Journal Article10.1109/TCYB.2021.3059252
Evolutionary Dynamic Multiobjective Optimization via Learning From Historical Search Process.
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TL;DR: In this article, a knowledge learning strategy for change response in the dynamic multiobjective optimization is proposed, which can accelerate convergence as well as introduce diversity for the optimization of the future environment.
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Abstract: Dynamic multiobjective optimization problems are challenging due to their fast convergence and diversity maintenance requirements. Prediction-based evolutionary algorithms currently gain much attention for meeting these requirements. However, it is not always the case that an elaborate predictor is suitable for different problems and the quality of historical solutions is sufficient to support prediction, which limits the availability of prediction-based methods over various problems. Faced with these issues, this article proposes a knowledge learning strategy for change response in the dynamic multiobjective optimization. Unlike prediction approaches that estimate the future optima from previously obtained solutions, in the proposed strategy, we react to changes via learning from the historical search process. We introduce a method to extract the knowledge within the previous search experience. The extracted knowledge can accelerate convergence as well as introduce diversity for the optimization of the future environment. We conduct a comprehensive experiment on comparing the proposed strategy with the state-of-the-art algorithms. Results demonstrate the better performance of the proposed strategy in terms of solution quality and computational efficiency.
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Citations
Combining a hybrid prediction strategy and a mutation strategy for dynamic multiobjective optimization
TL;DR: In this paper , the authors proposed a change response mechanism that combines a hybrid prediction strategy and a precision controllable mutation strategy (HPPCM) to solve the dynamic multiobjective optimization problems (DMOPs).
Multiple source transfer learning for dynamic multiobjective optimization
TL;DR: Wang et al. as mentioned in this paper presented a multiple source transfer learning method for dynamic multiobjective evolutionary algorithms (DMOEAs) with transfer learning, called MSTL-DMOEA, which runs two transfer learning procedures to fully exploit the historical information from all previous environments.
Interaction-based Prediction for Dynamic Multiobjective Optimization
TL;DR: In this paper , the authors proposed an interaction-based prediction method, which captures the correlation of variables with prediction targets and selects the most relevant variables to build prediction models using neural networks.
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A Mahalanobis Distance-based Approach for Dynamic Multi-objective Optimization with Stochastic Changes
TL;DR: In this paper , a Mahalanobis distance-based approach (MDA) is proposed to deal with dynamic multi-objective optimization problems with stochastic changes, where the authors make an all-sided assessment of search environments via Mahalanois distance on saved information to learn the relationship between the new environment and historical ones.
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A Framework Based on Historical Evolution Learning for Dynamic Multiobjective Optimization
TL;DR: Wang et al. as discussed by the authors proposed a historical evolution learning based framework to assist the static optimizers in fully using the historical evolution direction and Pareto optimal set distribution, and two new models are designed with a purpose of generating offsprings and environmental selection.
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