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首页> 外文期刊>Knowledge-Based Systems >An effective gbest-guided gravitational search algorithm for real-parameter optimization and its application in training of feedforward neural networks
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An effective gbest-guided gravitational search algorithm for real-parameter optimization and its application in training of feedforward neural networks

机译：一种有效的gbest制导引力搜索算法，用于实参优化及其在前馈神经网络训练中的应用

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Gravitational search algorithm (GSA) is a recently introduced meta-heuristic that has shown great performance in numerical function optimization and solving real world problems. GSA provides an excellent social interaction between its search agents. This social interaction results in admirable exploration of the search space and gives a unique social component to GSA. However, the social interaction is not able to exploit good solutions in an efficient manner. To overcome this problem, a novel algorithm named as gbest-guided gravitational search algorithm (GG-GSA) has been proposed by utilizing the global best (gbest) solution in the force calculation equation of GSA. The employment of gbest solution in any optimization algorithm is a tough task and can lead to premature convergence. In the proposed algorithm, the gbest solution is used adaptively and is able to achieve a better trade-off between exploration and exploitation. The performance of the proposed algorithm is compared with GSA and its variants on different suites of well-known benchmark test functions. The experimental results show that the GG-GSA performs better than other algorithms for most of the benchmark test functions. Furthermore, to test the ability of the proposed algorithm in solving real world applications, training of feedforward neural network problem is chosen. The results demonstrated the exceptional performance of GG-GSA on real world data-set. (C) 2017 Elsevier B.V. All rights reserved.
机译：引力搜索算法（GSA）是最近引入的一种元启发式算法，在数值函数优化和解决现实世界中的问题中表现出了出色的性能。 GSA在其搜索代理之间提供了出色的社交互动。这种社交互动可令人钦佩地探索搜索空间，并为GSA提供了独特的社交元素。但是，社交互动无法有效地利用好的解决方案。为了克服这个问题，通过在GSA的力计算方程中利用全局最佳（gbest）解，提出了一种称为gbest制导重力搜索算法（GG-GSA）的新算法。在任何优化算法中采用gbest解决方案都是一项艰巨的任务，并且可能导致过早收敛。在提出的算法中，gbest解决方案被自适应地使用，并且能够在勘探与开发之间实现更好的权衡。将该算法的性能与GSA及其变体在不同的知名基准测试功能套件上进行了比较。实验结果表明，对于大多数基准测试功能，GG-GSA的性能优于其他算法。此外，为了测试提出的算法在解决实际应用中的能力，选择了前馈神经网络问题的训练。结果证明了GG-GSA在真实数据集上的出色表现。 （C）2017 Elsevier B.V.保留所有权利。

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