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首页> 外文期刊>Soft computing: A fusion of foundations, methodologies and applications >Enhanced Salp Swarm Algorithm based on random walk and its application to training feedforward neural networks
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Enhanced Salp Swarm Algorithm based on random walk and its application to training feedforward neural networks

机译：基于随机步行的增强型SALP群算法及其在训练前馈神经网络的应用

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摘要

Salp Swarm Algorithm (SSA) is a new type of metaheuristic and has shown superiority over other well-known algorithms such as Particle Swarm Optimization and Grey Wolf Optimizer in solving challenging optimization problems. Despite its superior performance, SSA still has problems such as insufficient convergence speed. Moreover, its local optima avoidance ability is not as good as those evolutionary algorithms using crossover operators. In this paper, we propose a modified Salp Swarm Algorithm (m-SSA) which improves the exploitation and exploration of SSA by integrating random walk strategy and especially enhances exploration by adding a new controlling parameter. In addition, a simulated annealing-type acceptance criterion is adopted to accept the fittest follower position as the new best leader position. The performance of the proposed algorithm is benchmarked on a set of classical functions and CEC2014 test suite. The proposed algorithm (m-SSA) outperforms SSA significantly on most test functions. When compared with other state-of-the-art metaheuristics, it also presents very competitive results. Besides, we apply the proposed algorithm on training feedforward neural networks (FNNs) and the results prove the effectiveness and efficiency of m-SSA.
机译：SALP Swarm算法（SSA）是一种新型的成式型，并在其他众所周知的算法之类的诸如粒子群优化和灰狼优化器等其他知名算法中显示出优势。尽管其性能卓越，但SSA仍然存在诸如收敛速度不足的问题。此外，其本地最佳避免能力与使用交叉运算符的进化算法不如这些进化算法。在本文中，我们提出了一种改进的SALP群算法（M-SSA），通过集成随机步行策略来提高SSA的开发和探索，特别是通过添加新的控制参数来提高探索。此外，采用模拟的退火型验收标准接受最适合的追随者位置作为新的最佳领导位置。所提出的算法的性能在一组经典功能和CEC2014测试套件上是基准测试。所提出的算法（M-SSA）在大多数测试功能上显着优于SSA。与其他最先进的美容相比，它也呈现出非常有竞争力的结果。此外，我们在培训前馈神经网络（FNNS）上应用了所提出的算法，结果证明了M-SSA的有效性和效率。

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