OPTIMIZATION OF SKELETAL STRUCTURAL USING ARTIFICIAL BEE COLONY ALGORITHM

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. These algorithms have been used in the solution of various structural optimization problems where the main goal is to minimize the weight of structures while satisfying all design requirements imposed by design codes. In this paper, artificial bee colony algorithm (ABC) is utilized to optimize different skeletal structures. The results of the ABC are compared with the results of other optimization algorithms from the literature to show the efficiency of this technique for structural design problems.

[1]  A. Kaveh,et al.  A DISCRETE BIG BANG - BIG CRUNCH ALGORITHM FOR OPTIMAL DESIGN OF SKELETAL STRUCTURES , 2010 .

[2]  Siamak Talatahari,et al.  Ant Colony Optimization for Design of Space Trusses , 2008 .

[3]  Dervis Karaboga,et al.  Artificial bee colony algorithm , 2010, Scholarpedia.

[4]  Siamak Talatahari,et al.  Optimum design of skeletal structures using imperialist competitive algorithm , 2010 .

[5]  Jasbir S. Arora,et al.  Methods for Discrete Variable Structural Optimization , 2000 .

[6]  Barbara Webb,et al.  Swarm Intelligence: From Natural to Artificial Systems , 2002, Connect. Sci..

[7]  Mustafa Sonmez,et al.  Discrete optimum design of truss structures using artificial bee colony algorithm , 2011 .

[8]  Dervis Karaboga,et al.  Artificial Bee Colony (ABC) Optimization Algorithm for Solving Constrained Optimization Problems , 2007, IFSA.

[9]  Siamak Talatahari,et al.  An improved ant colony optimization for the design of planar steel frames , 2010 .

[10]  Mustafa Sonmez,et al.  Artificial Bee Colony algorithm for optimization of truss structures , 2011, Appl. Soft Comput..

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

[12]  Duc Truong Pham,et al.  OPTIMIZATION OF THE WEIGHTS OF MULTI-LAYERED PERCEPTIONS USING THE BEES ALGORITHM , 2006 .

[13]  Siamak Talatahari,et al.  Optimal design of skeletal structures via the charged system search algorithm , 2010 .

[14]  Dušan Teodorović,et al.  Bee Colony Optimization – a Cooperative Learning Approach to Complex Transportation Problems , 2005 .

[15]  Marco Dorigo,et al.  Distributed Optimization by Ant Colonies , 1992 .

[16]  Charles V. Camp.  DESIGN OF SPACE TRUSSES USING BIG BANG–BIG CRUNCH OPTIMIZATION , 2007 .

[17]  Dervis Karaboga,et al.  A survey: algorithms simulating bee swarm intelligence , 2009, Artificial Intelligence Review.

[18]  Xin-She Yang,et al.  Engineering Optimizations via Nature-Inspired Virtual Bee Algorithms , 2005, IWINAC.

[19]  Saeed Shojaee,et al.  Optimal design of skeletal structures using ant colony optimization , 2007 .

[20]  伊藤 稔.  Artificial Bee Colonyアルゴリズムの基礎的検討 , 2014 .

[21]  Shahram Pezeshk,et al.  Optimized Design of Two-Dimensional Structures Using a Genetic Algorithm , 1998 .

[22]  Dervis Karaboga,et al.  A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm , 2007, J. Glob. Optim..

[23]  Siamak Talatahari,et al.  Hybrid Algorithm of Harmony Search, Particle Swarm and Ant Colony for Structural Design Optimization , 2009 .

[24]  O. Hasançebi,et al.  Optimal design of planar and space structures with genetic algorithms , 2000 .

[25]  Dusan Teodorovic,et al.  Bee Colony Optimization (BCO) , 2009, Innovations in Swarm Intelligence.

[26]  Charles V. Camp,et al.  Design of Space Trusses Using Ant Colony Optimization , 2004 .