Department of Mathematics, Faculty of Science, University of Hradec Králové, Rokitanského 62, Hradec Králové, 500 03, Czech Republic.
Sci Rep. 2022 Sep 1;12(1):14861. doi: 10.1038/s41598-022-19313-2.
Metaheuristic algorithms have a wide range of applications in handling optimization problems. In this study, a new metaheuristic algorithm, called the chef-based optimization algorithm (CBOA), is developed. The fundamental inspiration employed in CBOA design is the process of learning cooking skills in training courses. The stages of the cooking training process in various phases are mathematically modeled with the aim of increasing the ability of global search in exploration and the ability of local search in exploitation. A collection of 52 standard objective functions is utilized to assess the CBOA's performance in addressing optimization issues. The optimization results show that the CBOA is capable of providing acceptable solutions by creating a balance between exploration and exploitation and is highly efficient in the treatment of optimization problems. In addition, the CBOA's effectiveness in dealing with real-world applications is tested on four engineering problems. Twelve well-known metaheuristic algorithms have been selected for comparison with the CBOA. The simulation results show that CBOA performs much better than competing algorithms and is more effective in solving optimization problems.
元启发式算法在处理优化问题方面有广泛的应用。在本研究中，开发了一种新的元启发式算法，称为基于厨师的优化算法（CBOA）。CBOA 设计的基本灵感来自于培训课程中学习烹饪技巧的过程。采用数学模型来模拟各个阶段的烹饪训练过程，旨在提高全局搜索在探索中的能力和局部搜索在开发中的能力。使用了 52 个标准目标函数集合来评估 CBOA 在解决优化问题方面的性能。优化结果表明，CBOA 通过在探索和开发之间取得平衡，能够提供可接受的解决方案，并且在处理优化问题方面非常高效。此外，还在四个工程问题上测试了 CBOA 处理实际应用的有效性。选择了 12 种知名的元启发式算法与 CBOA 进行比较。仿真结果表明，CBOA 的性能优于竞争算法，在解决优化问题方面更有效。
