The Group Search Optimizer and its Application to Truss Structure Design

This paper introduces a novel optimization algorithm, group search optimization (GSO) algorithm and its implementation method is presented in detail. The GSO is used to investigate the planar and space truss structures with continuous variables and is tested by two truss optimization problems. The optimization results are compared with that of the particle swarm optimization (PSO) algorithm, the particle swarm optimization with passive congregation (PSOPC) and the heuristic particle swarm optimizer (HPSO) algorithm. Results from the two tested cases illustrate the ability of the GSO algorithm to find the optimal results, which are better than that of the PSO and PSOPC, while are at the same level of that of HPSO optimization method. Meanwhile, the results also show that the GSO algorithm maintains a preferable convergence accuracy among these four algorithms.

[1]  Q. H. Wu,et al.  A heuristic particle swarm optimizer for optimization of pin connected structures , 2007 .

[2]  L I Li,et al.  AN IMPROVED PARTICLE SWARM OPTIMIZATION METHOD AND ITS APPLICATION IN CIVIL ENGINEERING , 2006 .

[3]  Feng Liu,et al.  A heuristic particle swarm optimization method for truss structures with discrete variables , 2009 .

[4]  Luca Maria Gambardella,et al.  Ant Algorithms for Discrete Optimization , 1999, Artificial Life.

[5]  Shang He,et al.  An improved particle swarm optimizer for mechanical design optimization problems , 2004 .

[6]  G. Steven,et al.  A performance-based optimization method for topology design of continuum structures with mean compliance constraints , 2002 .

[7]  W. J. O'brien,et al.  A new view of the predation cycle of a planktivorous fish, white crappie (Pomoxis annularis) , 1986 .

[8]  Q. Henry Wu,et al.  A Novel Group Search Optimizer Inspired by Animal Behavioural Ecology , 2006, 2006 IEEE International Conference on Evolutionary Computation.

[9]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[10]  S. Liversedge,et al.  Saccadic eye movements and cognition , 2000, Trends in Cognitive Sciences.

[11]  I. Couzin,et al.  Effective leadership and decision-making in animal groups on the move , 2005, Nature.

[12]  Kamran Behdinan,et al.  Particle swarm approach for structural design optimization , 2007 .

[13]  A. Kaveh,et al.  Optimal Design of Scissor-Link Foldable Structures Using Ant Colony Optimization Algorithm , 2007, Comput. Aided Civ. Infrastructure Eng..

[14]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[15]  R. Sibly,et al.  Producers and scroungers: A general model and its application to captive flocks of house sparrows , 1981, Animal Behaviour.

[16]  Y. Xie,et al.  A simple evolutionary procedure for structural optimization , 1993 .

[17]  A. Dixon AN EXPERIMENTAL STUDY OF THE SEARCHING BEHAVIOUR OF THE PREDATORY COCCINELLID BEETLE ADALIA DECEMPUNCTATA (L.) , 1959 .