A hybrid TLNNABC algorithm for reliability optimization and engineering design problems

The paper aims to present a new TLNNABC hybrid algorithm to solve reliability and engineering design optimization problems. In this algorithm, the structure of the artificial bee colony (ABC) algorithm has been improved by incorporating the features of the neural network algorithm (NNA) and teaching-learning based optimization (TLBO). In the standard ABC, the onlooker bees apply the same searching method as the employed bees, which causes slow convergence and also restricts its practical application of solving optimization problems. In view of this inadequacy and resulting in a better balance between exploration and exploitation, searching procedures for employed bees and onlooker bees of the conventional ABC are renovated based on NNA and improved TLBO algorithms respectively and a new hybrid algorithm called TLNNABC has been developed in this paper. In TLNNABC, for the employed bee phase, NNA is used to increase the population diversity. However, the improved teaching learning-based optimization is embedded in the onlooker bee phase. In this context, a new search operator is introduced which increases the exploitation capability of the algorithm to operate, and a probabilistic selection strategy, which helps to determine whether to apply the original or the new search operator to construct a new solution. Finally, the performance of the proposed TLNNABC algorithm has been demonstrated by the well-known benchmark problems related to reliability optimization, structural engineering design problems, and 23 unconstrained benchmark functions and finally compared with several existing algorithms. Experimental results show that the proposed algorithm is very effective and achieves superior performance than the other algorithms.