Abstract
After the teaching–learning-based optimization (TLBO) algorithm was proposed, many improved algorithms have been presented in recent years, which simulate the teaching–learning phenomenon of a classroom to effectively solve global optimization problems. In this paper, a cyclical non-linear inertia-weighted teaching–learning-based optimization (CNIWTLBO) algorithm is presented. This algorithm introduces a cyclical non-linear inertia weighted factor into the basic TLBO to control the memory rate of learners, and uses a non-linear mutation factor to control the learner’s mutation randomly during the learning process. In order to prove the significant performance of the proposed algorithm, it is tested on some classical benchmark functions and the comparison results are provided against the basic TLBO, some variants of TLBO and some other well-known optimization algorithms. The experimental results show that the proposed algorithm has better global search ability and higher search accuracy than the basic TLBO, some variants of TLBO and some other algorithms as well, and can escape from the local minimum easily, while keeping a faster convergence rate.
Highlights
As is well known, the research and application of swarm intelligence optimization mostly focus on nature-inspired algorithms
To enhance the exploiting ability and avoid the premature phenomenon of NIWTLBO, we propose a new improved TLBO variant which is a cyclical non-linear inertia weighted teaching–learning-based optimization algorithm
We introduce the cyclical non-linear inertia weight factor wc into Equations (1) and (4) based on the basic TLBO, which scale the existing knowledge of the learner for calculating the new value
Summary
The research and application of swarm intelligence optimization mostly focus on nature-inspired algorithms. Aiming at neural network training in portable AI (artificial intelligence) devices, Yang et al [21] proposed the CTLBO (compact teaching–learning-based optimization) algorithm to solve global continuous problems, which can reduce the memory requirement while maintaining the high performance. To enhance the exploiting ability and avoid the premature phenomenon of NIWTLBO, we propose a new improved TLBO variant which is a cyclical non-linear inertia weighted teaching–learning-based optimization algorithm (called as CNIWTLBO). This algorithm uses a cyclical non-linear inertia weight factor to replace the old one to control the memory rate of learners, and employs a non-linear mutation factor to control the learner’s mutation randomly in teacher and learner phase.
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