• V.P. Sakthivel

Glowworm Swarm Optimization (GSO) algorithm is a new swarm intelligence technique which replicates the movement of the glowworms in a swarm based on the distance between them and on a luminescent quantity called luciferin. This algorithm has been proven very efficient in the problems that have been applied. However, this algorithm is not applied to solve the three phase cage induction motor design (TPCIMD) problems. In this paper, the TPCIMD problem is solved using GSO approach. Due to stochastic nature of GSO, this algorithm does not trap in local optimums. In order to prove the effectiveness of this algorithm, the proposed algorithm is applied for designing two sample motors. The simulation results are compared with genetic algorithm (GA), particle swarm optimization (PSO) and conventional approaches. The results prove the effectiveness, accuracy and speed of this algorithm for solving TPCIMD problem.

"Intelligent Design Approach for Three-phase Cage Induction Motor Using Glowworm Swarm Optimization Algorithm", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.4, Issue 9, page no.562-568, September-2017, Available :http://www.jetir.org/papers/JETIR1709084.pdf

"Intelligent Design Approach for Three-phase Cage Induction Motor Using Glowworm Swarm Optimization Algorithm", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.4, Issue 9, page no. pp562-568, September-2017, Available at : http://www.jetir.org/papers/JETIR1709084.pdf