Adaptive penalty strategies in genetic search for problems with inequality and equality constraints

This research aims to develop effective and robust self-organizing adaptive penalty strategies (SOAPS and SOAPS') for genetic algorithms to handle constrained optimization problems without the need of searching for proper values of penalty factors for a given optimization problem in hand. The proposed strategies are based on the idea that the constrained optimal design is almost always located at the boundary between feasible and infeasible domains. Both adaptive penalty strategies automatically adjust 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 design in each side of the constraint boundary so that the chance of locating their offspring designs around the boundary is maximized. Illustrative examples showed consistently improved performance on locating the global optimum in the problem with only inequality constraints and in the problem with both inequality and equality constraints.