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A Two-Engine interaction driven many-objective evolutionary algorithm with feasibility-aware adaptation
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DOI：10.1016/j.asoc.2019.105588.png)
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Abstract
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The infeasible parts of the objective space in many-objective optimization problems make evolutionary algorithms face difficulties in obtaining proximity and maintaining diversity simultaneously. This paper proposes a Two-Engine interaction driven many-objective Evolutionary Algorithm with feasibility-aware adaptation (TEEA) that adapts the reference vectors and evolves the population towards the true Pareto Front (PF). The two interacting engines make reference vectors always approximately evenly distributed within the current PF for providing appropriate guidance for selection. The current PF is tracked by maintaining an Individual Archive (IA) of undominated individuals, and the adaptation of reference vectors is conducted with the help of a Reference Archive (RA) that contains layers of reference vectors corresponding to different density. On CEC'2018 benchmark functions with competition standards, the experimental results of the proposed TEEA have demonstrated the expected characteristics and competitive performance. (C) 2019 Elsevier B.V. All rights reserved.
Keywords:
Many-objective optimization
Reference vector
Feasible objective space
Interaction
Adaptation
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