A Novel Repair Mechanism based on Most Probable Point of Failure
Handling equality constraints is a challenging endeavour for researchers in optimization. A single equality constraint can pose serious difficulties to an optimization algorithm severely limiting its capability when the size of the feasible search space is small. This work introduces a novel approach for repairing infeasible solutions, wherein one or all the solutions of the population are repaired to yield feasible solution(s). Subsequently, a suitable classic or evolutionary optimization procedure can be used to obtain optimal solution(s). Our [Ray and Saha] current approach is implemented within a Real-coded Genetic Algorithm (RGA) framework and the repair method is based on the idea of Most Probable Point (MPP) (of failure) which is derived from the context of Reliability Based Optimization (RBO). Promising results have been obtained for problems with equality constraints and ones with active inequalities.