Empirical Study on Clustering Based on Modified Teaching Learning Based Optimization

Panigrahi, S.K.; Pattnaik, Sabyasachi

doi:10.1016/j.procs.2016.07.338

Cited by 8 publications

(2 citation statements)

References 19 publications

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“…Since the teacher might be able to change the mean for a better level or not, a teaching factor (T F ) is assigned that can be either 1 or 2, and r i is a number between [0,1] that randomly decides what the mean value is. Hence, the new level of each student (X new,i ) can be improved according to the mean difference using (19)(20)(21) [26], and if the new level of a student gives better function value, it is accepted for proceeding the optimization.…”

Section: ) Teacher Phasementioning

confidence: 99%

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Optimal Design of Integrated Heat Pipe Air-Cooled System Using TLBO Algorithm for SiC MOSFET Converters

Alizadeh

Rodriguez

Bauman

et al. 2020

IEEE Open J. Power Electron.

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Optimal thermal management system design is critical for power electronic converters to ensure the reliability of power semiconductor switches. Medium power density inverter systems are often air-cooled to ensure an efficient and cost-effective thermal management solution. In addition, using heat pipes as the heat transfer medium between the heat sources and the heat sink can provide lower volume for the entire inverter. This paper investigates the effectiveness of Teaching Learning Based Optimization (TLBO) for finding the optimal forced-air heat sink with heat pipe cooling system to achieve higher fan efficiency and lower inverter packaging volume. The optimal design is found utilizing commercially available fans and heat pipes. The TLBO design optimization is also compared to the highly implemented Particle Swarm Optimization (PSO) and it is found that TLBO uses 20 times fewer iterations than PSO to converge, and that the TLBO results are more robust for different design constraints.

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Section: ) Teacher Phasementioning

confidence: 99%

“…If X i is a better student than X j , the level of knowledge will change to (22), otherwise it will be (23). At the end of the comparison loop for the entire student population (N pop ), if X new results in a better objective value ( f (X new )), X new is accepted as the best optimization point [26].…”

Section: ) Teacher Phasementioning

confidence: 99%