teaching–learning based optimization (tlbo)

In this paper, the Forward-Backward (FB) strategy has been developed to solve the load flow (LF) problem of a large-scale multi-carrier network. The Teaching–Learning-Based Optimization (TLBO), as a powerful heuristic tool, has been used for the optimization of the proposed LF. For this purpose, a large-scale unbalanced multi-carrier energy system (MCES) including an IEEE 33-bus system, a natural gas network with 25 nodes, and a heat network with 20 nodes have been considered to ascertain the applicability of the proposed FB-based machine learning strategy. By minimizing an objective function of the MCES system under unequal constraints, the TLBO tries to solve the optimization problem of MCES. The unbalanced current and voltage performance of the MCES system has been investigated by employing the current unbalance index (CUI) and voltage unbalance index (VUI). The numerical analysis with the application of the current unbalance index and voltage unbalance index has been studied to appraise the efficiency of the proposed optimal strategy to solve the LF problem of the MCES system.

The aim of this paper is to detect bank credit cards related frauds. The large amount of data and their similarity lead to a time consuming and low accurate separation of healthy and unhealthy samples behavior, by using traditional classifications. Therefore in this study, the Adaptive Neuro-Fuzzy Inference System (ANFIS) is used in order to reach a more efficient and accurate algorithm. By combining evolutionary algorithms with ANFIS, the optimal tuning of ANFIS parameters is achieved by the Teaching-Learning-Based Optimization (TLBO) and the Particle Swarm Optimization (PSO). The aim of using this approach is to improve the network performance and to reduce calculation complexities compared to gradient descent and least square methods. The proposed algorithm is implemented and evaluated on credit cards data to detect fraud. The results demonstrate superior performance of the designed scheme compared to other intelligent identification methods.