Li / Liu Group Search Optimization for Applications in Structural Design

1. Auflage 2011
ISBN: 978-3-642-20536-1
Verlag: Springer
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, Band 9, 257 Seiten

Reihe: Adaptation, Learning, and Optimization

Civil engineering structures such as buildings, bridges, stadiums, and offshore structures play an import role in our daily life. However, constructing these structures requires lots of budget. Thus, how to cost-efficiently design structures satisfying all required design constraints is an important factor to structural engineers. Traditionally, mathematical gradient-based optimal techniques have been applied to the design of optimal structures. While, many practical engineering optimal problems are very complex and hard to solve by traditional method. In the past few decades, swarm intelligence algorithms, which were inspired by the social behaviour of natural animals such as fish schooling and bird flocking, were developed  because they do not require conventional mathematical assumptions and thus possess better global search abilities than the traditional optimization algorithms and  have attracted more and more attention. These intelligent based algorithms are very suitable for continuous and discrete design variable problems such as ready-made structural members and have been vigorously applied to various structural design problems and obtained good results. This book gathers the authors' latest research work related with particle swarm optimizer algorithm and group search optimizer algorithm as well as their application to structural optimal design. The readers can understand the full spectrum of the algorithms and apply the algorithms to their own research problems. 

Autoren/Hrsg.

Li, Lijuan

Liu, Feng

Inhaltsverzeichnis

1;Title;2
2;Preface;6
3;Contents;7
4;Introduction of Swarm Intelligent Algorithms;11
4.1;References;14
5;Application of Particle Swarm Optimization Algorithm to Engineering Structures;17
5.1;Introduction;17
5.2;Problem Statement;18
5.3;Particle Swarm Optimizer;20
5.4;Mixed-Variable Handling Methods;21
5.5;The Constraint Handling Method;22
5.6;Application of the PSO in Structural Optimization Problems;24
5.7;Examples;25
5.8;Conclusions;29
5.9;References;29
6;Optimum Design of Structures with Heuristic Particle Swarm Optimization Algorithm;31
6.1;Introduction;31
6.2;Constraint Handling Method: Fly-Back Mechanism;33
6.3;A Heuristic Particle Swarm Optimization (HPSO);33
6.4;The Application of the HPSO on Truss Structures with Continuous Variables;36
6.4.1;Numerical Examples;36
6.5;The Application of the HPSO on Truss Structures with Discrete Variables;50
6.5.1;Mathematical Model for Discrete Structural Optimization Problems;50
6.5.2;The Heuristic Particle Swarm Optimizer (HPSO) for Discrete Variables;51
6.5.3;Numerical Examples;52
6.6;The Weight Optimization of Grid Spherical Shell Structure;67
6.7;The Application of the HPSO on Plates with Discrete Variables;68
6.7.1;Numerical Examples;68
6.8;Conclusions Remarks;75
6.9;References;76
7;Optimum Design of Structures with Group Search Optimizer Algorithm;78
7.1;Introduction;78
7.2;Group Search Optimizer (GSO);79
7.3;The Application of the GSO to Truss Structure Optimal Design;82
7.3.1;The 10-Bar Planar Truss Structure;83
7.3.2;The 17-Bar Planar Truss Structure;84
7.3.3;The 22-Bar Space Truss Structure;86
7.3.4;The 25-Bar Space Truss Structure;88
7.3.5;The 72-Bar Space Truss Structure;91
7.4;The Application of GSO to Frame Cross-Section Optimization;94
7.4.1;The Optimal Model of Frame Cross-Section Optimization;94
7.4.2;Numerical Examples;95
7.5;Conclusions;104
7.6;References;104
8;Improvements and Applications of Group Search Optimizer in Structural Optimal Design;106
8.1;Introduction;106
8.2;Group Search Optimizer;107
8.3;The Improvement of GSO (IGSO);111
8.4;The Application of the IGSO on Truss Shape Optimization;112
8.4.1;Mathematical Models;113
8.4.2;Numerical Examples;113
8.5;The Application of IGSO in Truss Shape Optimization with Dynamic Constraint;136
8.5.1;Two Kinds of Mathematical Model for Truss Dynamic Shape Optimization Problems;136
8.5.2;Numerical Examples;137
8.6;The Application of IGSO in Truss Topology Optimization;149
8.6.1;Topology Optimization Model;149
8.6.2;Topology Methods;150
8.6.3;Numerical Example;152
8.7;Conclusions Remarks;165
8.8;References;166
9;Optimum Design of Structures with Quick Group Search Optimization Algorithm;169
9.1;Introduction;169
9.2;The Introduction of GSO;170
9.3;Constraint Handling Method;172
9.4;The Quick Group Search Optimizer (QGSO);173
9.4.1;The QGSO Algorithm for the Discrete Variables;175
9.5;The Application of the QGSO on Truss Structures with Continuous Variables;176
9.5.1;Numerical Examples;177
9.6;The Application of the QGSO on Truss Structures with Discrete Variables;190
9.6.1;Numerical Examples;191
9.6.2;Conclusion;204
9.7;Stability Studies;205
9.8;The Quick Group Search Optimizer with Passive Congregation (QGSOPC);205
9.8.1;Numerical Examples;206
9.8.2;Conclusion;212
9.9;Conclusion Remark;212
9.10;References;213
10;Group Search Optimizer and Its Applications on Multi-objective Structural Optimal Design;215
10.1;Introduction;215
10.2;Multi-objective Optimization Concepts;216
10.2.1;Multi-objective Optimization Problem;216
10.2.2;Feasible Set;216
10.2.3;For Any Two Objective Vectors u and v:;217
10.2.4;Pareto Optimality;217
10.2.5;Pareto Dominance;217
10.2.6;Pareto Optimal Set and Pareto Front;217
10.2.7;Pareto Constraint-Dominance;218
10.2.8;The Measure of Multi-objective Optimization Results;218
10.3;Group Search Optimizer;218
10.3.1;Group Search Optimizer for Continuous Variables;218
10.3.2;Group Search Optimizer for Discrete Variables;220
10.4;Multi-objective Group Search Optimizer (MGSO) and Calculation Procedure;221
10.4.1;Key Issues of Multi-objective GSO Algorithm;222
10.4.2;The Structure of Multi-objective Search Group Optimizer;223
10.5;Truss Optimization;224
10.5.1;The Size Optimization of Truss;225
10.5.2;The Shape Optimization of Truss;230
10.5.3;The Dynamic Optimization of Truss;238
10.6;The Optimization of Frame;243
10.6.1;The One Bay Eight Storey Planar Frame;244
10.6.2;The Two Bay Five Story Planar Frame;248
10.7;Conclusions;253
10.8;References;253
11;Prospecting Swarm Intelligent Algorithms;255
11.1;References;257