Implementation of an H-PSOGA Optimization Model for Vehicle Routing Problem

Background To optimize α-amylase synthesis from fermented broth, this research employed evolutionary and swarm intelligence-based techniques such as genetic algorithm (GA) and particle swarm optimisation (PSO). The nonlinear response surface model (RSM) of α-amylase from Bacillus velezensis sp.was used as the objective function. Results In contrast to the enzyme activity, 418.25 U/mL, achieved by the thirteen factors OFAT technique was increased by 2.76 times using GA and PSO optimization with only nine significant parameters. The optimal process parameters predicted using GA and PSO were pH (5.37), temperature (34.18 oC), carbon source (4.12%), nitrogen source (2.04%), K2PO4 (0.34%), MgSO4 (0.2%), NaCl (0.14%), fructose (2.0%), and NaNO3 (0.47%). Conclusions The results showed that α-amylase activity was significantly improved with both approaches, with similar predictions of optimized process parameters and α-amylase activity of 1157.2 U/ml. PSO, on the other hand, surpasses GA in terms of optimized α-amylase activity and convergence rate, which might be attributed to its simple structure and efficient memory capabilities. In conclusion, the suggested GA and PSO techniques are thought to be natural and may be used instead of existing gradient-based optimization strategies in downstream enzyme processing.

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Evolutionary and Swarm Intelligence in Optimization of α-Amylase From Bacillus velezensis Sp.

SASIDHAR,

RAVINDRAN

2023

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Background To optimize α-amylase synthesis from fermented broth, this research employed evolutionary and swarm intelligence-based techniques such as genetic algorithm (GA) and particle swarm optimisation (PSO). The nonlinear response surface model (RSM) of α-amylase from Bacillus velezensis sp.was used as the objective function. Results In contrast to the enzyme activity, 418.25 U/mL, achieved by the thirteen factors OFAT technique was increased by 2.76 times using GA and PSO optimization with only nine significant parameters. The optimal process parameters predicted using GA and PSO were pH (5.37), temperature (34.18 oC), carbon source (4.12%), nitrogen source (2.04%), K2PO4 (0.34%), MgSO4 (0.2%), NaCl (0.14%), fructose (2.0%), and NaNO3 (0.47%). Conclusions The results showed that α-amylase activity was significantly improved with both approaches, with similar predictions of optimized process parameters and α-amylase activity of 1157.2 U/ml. PSO, on the other hand, surpasses GA in terms of optimized α-amylase activity and convergence rate, which might be attributed to its simple structure and efficient memory capabilities. In conclusion, the suggested GA and PSO techniques are thought to be natural and may be used instead of existing gradient-based optimization strategies in downstream enzyme processing.

show abstract

Implementation of an H-PSOGA Optimization Model for Vehicle Routing Problem

Cited by 3 publications

References 45 publications

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Evolutionary and Swarm Intelligence in Optimization of Α-amylase From Bacillus Velezensis Sp.

Evolutionary and Swarm Intelligence in Optimization of α-Amylase From Bacillus velezensis Sp.

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