A Spring Search Algorithm Applied to Engineering Optimization Problems
A Spring Search Algorithm Applied to Engineering Optimization Problems
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DOI：
10.3390/app10186173
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发表时间：
2020-09-01
影响因子：
2.7
通讯作者：
Parra-Arroyo, Lizeth
中科院分区：
文献类型：
--
作者：
Dehghani, Mohammad;Montazeri, Zeinab;Parra-Arroyo, Lizeth
目前，优化算法被广泛使用。一种特定类型的这种算法包括基于随机的启发式种群优化算法，其可以通过对科学现象（例如，物理过程）进行建模来创建。本文提出了一种新的优化算法，称为弹簧搜索算法（SSA）的基础上胡克的法律，旨在解决单目标约束优化问题。在SSA中，搜索代理是通过弹簧连接的权重，正如胡克定律所述，弹簧具有与其长度相对应的力。算法背后的数学在文中介绍。为了测试其功能，它在38个已建立的基准测试函数上执行，并与其他八个优化算法进行权衡：遗传算法（GA）、引力搜索算法（GSA）、草蜢优化算法（果阿）、粒子群优化（PSO）、基于教学的优化（TLBO）、灰狼优化器（GWO）、斑点鬣狗优化器（SHO）以及帝企鹅优化器（EPO）。为了测试SSA的可用性，它被用于五个工程优化问题。SSA在单峰目标函数、多峰目标函数、CEC 2015以及工程中的优化问题上都比其他算法有更好的拟合效果。
At present, optimization algorithms are used extensively. One particular type of such algorithms includes random-based heuristic population optimization algorithms, which may be created by modeling scientific phenomena, like, for example, physical processes. The present article proposes a novel optimization algorithm based on Hooke's law, called the spring search algorithm (SSA), which aims to solve single-objective constrained optimization problems. In the SSA, search agents are weights joined through springs, which, as Hooke's law states, possess a force that corresponds to its length. The mathematics behind the algorithm are presented in the text. In order to test its functionality, it is executed on 38 established benchmark test functions and weighed against eight other optimization algorithms: a genetic algorithm (GA), a gravitational search algorithm (GSA), a grasshopper optimization algorithm (GOA), particle swarm optimization (PSO), teaching-learning-based optimization (TLBO), a grey wolf optimizer (GWO), a spotted hyena optimizer (SHO), as well as an emperor penguin optimizer (EPO). To test the SSA's usability, it is employed on five engineering optimization problems. The SSA delivered better fitting results than the other algorithms in unimodal objective function, multimodal objective functions, CEC 2015, in addition to the optimization problems in engineering.