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Discrete optimum design of truss structures using artificial bee colony algorithm

机译:基于人工蜂群算法的桁架结构离散优化设计

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摘要

Over the past few years, swarm intelligence based optimization techniques such as ant colony optimization and particle swarm optimization have received considerable attention from engineering researchers and practitioners. These algorithms have been used in the solution of various engineering problems. Recently, a relatively new swarm based optimization algorithm called the Artificial Bee Colony (ABC) algorithm has begun to attract interest from researchers to solve optimization problems. The aim of this study is to present an optimization algorithm based on the ABC algorithm for the discrete optimum design of truss structures. The ABC algorithm is a meta-heuristic optimization technique that mimics the process of food foraging of honeybees. Originally the ABC algorithm was developed for continuous function optimization problems. This paper describes the modifications made to the ABC algorithm in order to solve discrete optimization problems and to improve the algorithm's performance. In order to demonstrate the effectiveness of the modified algorithm, four structural problems with up to 582 truss members and 29 design variables were solved and the results were compared with those obtained using other well-known meta-heuristic search techniques. The results demonstrate that the ABC algorithm is very effective and robust for the discrete optimization designs of truss structural problems.
机译:在过去的几年中,基于蚁群优化和粒子群优化等基于群体智能的优化技术受到了工程研究人员和实践者的极大关注。这些算法已用于解决各种工程问题。最近,一种称为“人工蜂群”(ABC)算法的相对较新的基于群体的优化算法已开始引起研究人员对解决优化问题的兴趣。这项研究的目的是为桁架结构的离散优化设计提出一种基于ABC算法的优化算法。 ABC算法是一种元启发式优化技术,可模仿蜜蜂的食物觅食过程。最初,ABC算法是针对连续函数优化问题而开发的。本文介绍了对ABC算法所做的修改,以解决离散优化问题并提高算法的性能。为了证明改进算法的有效性,解决了多达582个桁架成员和29个设计变量的四个结构问题,并将结果与​​使用其他知名的元启发式搜索技术获得的结果进行了比较。结果表明,ABC算法对于桁架结构问题的离散优化设计非常有效且鲁棒。

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