Self-organizing adaptive penalty strategy in constrained genetic search

This research aims to develop an effective and robust self-organizing adaptive penalty strategy for genetic algorithms to handle constrained optimization problems without the need to search for appropriate values of penalty factors for the given optimization problem. The proposed strategy is based on the idea that the constrained optimal design is almost always located at the boundary between feasible and infeasible domains. This adaptive penalty strategy automatically adjusts the value of the penalty parameter used for each of the constraints according to the ratio between the number of designs violating the specific constraint and the number of designs satisfying the constraint. The goal is to maintain equal numbers of designs on each side of the constraint boundary so that the chance of locating their offspring designs around the boundary is maximized. The new penalty function is self-defining and no parameters need to be adjusted for objective and constraint functions in any given problem. This penalty strategy is tested and compared with other known penalty function methods in mathematical and structural optimization problems, with favorable results.