Tree–seed algorithm (TSA) establishes a novel approach to solve continuous optimization problems, which is applied in many fields because of its simplicity and strength in finding optimal solutions. However, due to somewhat imbalance of its ability between exploration and exploitation in different search phases, the exploratory capability of TSA is relatively weak in optimizing multimodal and high-dimensional objective functions. To make some improvements, we propose a hybrid heuristic tree–seed algorithm named TSASC by integrating two features from sine–cosine algorithm. The proposed algorithm is then tested in comparison with TSA and other relevant algorithms through 30 benchmark functions from IEEE CEC 2014 and 3 constrained real engineering optimization problems. The results prove its enhanced balance between exploration and exploitation in both finding better global optimal solutions and effectively avoiding falling into local optimum, which shows that it has promising advantages in solving continuous optimization problems in engineering practices.
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The authors are grateful to the financial support by the Natural Science Foundation of the Science and Technology Department of Jilin Province, China (No. 20180101044JC), the Foundation of the Education Department of Jilin Province, China (No. JJKH20200141KJ), the Foundation of Social Science of Jilin Province, China (No. 2019B68), the Foundation of Jilin University of Finance and Economics (Nos. 2020ZY14, 2020ZY09) and National Natural Science Foundation of China (No. 61572225).
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Jiang, J., Han, R., Meng, X. et al. TSASC: tree–seed algorithm with sine–cosine enhancement for continuous optimization problems. Soft Comput (2020). https://doi.org/10.1007/s00500-020-05099-w
- Continuous optimization problem
- Tree–seed algorithm (TSA)
- Sine–cosine algorithm (SCA)
- Swarm intelligence