Adaptive Feedback Elitist Teaching-Learning-Based Optimization Algorithm

doi:10.16182/j.issn1004731x.joss.201808016

Abstract

Abstract: Elitist teaching-learning-based optimization (ETLBO) is a novel optimization algorithm based on the practical teaching-learning process of the class. An adaptive feedback elitist teaching-learning-based optimization (AFETLBO) algorithm is proposed to solve the problem of low precision and poor stability of the ETLBO. At the end of the learner phase, student can be divided into the top students and the inferior students dynamically by adding the adaptive feedback phase. In this phase, the inferior students should communicate with the teacher and enable themselves to be close to the teacher quickly so as to strengthen the convergence ability. The top students should study by themselves to local search carefully. The adaptive feedback phase can increase the learning style and ensure the diversity of students so as to improve the algorithm’s global search ability. Six unconstrained and five constrained classic tests show that the AFETLBO algorithm has a higher ability of optimization precision and convergence than other algorithms.

Key words: ETLBO, adaptive feedback, evolutionary algorithm, function optimization

CLC Number:

TP301.6

Li Rongyu, Liang Dong, Qi Guihong. Adaptive Feedback Elitist Teaching-Learning-Based Optimization Algorithm[J]. Journal of System Simulation, 2018, 30(8): 2950-2957.

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