Open AccessA Novel Modified Tree‐Seed Algorithm for High‐Dimensional Optimization Problems
Author(s) -
Zhao Shijie,
Gao Leifu,
Tu Jun,
Yu Dongmei
Publication year - 2020
Publication title -
chinese journal of electronics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.267
H-Index - 25
eISSN - 2075-5597
pISSN - 1022-4653
DOI - 10.1049/cje.2020.01.012
Subject(s) - computer science , robustness (evolution) , convergence (economics) , tree (set theory) , domain (mathematical analysis) , optimization algorithm , process (computing) , mathematical optimization , local optimum , algorithm , mathematics , mathematical analysis , biochemistry , chemistry , economics , gene , economic growth , operating system
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.