Khalid Asmaa M, Hosny Khalid M, Mirjalili Seyedali
Department of Information Technology, Faculty of Computers and Informatics, Zagazig University, Zagazig, 44519 Egypt.
Centre for Artificial Intelligence Research and Optimization, Torrens University Australia, Fortitude Valley, Brisbane, QLD 4006 Australia.
Neural Comput Appl. 2022;34(24):22465-22492. doi: 10.1007/s00521-022-07639-x. Epub 2022 Aug 26.
This paper presents a novel bio-inspired optimization algorithm called Coronavirus Optimization Algorithm (COVIDOA). COVIDOA is an evolutionary search strategy that mimics the mechanism of coronavirus when hijacking human cells. COVIDOA is inspired by the frameshifting technique used by the coronavirus for replication. The proposed algorithm is tested using 20 standard benchmark optimization functions with different parameter values. Besides, we utilized five IEEE Congress of Evolutionary Computation (CEC) benchmark test functions (CECC06, 2019 Competition) and five CEC 2011 real-world problems to prove the proposed algorithm's efficiency. The proposed algorithm is compared to eight of the most popular and recent metaheuristic algorithms from the state-of-the-art in terms of best cost, average cost (AVG), corresponding standard deviation (STD), and convergence speed. The results demonstrate that COVIDOA is superior to most existing metaheuristics.
本文提出了一种名为冠状病毒优化算法（COVIDOA）的新型生物启发式优化算法。COVIDOA是一种进化搜索策略，它模仿了冠状病毒劫持人类细胞时的机制。COVIDOA受到冠状病毒用于复制的移码技术的启发。使用20个具有不同参数值的标准基准优化函数对所提出的算法进行了测试。此外，我们利用了五个IEEE进化计算大会（CEC）基准测试函数（CECC06，2019年竞赛）和五个CEC 2011现实世界问题来证明所提出算法的效率。在所提出的算法与最先进的八个最流行和最新的元启发式算法在最佳成本、平均成本（AVG）、相应标准差（STD）和收敛速度方面进行了比较。结果表明，COVIDOA优于大多数现有的元启发式算法。
