Grey Wolf Optimizer (GWO) Algorithm for Minimum Weight Planer Frame Design Subjected to AISC-LRFD

被引：6

作者：

Bhensdadia, Vishwesh ^{[1
]}

Tejani, Ghanshyam ^{[1
]}

机构：

[1] RK Univ, Sch Engn, Rajkot, Gujarat, India

来源：

PROCEEDINGS OF INTERNATIONAL CONFERENCE ON ICT FOR SUSTAINABLE DEVELOPMENT ICT4SD 2015, VOL 2 | 2016年 / 409卷

关键词：

Structural optimization; Meta-heuristics; Grey wolf optimizer (GWO) algorithm; Planer frame design; AISC-LRFD; CHARGED SYSTEM SEARCH; STEEL FRAMES; OPTIMUM DESIGN; STRATEGY;

D O I：

10.1007/978-981-10-0135-2_13

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, an optimum planer frame design is achieved using the Grey Wolf Optimizer (GWO) algorithm. The GWO algorithm is a nature involved meta-heuristic which is correlated with grey wolves' activities in social hierarchy. The objective of the GWO algorithm is to produce minimum weight planer frame considering the material strength requirements specified by American Institute for Steel Construction-Load and Resistance Factor Design (AISC-LRFD). The frame design is produced by choosing the W-shaped cross sections from AISC-LRFD steel sections for a beam and column members. A benchmark problem is investigated in the present work to monitor the success rate in a way of best solution and effectiveness of the GWO algorithm. The result of the GWO algorithm is compared with other meta-heuristics, namely GA, ACO, TLBO and EHS. The results show that the GWO algorithm gives better design solutions compared to other meta-heuristics.

引用

页码：143 / 151

页数：9

共 23 条

[1]

American Institute of Steel Construction (AISC), 1994, Manual of Steel Construction, Load & Resistance Factor Design, V2nd

[2]

[Anonymous], 2011, ASIAN J CIVIL ENG BU

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