Teaching-learning-based optimization (TLBO) is an intelligent optimization algorithm with relatively fewer parameters that should be determined in updating equations. For solving complex optimization problems, the local optima often appear in the evolution. To decrease the possibility of this phenomenon, a novel TLBO variant (EI-TLBO) with experience information (EI) and differential mutation is presented. In the method, neighborhood information (the best individual NTeacher and the mean individual NMean) of each learner's neighbors is introduced to improve the exploration capability. The EI before the current iteration of each learner is introduced to make him or her accurately judge the learning behavior in future. In addition, instead of duplicate elimination to maintain the diversity of population at the end of each generation in the original TLBO, differential mutation is introduced to maintain the diversity of learners during the iterative learning process. The main contribution of this paper is to improve the convergence speed and accuracy by introducing neighborhood topology structure, EI, and differential mutation. The efficiency of the proposed algorithm is evaluated on 46 benchmark functions, among which 27 functions are selected from CEC2013. Its performance is compared with those of six other reported EAs. The results indicate that EI-TLBO algorithm can achieve superior performance.

Optimal approximation of linear system models is an important task in the controller design and simulation for complex dynamic systems. In this paper, we put forward a novel nature-based meta-heuristic method, called artificial raindrop algorithm, which is inspired from the phenomenon of natural rainfall, and apply it for optimal approximation of a stable linear system. It mimics the changing process of a raindrop, including the generation of raindrop, the descent of raindrop, the collision of raindrop, the flowing of raindrop and the updating of raindrop. Five corresponding operators are designed in the algorithm. Numerical experiment is carried on the optimal approximation of a typical stable linear system in two fixed search intervals. The result demonstrates better performance of the proposed algorithm comparing with that of other five state-of-the-art optimization algorithms.

The paper is focused on improving the performance of neuro-endocrine models with considering the interaction of glands. Comparing to conventional neuro-endocrine models, the concentration of hormone of one gland is modulated by those of others, and the weights of cells are modulated by the improved endocrine system. The interacted equation among all glands is designed and the parameters of them are chosen with theory analysis. Because all the parameters of the model are constants when the system reaches the equilibrium state, particle swarm optimization algorithm is utilized to search the optimal parameters of the model. The theory analysis indicates that the performance of neuro-endocrine model is better than or at least equal to that of corresponding artificial neural network. To indicate the effectiveness of the proposed model, some time series from different research fields, which are used in some literatures, are tested with the proposed model, the results indicate that the proposed model has some good performance.