College of Computer Science and Technology, Jilin University, Changchun, China.
Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, China.
PLoS One. 2021 Nov 30;16(11):e0260725. doi: 10.1371/journal.pone.0260725. eCollection 2021.
Metaheuristic optimization algorithms are one of the most effective methods for solving complex engineering problems. However, the performance of a metaheuristic algorithm is related to its exploration ability and exploitation ability. Therefore, to further improve the African vultures optimization algorithm (AVOA), a new metaheuristic algorithm, an improved African vultures optimization algorithm based on tent chaotic mapping and time-varying mechanism (TAVOA), is proposed. First, a tent chaotic map is introduced for population initialization. Second, the individual's historical optimal position is recorded and applied to individual location updating. Third, a time-varying mechanism is designed to balance the exploration ability and exploitation ability. To verify the effectiveness and efficiency of TAVOA, TAVOA is tested on 23 basic benchmark functions, 28 CEC 2013 benchmark functions and 3 common real-world engineering design problems, and compared with AVOA and 5 other state-of-the-art metaheuristic optimization algorithms. According to the results of the Wilcoxon rank-sum test with 5%, among the 23 basic benchmark functions, the performance of TAVOA has significantly better than that of AVOA on 13 functions. Among the 28 CEC 2013 benchmark functions, the performance of TAVOA on 9 functions is significantly better than AVOA, and on 17 functions is similar to AVOA. Besides, compared with the six metaheuristic optimization algorithms, TAVOA also shows good performance in real-world engineering design problems.
元启发式优化算法是解决复杂工程问题最有效的方法之一。然而，元启发式算法的性能与其探索能力和开发能力有关。因此，为了进一步提高非洲秃鹫优化算法（AVOA），一种新的元启发式算法，一种基于帐篷混沌映射和时变机制的改进非洲秃鹫优化算法（TAVOA）被提出。首先，引入帐篷混沌映射进行种群初始化。其次，记录个体的历史最优位置，并将其应用于个体位置更新。第三，设计了一个时变机制来平衡探索能力和开发能力。为了验证 TAVOA 的有效性和效率，将 TAVOA 应用于 23 个基本基准函数、28 个 CEC 2013 基准函数和 3 个常见的实际工程设计问题，并与 AVOA 和 5 种其他最先进的元启发式优化算法进行了比较。根据 5%的威尔科克森秩和检验结果，在 23 个基本基准函数中，TAVOA 在 13 个函数上的性能明显优于 AVOA。在 28 个 CEC 2013 基准函数中，TAVOA 在 9 个函数上的性能明显优于 AVOA，在 17 个函数上与 AVOA 相似。此外，与 6 种元启发式优化算法相比，TAVOA 在实际工程设计问题中也表现出良好的性能。
