  Several Artificial Intelligence based heuristic and metaheuristic algorithms
have been developed so far. These algorithms have shown their superiority
towards solving complex problems from different domains. However, it is
necessary to critically validate these algorithms for solving real-world
constrained optimization problems. The search behavior in those problems is
different as it involves large number of linear, nonlinear and non-convex type
equality and inequality constraints. In this work a 57 real-world constrained
optimization problems test suite is solved using two constrained metaheuristic
algorithms originated from a socio-based Cohort Intelligence (CI) algorithm.
The first CI-based algorithm incorporates a self-adaptive penalty function
approach i.e., CI-SAPF. The second algorithm combines CI-SAPF with the
intrinsic properties of the physics-based Colliding Bodies Optimization (CBO)
referred to CI-SAPF-CBO. The results obtained from CI-SAPF and CI-SAPF-CBO are
compared with other constrained optimization algorithms. The superiority of the
proposed algorithms is discussed in details followed by future directions to
evolve the constrained handling techniques.
