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首页> 外文期刊>Arabian Journal for Science and Engineering >Improved Salp Swarm Algorithm with Space Transformation Search for Training Neural Network
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Improved Salp Swarm Algorithm with Space Transformation Search for Training Neural Network

机译：带空间变换搜索的改进Salp Swarm算法在神经网络训练中的应用

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摘要

Swarm-based algorithm is best suitable when it can perform smooth balance between the exploration and exploitation aswell as faster convergence by successfully avoiding local optima entrapment. At recent time, salp swarm algorithm (SSA)is developed as a nature-inspired swarm-based algorithm. It can solve continuous, nonlinear and complex in nature day-todaylife optimization problems. Like many other optimization algorithms, SSA suffers with the problem of local stagnation.This paper introduces an improved version of the SSA, which improves the performance of the existing SSA by using spacetransformation search (STS). The proposed algorithm is termed as STS-SSA. The STS-SSA enhances the exploration andexploitation capability in the search space and successfully avoids local optima entrapment. The STS-SSA is evaluated byconsidering the IEEE CEC 2017 standard benchmark function set. The efficiency and robustness of the proposed STS-SSAare measured using performance metrics, convergence analysis and statistical significance. A demonstration is given as anapplication of the proposed algorithm for solving a real-life problem. For this purpose, the multi-layer feed-forward networkis trained using the proposed STS-SSA. The experimental results demonstrate that the developed STS-SSA can be used forsolving optimization problems effectively.
机译：基于群体算法的算法可以通过成功地避免局部最优陷波而在勘探与开发之间实现平稳的平衡，并实现更快的收敛速度，因此是最合适的算法。最近，作为一种自然启发式的基于群算法的算法开发了Salp群算法（SSA）。它可以解决自然界中日常工作中连续，非线性和复杂的优化问题。像许多其他优化算法一样，SSA也存在局部停滞的问题。本文介绍了SSA的改进版本，该版本通过使用空间变换搜索（STS）来提高现有SSA的性能。提出的算法称为STS-SSA。 STS-SSA增强了搜索空间中的探索和开发能力，并成功避免了局部最优陷阱。通过考虑IEEE CEC 2017标准基准功能集来评估STS-SSA。拟议的STS-SSA的效率和鲁棒性通过性能指标，收敛性分析和统计显着性来衡量。给出了作为所提出的算法解决实际问题的应用的演示。为此，使用建议的STS-SSA训练多层前馈网络。实验结果表明，所开发的STS-SSA可以有效地解决优化问题。

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