Quadratic Interpolation enhanced hybrid Grey Wolf Optimization and Moth Flame Optimization for global optimization

In order to construct a powerful meta-heuristic algorithm, this work suggests a novel hybrid technique namely h-QGMFO (Hybrid Quadratic Interpolation enhanced Grey Wolf - Moth Flame Optimization), which combines the Grey Wolf Optimizer (GWO) and Moth Flame Optimization (MFO) algorithms with Simple Quadratic Interpolation (SQI). Both algorithms suffer with identifying effective solutions, slow rate of convergence, and a propensity to become trapped in local optimal solutions. The h-QGMFO is proposed to combine the effectiveness of GWO and MFO algorithms in order to address the shortcomings concerned with these robust algorithms. The SQI technique, a part of this hybrid strategy, permits diversity in search while inhibiting early convergence of both algorithms. The recommended hybrid methodology is used to test the effectiveness of the algorithm on 23 (twenty-three) familiar benchmark functions and tested on real world problems with satisfactory results. The proposed strategy provides an effectual and capable solution in terms of the eminence of the output, and the convergence rate, according to the findings by comparing the proposed approach with other state-of-the-art algorithms.