ZHAO Shijie, GAO Leifu, TU Jun, YU Dongmei. A Novel Modified Tree-Seed Algorithm for High-Dimensional Optimization Problems[J]. Chinese Journal of Electronics, 2020, 29(2): 337-343. doi: 10.1049/cje.2020.01.012
Citation: ZHAO Shijie, GAO Leifu, TU Jun, YU Dongmei. A Novel Modified Tree-Seed Algorithm for High-Dimensional Optimization Problems[J]. Chinese Journal of Electronics, 2020, 29(2): 337-343. doi: 10.1049/cje.2020.01.012

A Novel Modified Tree-Seed Algorithm for High-Dimensional Optimization Problems

doi: 10.1049/cje.2020.01.012
Funds:  This work is supported by the Doctoral Scientific Research Foundation of Liaoning Province (No.2019-BS-118, No.20170520075), the Project of Liaoning Provincial Department of Education (No.LJ2019JL017, No.LJ2017QL031), the National Natural Science Foundation of China (No.51704140), the China Postdoctoral Science Foundation (No.2019M650449), and National Natural Science Foundation of Liaoning Province (No.2019-ZD-0032).
  • Received Date: 2018-10-11
  • Rev Recd Date: 2019-03-06
  • Publish Date: 2020-03-10
  • To efficiently handle high-dimensional continuous optimization problems, a Modified tree-seed algorithm(MTSA) is proposed by coupling a newly introduced control parameter named as Seed domain shrinkable coefficient(SDSC) and Local reinforcement strategy(LRS) based on gradient information of adjacentgeneration best trees. SDSC is dynamically decreased with iterations to adjust the produced domain of offspring seeds, for achieving the tradeoff between the global exploration and local exploitation. LRS strategy is to execute local exploitation process by employing gradient information of two best trees, for enhancing convergence efficiency and local optima avoidance with probabilities. The compared experimental results show the different effects of differenttype SDSC on MTSA, the faster convergence efficiency and the stronger robustness of the proposed MTSA.
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