Multi-chaotic Sparrow Search Algorithm Based on Learning Mechanism

Abstract

To solve the shortcomings of sparrow search algorithm (SSA), such as falling into local extremum easily influenced by initial solution and slow convergence in late iteration, a multi-chaotic sparrow search algorithm  based on learning mechanism (MMCSSA) is proposed. Firstly, the centroid opposition-based learning strategy (COBL) is introduced to generate elite population to enhance the exploration of multi-source high-quality search areas, and then the local extreme escape ability and convergence performance of the algorithm are improved. Secondly, a scaled golden sine algorithm is proposed and embedded in SSA to improve the guidance search mode and enhance the global search ability of the algorithm. Thirdly, a multi-chaos mapping strategy based on learning mechanism is proposed, which utilizes the characteristics of multi-chaos and multi-disturbance, and enforces different disturb-ance features on the algorithm by dynamically calling different chaotic maps. When chaotic perturbation fails, Gaussian mutation strategy is introduced to deeply develop the current solution. The two strategies cooperate and promote each other, greatly enhancing the ability of the algorithm to escape from local optimal. Finally, the proposed algorithm is applied to 12 benchmark functions with different characteristics, and the results show that compared with other algorithms, MMCSSA has better performance in convergence accuracy, optimization speed and robustness.

Keywords

• sparrow search algorithm (ssa); golden sine algorithm; gaussian mutation; multiple chaotic learning mechanisms; centroid opposition-based learning strategy (cobl)