Improved Gradient-Based Optimizer for solving real-world engineering problems

M. Shehab, Omar Tarawneh, Hani AbuSalem, Fatima Shannag, Walaa Al-Omari
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引用次数: 0

Abstract

Gradient-based optimizer (GBO) is one of the most promising metaheuristic algorithms, where it proved its efficiency in various fields. GBO combine two major search mechanisms population-based and gradient-based Newton. Thus, it has a strong ability in global search. However, it suffers from dealing with local search problems. In this paper, a new version introduces which integrates the feature of Simulating annealing method (SA) with the GBO (GBOSA) to enhance the local search technique. The proposed GBOSA has been compared with various popular algorithms and improved variants on a set of real-world engineering problems. The experiment results show that GBOSA outperformed the other algorithms in the literature.
改进的基于梯度的优化器,用于解决实际工程问题
基于梯度的优化器(Gradient-based optimizer, GBO)是一种最有前途的元启发式算法,在各个领域都证明了它的有效性。GBO结合了基于人口和基于梯度的牛顿两种主要的搜索机制。因此,它具有很强的全局搜索能力。然而,它在处理局部搜索问题方面存在问题。本文介绍了一种将模拟退火法(SA)与GBO (GBOSA)相结合的新版本,以增强局部搜索技术。提出的GBOSA已经与各种流行的算法和改进的变体在一组现实世界的工程问题上进行了比较。实验结果表明,GBOSA算法优于文献中的其他算法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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