چکیده مقاله
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
کلیدواژهها
نویسندگان
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
شیوه ارجاع
Saghafi, Erfan and Asadi, Shahrokh,1402,AOACO : Aquila Optimizer Based on Ant Colony Optimization,The 9th International Conference on Industrial and Systems Engineering,Mashhad
ارائهشده در
مجموعه مقالات نهمین کنفرانس بین المللی مهندسی صنایع و سیستم ها21 شهریور 1402 · مشهد