Differential Evolution with Better and Nearest Option for Function Optimization
This is an incremental improvement for researchers and practitioners in optimization algorithms, addressing a known bottleneck in DE.
The paper tackles the problem of differential evolution (DE) getting trapped in local optima by proposing NbDE, a new DE algorithm with a better and nearest option mutation strategy, which outperforms other meta-heuristic algorithms in convergence speed and accuracy on nine benchmark test functions.
Differential evolution(DE) is a conventional algorithm with fast convergence speed. However, DE may be trapped in local optimal solution easily. Many researchers devote themselves to improving DE. In our previously work, whale swarm algorithm have shown its strong searching performance due to its niching based mutation strategy. Based on this fact, we propose a new DE algorithm called DE with Better and Nearest option (NbDE). In order to evaluate the performance of NbDE, NbDE is compared with several meta-heuristic algorithms on nine classical benchmark test functions with different dimensions. The results show that NbDE outperforms other algorithms in convergence speed and accuracy.