Sang-To Thanh, Hoang-Le Minh, Wahab Magd Abdel, Cuong-Le Thanh
Laboratory Soete, Department of Electromechanical, Systems and Metal Engineering, Ghent University, Technologiepark Zwijnaarde 903, 9052, Zwijnaarde, Belgium.
Faculty of Civil Engineering, Ho Chi Minh City Open University, Ho Chi Minh City, Vietnam.
Sci Rep. 2022 May 19;12(1):8362. doi: 10.1038/s41598-022-12030-w.
In this study, a meta-heuristic algorithm, named The Planet Optimization Algorithm (POA), inspired by Newton's gravitational law is proposed. POA simulates the motion of planets in the solar system. The Sun plays the key role in the algorithm as at the heart of search space. Two main phases, local and global search, are adopted for increasing accuracy and expanding searching space simultaneously. A Gauss distribution function is employed as a technique to enhance the accuracy of this algorithm. POA is evaluated using 23 well-known test functions, 38 IEEE CEC benchmark test functions (CEC 2017, CEC 2019) and three real engineering problems. The statistical results of the benchmark functions show that POA can provide very competitive and promising results. Not only does POA require a relatively short computational time for solving problems, but also it shows superior accuracy in terms of exploiting the optimum.
在本研究中，提出了一种受牛顿引力定律启发的元启发式算法——行星优化算法（POA）。POA模拟太阳系中行星的运动。太阳在算法中起着关键作用，处于搜索空间的核心位置。该算法采用局部搜索和全局搜索两个主要阶段，以同时提高精度和扩大搜索空间。采用高斯分布函数作为提高该算法精度的一种技术。使用23个著名测试函数、38个IEEE CEC基准测试函数（CEC 2017、CEC 2019）以及三个实际工程问题对POA进行评估。基准函数的统计结果表明，POA能够提供非常有竞争力且前景广阔的结果。POA不仅在解决问题时所需的计算时间相对较短，而且在寻优方面表现出卓越的精度。
