Investigation and application of sequential quadratic programming based on simulated annealing

In order to achieve optimal control on mechanism model of fermentation process, We studied the global stochastic methods (simulated annealing algorithm SA) and local deterministic method (sequential quadratic programming SQP), Taking into account the full use of their complementary strengths: some global convergence properties, in the case of the stochastic strategy, and fast convergence if started close to the global solution, in the case of the deterministic approach. The paper proposes a novel Hybrid algorithm which combines the global stochastic method (SA) and the local deterministic method (SQP). In the hybrid algorithm simulated annealing method is introduced in the SQP algorithm to generate new solutions out of local minima, and improves the SA sampling criteria. The test results of several classical COPs using the hybrid algorithm proved the effectiveness of the hybrid algorithm for constrained optimization problems.