Intelligent neighbourhood teaching learning based optimization algorithm

Teaching Learning Based Optimization Algorithm (TLBOA) is an efficient approach of dealing with linear, nonlinear and multidimensional optimization problems. It depicts the classroom behaviour of teachers and students (learners). The algorithm is divided in two phases, namely Teacher phase and Learner phase. The position update process of learner phase is based on the difference vector of two randomly selected solutions, which improves the diversification ability of the algorithm. Further, in case of high step size, the solutions may skip the global optima. Therefore, to improve the exploitation ability and to control the step size, a new phase called as intelligent phase inspired from the Onlooker bee phase of Artificial Bee Colony optimization algorithm, is incorporated with the TLBO. In the propounded phase, intelligent solutions will be given more chances in participation of the position update process. This will leads to high convergence in the swarm. The performance of this propounded algorithm is calculated over 15 benchmark functions and compared with other state of art optimization algorithms namely TLBO, Gravitational Search Algorithm (GSA), and Grey Wolf Optimizer (GWO).