Dehghani Mohammad, Trojovský Pavel, Malik Om Parkash
Department of Mathematics, Faculty of Science, University of Hradec Králové, 500 03 Hradec Králové, Czech Republic.
Department of Electrical and Computer Engineering, University of Calgary, Calgary, AB T2N 1N4, Canada.
Biomimetics (Basel). 2023 Mar 14;8(1):121. doi: 10.3390/biomimetics8010121.
A new metaheuristic algorithm called green anaconda optimization (GAO) which imitates the natural behavior of green anacondas has been designed. The fundamental inspiration for GAO is the mechanism of recognizing the position of the female species by the male species during the mating season and the hunting strategy of green anacondas. GAO's mathematical modeling is presented based on the simulation of these two strategies of green anacondas in two phases of exploration and exploitation. The effectiveness of the proposed GAO approach in solving optimization problems is evaluated on twenty-nine objective functions from the CEC 2017 test suite and the CEC 2019 test suite. The efficiency of GAO in providing solutions for optimization problems is compared with the performance of twelve well-known metaheuristic algorithms. The simulation results show that the proposed GAO approach has a high capability in exploration, exploitation, and creating a balance between them and performs better compared to competitor algorithms. In addition, the implementation of GAO on twenty-one optimization problems from the CEC 2011 test suite indicates the effective capability of the proposed approach in handling real-world applications.
一种名为绿色水蚺优化算法（GAO）的新型元启发式算法已被设计出来，它模仿了绿色水蚺的自然行为。GAO的基本灵感来源于雄性水蚺在交配季节识别雌性物种位置的机制以及绿色水蚺的捕猎策略。基于对绿色水蚺这两种策略在探索和利用两个阶段的模拟，给出了GAO的数学建模。通过CEC 2017测试套件和CEC 2019测试套件中的29个目标函数，评估了所提出的GAO方法在解决优化问题方面的有效性。将GAO为优化问题提供解决方案的效率与12种著名元启发式算法的性能进行了比较。仿真结果表明，所提出的GAO方法在探索、利用以及在它们之间建立平衡方面具有很高的能力，并且与竞争算法相比表现更好。此外，在CEC 2011测试套件的21个优化问题上实施GAO，表明了所提出方法在处理实际应用方面的有效能力。
