• UGANDHAR PULI
• PHANI KRISHNA KADIYALA
• VENKATASUBBAIAH K

This study has been undertaken to optimize void fraction of subcooled flow boiling by using evolutionary techniques like Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). Experimental results of void fraction are validated by Feed Forward Back Propagation Artificial Neural Network and ANN is integrated with GA and PSO for optimizing void fraction. Comparative performance analysis of GA and PSO is done and it is observed that for the present adopted problem, percentage of error for GA is 9.8% and while for PSO is 4.1%. In terms of time reaching the optimum solution, Particle Swarm Optimization is 17 times faster than Genetic Algorithm.

Void Fraction, Genetic Algorithm, Particle Swarm Optimization, Optimization

"COMPARATIVE PERFORMANCE ANALYSIS OF GA AND PSO IN OPTIMIZING VOID FRACTION", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.8, Issue 12, page no.d830-d835, December-2021, Available :http://www.jetir.org/papers/JETIR2112397.pdf

"COMPARATIVE PERFORMANCE ANALYSIS OF GA AND PSO IN OPTIMIZING VOID FRACTION", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.8, Issue 12, page no. ppd830-d835, December-2021, Available at : http://www.jetir.org/papers/JETIR2112397.pdf