Enhanced global optimization using quadratically interpolated hybrid pathfinder algorithm
Abstract
Abstract Traditional optimization algorithms often face challenges when addressing the complexity and expense associated with global optimization problems and engineering challenges. This study introduces a variation of the Pathfinder Algorithm (PFA) called the Quadratic Interpolated Hybridized Pathfinder Algorithm (QHIPFA), which incorporates enhancement techniques to improve efficiency in both global and local search processes. QHIPFA is specifically designed to address global numerical and engineering optimization problems. The algorithm integrates the Quadratic Interpolation (QI) technique into the original PFA, enhancing its performance by improving search within local regions to achieve the optimal global solution. Additionally, the QI technique fosters collaboration among individuals in the PFA population. The Salp Swarm Algorithm (SSA) technique further enhances the search process by improving the exploration capability of PFA, promoting diversity within the population, and assisting in avoiding suboptimal solutions. This increased exploration and exploitation capacity allows for a more comprehensive search of the problem domain. The effectiveness of QHIPFA’s exploitation and exploration capabilities is demonstrated through experiments conducted on 25 benchmark functions from CEC2015 and CEC2021 of various dimensions. In these tests, QHIPFA outperforms twelve well-established optimization methods. Furthermore, the algorithm was tested on five engineering problems, and the results validate its efficacy in optimizing engineering problems.