An Effective Hybridization of Quantum-based Avian Navigation and Bonobo Optimizers to Solve Numerical and Mechanical Engineering Problems  

作　　者：Mohammad H.Nadimi-Shahraki 

机构地区：[1]Faculty of Computer Engineering,Najafabad Branch,Islamic Azad University,Najafabad,8514143131,Iran [2]Big Data Research Center,Najafabad Branch,Islamic Azad University,Najafabad,8514143131,Iran

出　　处：《Journal of Bionic Engineering》2023年第3期1361-1385,共25页仿生工程学报（英文版）

摘　　要：Hybrid metaheuristic algorithms play a prominent role in improving algorithms' searchability by combining each algorithm's advantages and minimizing any substantial shortcomings. The Quantum-based Avian Navigation Optimizer Algorithm (QANA) is a recent metaheuristic algorithm inspired by the navigation behavior of migratory birds. Different experimental results show that QANA is a competitive and applicable algorithm in different optimization fields. However, it suffers from shortcomings such as low solution quality and premature convergence when tackling some complex problems. Therefore, instead of proposing a new algorithm to solve these weaknesses, we use the advantages of the bonobo optimizer to improve global search capability and mitigate premature convergence of the original QANA. The effectiveness of the proposed Hybrid Quantum-based Avian Navigation Optimizer Algorithm (HQANA) is assessed on 29 test functions of the CEC 2018 benchmark test suite with different dimensions, 30, 50, and 100. The results are then statistically investigated by the Friedman test and compared with the results of eight well-known optimization algorithms, including PSO, KH, GWO, WOA, CSA, HOA, BO, and QANA. Ultimately, five constrained engineering optimization problems from the latest test suite, CEC 2020 are used to assess the applicability of HQANA to solve complex real-world engineering optimization problems. The experimental and statistical findings prove that the proposed HQANA algorithm is superior to the comparative algorithms.

关 键 词：Optimization Metaheuristic algorithms Evolutionary algorithm Quantum-based avian navigation optimizer algorithm Engineering optimization problems Bionic algorithm 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]