ABSTRACT
The teaching-learning-based optimization (TLBO) algorithm, which has gained popularity among scholars for addressing practical issues, suffers from several drawbacks including slow convergence speed, susceptibility to local optima, and suboptimal performance. To overcome these limitations, this paper presents a novel algorithm called the teaching-learning optimization algorithm, based on the cadre-mass relationship with the tutor mechanism (TLOCTO). Building upon the original teaching foundation, this algorithm incorporates the characteristics of class cadre settings and extracurricular learning institutions. It proposes a new learner strategy, cadre-mass relationship strategy, and tutor mechanism. The experimental results on 23 test functions and CEC-2020 benchmark functions demonstrate that the enhanced algorithm exhibits strong competitiveness in terms of convergence speed, solution accuracy, and robustness. Additionally, the superiority of the proposed algorithm over other popular optimizers is confirmed through the Wilcoxon signed rank-sum test. Furthermore, the algorithm's practical applicability is demonstrated by successfully applying it to three complex engineering design problems.
ABSTRACT
We demonstrate a self-starting 700 MHz repetition rate Yb:fiber laser incorporated with a phase biased nonlinear amplifying loop mirror as an artificial saturable absorber. The laser delivers a maximum power of 150 mW and a pulse width of 215 fs at a pump power of 710 mW. The integration of relative intensity noise (RIN) between 10 Hz and 10 MHz results in a minimum integrated RIN of 0.015%. The phase noise of the fundamental repetition rate was also characterized at different net-cavity dispersion. Although the laser is made of nonpolarization maintaining fiber, the mode locking sustains over two weeks in open air, showing its environmental stability.
