Abstract
Swarm intelligence based optimization algorithms show great success to solve complex problems with high efficiency. Recently, a novel and heuristic algorithm, Bat searching algorithm (BA) has been proposed. Moreover, numerical evaluation has already demonstrated the better performance of BA compared with other algorithms variations. In this paper, we propose a coupled spring forced BA (SFBA) algorithm by considering that each particle is a spring and is coupled with the optimal solution found so far as the second abstract spring. The synergistic integration of the coupled springs, the bat's behavior, and swarm intelligence governs and navigates the new algorithm in the searching process. Moreover, the convergence of the SFBA is studied via Jury's Test. Numerical evaluation is provided for the proposed SFBA algorithm by conducting comparison with other variations of BA in the literature, which indicates that the performance of SFBA surpasses all the listed variations of BA significantly. Moreover, the proposed SFBA is applied so solve a large-scale energy resource management in uncertain environments, and the results are numerically compared with other BA algorithms.
Differential evolution strategies for large-scale energy resource management in smart grids
