Weighted pointwise prediction method for dynamic multiobjective optimization

Abstract Prediction methods are useful tools for dynamic multiobjective optimization (DMO), especially if the changes roughly follow some patterns. Multi-model prediction methods, in particular, may capture different types of change patterns; however, they should address two issues. First, they should define a similarity measure that can correctly find the corresponding Pareto-optimal solutions in two successive time steps. Second, they should be reasonably robust to input errors. This study introduces a new information-sharing strategy to improve the robustness of multi-model prediction methods in which each prediction model utilizes some information from the individual models of adjacent solutions. An adaptive scheme based on the relative distribution of population members is also proposed to utilize this information properly. The efficacy of this strategy in improving the robustness of the multi-model prediction method is demonstrated. Furthermore, this study introduces a similarity metric and thoroughly analyzes it alongside some of the commonly used similarity metrics for DMO. A weighted pointwise prediction method (WPPM) for DMO is then developed using the formulated information-sharing strategy and the proposed variable-based similarity metric. WPPM is compared with other well-known prediction methods on the CEC2018 test suite for DMO, with the numerical results revealing the superiority of WPPM.