Bio-inspired computing has progressed so far to deal with real-time multi-objective optimization problems. The Transmission expansion planning of the modern electricity grid requires finding the best and optimal routes for electricity transmission from the generation point to the endpoint while satisfying all the power and load constraints. Further, the transmission expansion cost allocation becomes a critical and pragmatic issue in the deregulated electricity industry. The prime objective is to minimize the total investment and expansion costs while considering N-1 contingency. The most optimal transmission expansion planning problem's solution is calculated using the objective function and the constraints. This optimal solution provides the total number and best locations for the candidates. The presented paper details the mathematical modeling of the shuffled frog leap algorithm with various modifications applied to the method to refine the results and finally proposes an enhanced novel approach to solve the transmission expansion planning problem. The proposed algorithm produces the expansion plans based on target-based evolution. The presented algorithm is rigorously tested on the standard Garver dataset and IEEE 24 bus system. The empirical results of the proposed algorithm led to better expansion plans while effectively considering typical electrical constraints along with modern and realistic constraints.
Keywords: enhanced modified shuffled frog leap algorithm; frog leap optimization; meta heuristic approach; modified shuffled frog leap algorithm.