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Copyright of China Sciencepaper is the property of China Sciencepaper and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Concerning the problem that the teaching learningbasedoptimization (TLBO) algorithm is easy topremature with low solutionprecision, we propose an improved TLBO algorithm with selfstudy of teachers and optional-study of students. In every iteration, individual teachers adopt the oppotition-based learning (OBL) to generate an opposition search population, and the search space of the algorithm is guided to approximate optimum space. This mechanism is helpful for improving the balance and exploring the ability of the TLBO. Every individual student executes OBL randomly and studies from teachers at the same time. For keeping the diversity of the population, we calculate the students' jumping probability to current teachers. We adopt the roulette mechanism to choose the individuals which will replace the parent individuals. Compared with related algorithms, the simulations on 11 classical benchmark functions show that the proposed algorithm has better convergence rate and accuracy for nu¬merical optimization, and is suitable for solving high dimensional optimization problem. [ABSTRACT FROM AUTHOR]