School of Mathematics and Computer Science, Shaanxi University of Technology, Hanzhong, P.R. China.
PLoS One. 2017 Apr 12;12(4):e0175114. doi: 10.1371/journal.pone.0175114. eCollection 2017.
Harmony Search (HS) and Teaching-Learning-Based Optimization (TLBO) as new swarm intelligent optimization algorithms have received much attention in recent years. Both of them have shown outstanding performance for solving NP-Hard optimization problems. However, they also suffer dramatic performance degradation for some complex high-dimensional optimization problems. Through a lot of experiments, we find that the HS and TLBO have strong complementarity each other. The HS has strong global exploration power but low convergence speed. Reversely, the TLBO has much fast convergence speed but it is easily trapped into local search. In this work, we propose a hybrid search algorithm named HSTLBO that merges the two algorithms together for synergistically solving complex optimization problems using a self-adaptive selection strategy. In the HSTLBO, both HS and TLBO are modified with the aim of balancing the global exploration and exploitation abilities, where the HS aims mainly to explore the unknown regions and the TLBO aims to rapidly exploit high-precision solutions in the known regions. Our experimental results demonstrate better performance and faster speed than five state-of-the-art HS variants and show better exploration power than five good TLBO variants with similar run time, which illustrates that our method is promising in solving complex high-dimensional optimization problems. The experiment on portfolio optimization problems also demonstrate that the HSTLBO is effective in solving complex read-world application.
和声搜索（HS）和基于教与学的优化（TLBO）作为新的群体智能优化算法，近年来受到了广泛关注。它们在解决 NP 难优化问题方面都表现出了出色的性能。然而，它们在处理一些复杂的高维优化问题时，性能也会急剧下降。通过大量的实验，我们发现 HS 和 TLBO 彼此之间具有很强的互补性。HS 具有很强的全局探索能力，但收敛速度较慢。相反，TLBO 的收敛速度很快，但很容易陷入局部搜索。在这项工作中，我们提出了一种混合搜索算法，名为 HSTLBO，它将两种算法融合在一起，通过自适应选择策略协同解决复杂的优化问题。在 HSTLBO 中，HS 和 TLBO 都进行了修改，旨在平衡全局探索和开发能力，其中 HS 主要用于探索未知区域，TLBO 主要用于快速开发已知区域中的高精度解决方案。我们的实验结果表明，与五个最先进的 HS 变体相比，该算法具有更好的性能和更快的速度，与五个性能良好的 TLBO 变体相比，具有相似的运行时间，探索能力更强，这表明该方法在解决复杂的高维优化问题方面具有很大的潜力。投资组合优化问题的实验也表明，HSTLBO 可以有效地解决复杂的实际应用问题。
