A local-dimension-improved teaching-learning-based optimization (LDimTLBO) algorithm based on local dimension improvement and self-learning disturbance was proposed, aiming at the problem of weak local search ability and easy to fall into local optimum during the anaphase of evolution in the original teaching-learning-based optimization (TLBO) algorithm. The local dimension improvement strategy was integrated into the teaching and learning phases, which passed the high-quality dimension variables down to the next generation, improved the low-quality ones, and enhanced the fine-grained search capability of the proposed algorithm. A new individual update mode combining global and local dimension improvements was designed. Through the generational change of these two improvements' weights, the global exploration at early stage and local exploitation at late stage was balanced in the hybrid update mode. A self-learning phase based on individual so-far-best position and search boundary information was also added into the search process, which made the population search towards the optimum even in the later stage of the evolution, thus, the algorithm escaped from getting into the local optimum in the early stage. The simulation results based on testing on benchmark functions demonstrated that in contrast to results of TLBO and other improved variants, the convergence accuracy of LDimTLBO was 102 to 105 times higher, and the convergence speed was 2 to 3 times faster.
Received: 03 March 2018
Published: 22 November 2018
HE Jie-guang, PENG Zhi-ping, CUI De-long, LI Qi-rui. Teaching-learning-based optimization algorithm with local dimension improvement. JOURNAL OF ZHEJIANG UNIVERSITY (ENGINEERING SCIENCE), 2018, 52(11): 2159-2170.
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