School of Civil Engineering, Guangzhou University, Guangzhou, China.
Comput Intell Neurosci. 2019 Aug 1;2019:1589303. doi: 10.1155/2019/1589303. eCollection 2019.
Lightning attachment procedure optimization (LAPO) is a new global optimization algorithm inspired by the attachment procedure of lightning in nature. However, similar to other metaheuristic algorithms, LAPO also has its own disadvantages. To obtain better global searching ability, an enhanced version of LAPO called ELAPO has been proposed in this paper. A quasi-opposition-based learning strategy is incorporated to improve both exploration and exploitation abilities by considering an estimate and its opposite simultaneously. Moreover, a dimensional search enhancement strategy is proposed to intensify the exploitation ability of the algorithm. 32 benchmark functions including unimodal, multimodal, and CEC 2014 functions are utilized to test the effectiveness of the proposed algorithm. Numerical results indicate that ELAPO can provide better or competitive performance compared with the basic LAPO and other five state-of-the-art optimization algorithms.
Lightning 附着过程优化 (LAPO) 是一种新的全局优化算法，灵感来自自然界中 lightning 的附着过程。然而，与其他启发式算法类似，LAPO 也有其自身的缺点。为了获得更好的全局搜索能力，本文提出了 LAPO 的增强版本 ELAPO。通过同时考虑估计值及其相反数，引入准反对称学习策略来提高算法的探索和开发能力。此外，还提出了维度搜索增强策略来增强算法的开发能力。本文使用 32 个基准函数（包括单峰、多峰和 CEC 2014 函数）来测试所提出算法的有效性。数值结果表明，ELAPO 可以提供比基本 LAPO 和其他五种最先进的优化算法更好或具有竞争力的性能。
