Abstract: By introducing a new reward mechanism into the original elitist teachinglearningbased optimization (ETLBO) algorithm, we proposed a new ETLBOreward algorithm combined with the new reward mechanism, and proposed a simple adaptive elite number RETLBOreward algorithm based on the ETLBO-reward algorithm. The proposed algorithm retained the advantages of traditional algorithm, such as few parameters, easy implementation and fast convergence and so on, and further improved convergence ability of traditional algorithms. The test results of six continuous nonlinear optimization problems show that the two algorithms have good performance, and compare with the original ETLBO algorithm, the efficiency of the solution is obviously improved.

Key words: teachinglearningbased optimization (TLBO) algorithm, reward mechanism, adaptive, continuous nonlinear optimization