Evaluation of Lipase Production by Genetic Algorithm and Particle Swarm Optimization and Their Comparative Study

Abstract

This paper presents the nature-inspired genetic algorithm (GA) and particle swarm optimization (PSO) approaches for optimization of fermentation conditions of lipase production for enhanced lipase activity. The central composite non-linear regression model of lipase production served as the optimization problem for PSO and GA approaches. The overall optimized fermentation conditions obtained thereby, when verified experimentally, have brought about a significant improvement (more than 15 U/gds (gram dry substrate)) in the lipase titer value. The performance of both optimization approaches in terms of computational time and convergence rate has been compared. The results show that the PSO approach (96.18 U/gds in 46 generations) has slightly better performance and possesses better convergence and computational efficiency than the GA approach (95.34 U/gds in 337 generations). Hence, the proposed PSO approach with the minimal parameter tuning is a viable tool for optimization of fermentation conditions of enzyme production.

Naïve Bayes ant colony optimization for designing high dimensional experiments

“…To understand how NACO behaved when the dimension of the search space increased we considered various values for k and d, corresponding to dim(X) = k d ≈ 10 4 , 10 5 and 10 6 . The results are reported in Table 1.…”

“…Different optimization approaches have been proposed in the literature to tackle problems with high dimensional and complex search spaces [5][6][7][8][9][10][11]. Some approaches are based on Evolutionary Computation Algorithms [12].…”

Kinetic parameters estimation of protease production using penalty function method with hybrid genetic algorithm and particle swarm optimization

“…Each bound is divided to m sections; m is the same for the all kinetic parameters (at first m was set to 2) In the proposed hybrid GA/PSO algorithm, the search space is divided to m n subspaces (Equation (5)). Each subspace consists of a vector of solutions named x {x D ðx 1 i1 ; x 2 i2 ; :::; x d id Þ T , where each one of i1, i2, … and id are an integer in the range [1,10]}. Then a group of GAs fGA 1 ; GA 2 ; :::; GA m n g searches the best solution in each subspace.…”

“…[9] These optimization approaches can find global optima more quickly through cooperation and competition among the population of potential solutions of the search space even for complex optimization problems such as fermentation processes. [10] In recent years, both the GA and the PSO, as well as hybrid algorithms combining the two, have been applied to parameter estimation problems in the context of kinetic models of microorganism growth. [7,10À17] GA has an advantage in exploration search and PSO has an advantage in sharing movement information between particles.…”

Modeling, Simulation, and Kinetic Studies of Solvent‐Free Biosynthesis of Benzyl Acetate

“…This integrated approach works on the statistical-based response surface methodology (RSM) and artificial intelligence-based Genetic algorithm (GA) approach [11]. RSM is a powerful mathematical modeling approach which does not need the explicit expressions of the physical meaning of the system or process under investigation and develops nonparametric regression model.…”