An ANN-Assisted Repair Operator for Evolutionary Multiobjective Optimization

Learning for effective problem information from already explored search-space in an optimization run, and utilizing it to improve convergence properties have presented important directions in Evolutionary Computation research. In this paper, we propose an Artificial Neural Network (ANN)-assisted modeling approach which learns from pairs of solutions—changes in the design variables resulting in improved solutions—and uses the resulting ANN as an innovized repair operator in an adaptive manner. Although the concept can be easily applied to single-objective problems, in this paper, we develop the overall procedure for multi-objective optimization. On a number of test and engineering problems involving two and three objectives, with or without constraints, we demonstrate faster convergence behavior of ANN-assisted Evolutionary Multi-objective Optimization Algorithms (EMOA) in terms of the hypervolume metric. The results are encouraging, pave a new path for the performance enhancement of EMOAs, and set the motivation for further exploration on more challenging problems.

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