Research And Application On Teaching And Learning Algorithm Based On Gaussian Distribution

Teaching-leaning based optimization(TLBO)is a novel intelligence optimization algorithm,which simulates the learning process of human,to seek optimal solution of the problem.The advantage of this algorithm with its less parameters,simple,easy to understand,speed,can be widely used to solve various optimization problems.But,TLBO algorithm is easy to fall into local optimal solution due to the fast converge speed,which can affect the global optimization ability of the algorithm.As for this,in this paper we do research work around this.The main content of this paper is as follows:(1)This paper proposes a teaching and learning mechanism and Gaussian distribution based hybrid optimization algorithm(GTLBO).First,this paper uses the opposition-based learning to initialize the population to improve the quality of initial solutions.Second,Gaussian distribution is combined to the teaching phase to increase the efficiency of global search.In addition,in the learning phase we join the crossover operation,the retention of historical information is conducive to jump out of local optimal.Finally,33 standard functions are selected to verify,and compared to other similar TLBO optimization algorithms.Experimental results show that compared with other classes of TLBO algorithm,GTLBO can effectively improve the global optimization ability.(2)The GTLBO is adopted to solve the no-wait flow shop scheduling with the objective of minimization of makespan.The encoding mapping mechanism is used to map the continuous individuals to the job sequences,and the local search methods is adopted to further improve the quality of solutions.The experimental results demonstrate that the GTLBO can solve.The experimental results show that the GTLBO algorithm can solve the typical hybrid optimization problem of the no-wait FSP effectively.