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Avoidance of constraint violation for experiment-based evolutionary multi-objective optimization

Experiment-based optimization using Evolutionary Algorithms (EAs) is a promising approach for real world problems in which construction of simulation models is difficult. When using EAs, three difficulties have to be considered. Currently, two difficulties, uncertainty of the evaluation value and limitation of the number of evaluations, are active research topics into EAs. However, the other difficulty, avoidance of extreme trial, has not entered into the spotlight. Extreme trials run the ‘risk’ of breakdown of the optimized object and its measurement instruments in experiment-based optimization. In this paper, we consider that the extreme trial means a large constraint violation of the problems, and install the concept of ‘risky-constraint’. Then, to avoid risky-constraint violation, we propose a violation avoidance method and combine it with Multi-objective Evolutionary Algorithms (MOEAs). The effectiveness of the proposed method is confirmed through numerical experiments and real common-rail diesel engine experiments.