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An improved group teaching optimization algorithm for global function optimization
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DOI：10.1038/s41598-022-15170-1.png)
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Abstract
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This paper proposes an improved group teaching optimization algorithm (IGTOA) to improve the convergence speed and accuracy of the group teaching optimization algorithm. It assigns teachers independently for each individual, replacing the original way of sharing the same teacher, increasing the evolutionary direction and expanding the diversity of the population; it dynamically divides the students of the good group and the students of the average group to meet the different needs of convergence speed and population diversity in different evolutionary stages; in the student learning stage, the weak self-learning part is canceled, the mutual learning part is increased, and the population diversity is supplemented; for the average group students, a new sub-space search mode is proposed, and the teacher's teaching method is improved to reduce the diversity in the population evolution process. and propose a population reconstruction mechanism to expand the search range of the current population and ensure population diversity. Finally, the experimental results on the CEC2013 test suite show that IGTOA has clear advantages in convergence speed and accuracy over the other five excellent algorithms.
Keywords:
PARTICLE SWARM OPTIMIZATION
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