An Interactive Many Objective Evolutionary Algorithm with Cascade Clustering and Reference Point Incremental Learning
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Researches have shown difficulties in obtaining proximity while maintaining diversity for solving many-objective optimization problems (MaOPs). The complexities of the true Pareto Front (PF) also pose serious challenges for the pervasive algorithms for their insufficient ability to adapt to the characteristics of the true PF with no priori. This paper proposes a cascade Clustering and reference point incremental Learning based Interactive Algorithm (CLIA) for many-objective optimization. In the cascade clustering process, using reference lines provided by the learning process, individuals are clustered and intraclassly sorted in a bi-level cascade style for better proximity and diversity. In the reference point incremental learning process, using the feedbacks from the clustering process, the proper generation of reference points is gradually obtained by incremental learning and the reference lines are accordingly repositioned. The advantages of the proposed interactive algorithm CLIA lie not only in the proximity obtainment and diversity maintenance but also in the versatility for the diverse PFs which uses only the interactions between the two processes without incurring extra evaluations. The experimental studies on the CEC'2018 MaOP benchmark functions have shown that the proposed algorithm CLIA has satisfactory covering of the true PFs, and is competitive, stable and efficient compared with the state-of-the-art algorithms.
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