A socio-physics based hybrid metaheuristic for solving complex non-convex constrained optimization problems

TL;DR

This paper introduces a socio-physics inspired hybrid metaheuristic combining Cohort Intelligence with physics-based optimization to effectively solve complex real-world constrained optimization problems, outperforming existing methods.

Contribution

It proposes a novel hybrid metaheuristic algorithm integrating socio-physics principles with constraint handling techniques for complex optimization tasks.

Findings

Proposed algorithms successfully solved 57 real-world constrained problems.

CI-SAPF-CBO outperformed other constrained optimization algorithms.

The hybrid approach demonstrated superior convergence and solution quality.

Abstract

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.