  Evolutionary Algorithms (EAs) are being routinely applied for a variety of
optimization tasks, and real-parameter optimization in the presence of
constraints is one such important area. During constrained optimization EAs
often create solutions that fall outside the feasible region; hence a viable
constraint- handling strategy is needed. This paper focuses on the class of
constraint-handling strategies that repair infeasible solutions by bringing
them back into the search space and explicitly preserve feasibility of the
solutions. Several existing constraint-handling strategies are studied, and two
new single parameter constraint-handling methodologies based on parent-centric
and inverse parabolic probability (IP) distribution are proposed. The existing
and newly proposed constraint-handling methods are first studied with PSO, DE,
GAs, and simulation results on four scalable test-problems under different
location settings of the optimum are presented. The newly proposed
constraint-handling methods exhibit robustness in terms of performance and also
succeed on search spaces comprising up-to 500 variables while locating the
optimum within an error of 10$^{-10}$. The working principle of the IP based
methods is also demonstrated on (i) some generic constrained optimization
problems, and (ii) a classic `Weld' problem from structural design and
mechanics. The successful performance of the proposed methods clearly exhibits
their efficacy as a generic constrained-handling strategy for a wide range of
applications.
