Solving Optimization Problems Using Hybrid Metaheuristics: Hybrid Genetic Algorithm With Modified Black Hole Algorithm as Local Search

An improved hybrid metaheuristic algorithm that combines the genetic algorithm (GA) for global search initiation and the black hole (BH) algorithm for local search is proposed. The proposed algorithm is developed to address the accuracy and robustness challenges associated with the original hybrid genetic black hole (GABH) algorithm, particularly when applied to high‐dimensional and real‐world engineering optimization problems. Variable population‐size GA based on the saw‐tooth scheme and a local BH algorithm with its modified method of generating new stars or solutions to replace eliminated stars are used to improve the performance of the original GABH algorithm. The proposed algorithm’s efficacy is evaluated using a suite of twenty‐three CEC benchmark functions, the welded beam, and the tension/compression spring design engineering benchmark. Comparative analyses of the proposed algorithm against traditional and state‐of‐the‐art optimization algorithms, including GA, PSO, mayfly, BH, and the original hybrid GABH, demonstrate that the improved algorithm exhibits superior performance in terms of optimal value, variability, and accuracy. This was further validated by the statistical significance test, which was done using the Friedman test and the Holm–Šidák post hoc method for pairwise comparison.