Erfan Saghafi, - Data Mining Laboratory, Industrial Engineering Department, Faculty of Engineering, College of Farabi,University of Tehran, Tehran, Iran
Shahrokh Asadi - Data Mining Laboratory, Industrial Engineering Department, Faculty of Engineering, College of Farabi,University of Tehran, Tehran, Iran

Over the past two decades, Metaheuristic (MH) algorithms have played a crucial role in solvingintractable optimization problems. Although meta-heuristic algorithms have proven to be highlyeffective in providing efficient solutions to a broad spectrum of complex problems, there are instanceswhere hybrid algorithms have demonstrated their potential in further enhancing problem-solvingcapabilities and augmenting the performance of meta-heuristic algorithms. In this study we proposed anovel hybrid method based on two metaheuristic algorithms, The Aquila Optimizer (AO) algorithm andAnt Colony Optimization for continuous domains (ACOR) for solving global optimization. Since theAquila algorithm is a population-based method and has a continuous nature, it can be very effective inimproving the continuous domains of Ant Colony Optimization. In order to verify the effectiveness ofthe algorithm, the algorithm was benchmarked on some well-known test functions and compared withother popular meta-heuristic algorithms. The results show that this hybrid algorithm performssignificantly better than other algorithms

Hybrid Algorithm, Ant Colony Optimization, Aquila Optimizer, Global Optimization