A Socio-Physics-Based Hybrid Metaheuristic for Solving Complex Non-convex Constrained Optimization Problems

Several artificial intelligence-based heuristic and metaheuristic algorithms have been developed so far. These algorithms have shown their superiority toward 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 2 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 as CI-SAPF-CBO. The performance of the proposed CI-SAPF and CI-SAPF-CBO algorithms is compared with other constrained optimization algorithms such as IUDE, ϵMAg-ES, and iLSHADEϵ. The superiority of the proposed algorithms is discussed in details followed by future directions to evolve the constrained handling techniques.