynamic multiobjective optimization problems (DMOPs), dynamic multiobjective evolutionary algorithms (DMOEAs) need to search only for the PF portion of interest to decision makers. However, this is challenging for most existing DMOEAs, as they are designed to search the entire PF, overlooking the preferences of decision makers. Therefore, we present a novel dynamic multiobjective optimization algorithm based on decision makers’ preference information, involving an improved r-dominance relation and a response strategy. It focuses on searching for the preference solutions (the region of interest) according to the decision makers’ preference information in dynamic multiobjective optimization. The improved r-dominance relation adapts the angle to measure the closeness between the solution and the preference information, which solves the convergence problem of the r-dominance relation since the original r-dominance relation has difficulty converging to the true PF when the preference information is located in the feasible objective region. The prediction mechanism is based on the movement of the population’s special points; it helps the population make adjustments in its moving direction and step size towards the new PF when a change is detected. Experimental results show that the proposed algorithm is efficient for dynamic multiobjective optimization and is competitive compared to state-of-the-art methods.