Charged system search and particle swarm optimization hybridized for optimal design of engineering structures

In this paper, a new Hybrid Charged System Search and Particle Swarm Optimization, HCSSPSO, is presented. Although Particle Swarm Optimization (PSO) has many advantages, including directional search, it has also some disadvantages resulting in slow convergence rate and low performance. On the other hand, the Charged System Search (CSS) is a robust optimization algorithm which has been successfully utilized in many structural optimization problems. In this study, the goal is to incorporate the positive features of the PSO in CSS and make it more capable of solving optimization problems. The hybrid CSS and PSO is named HCSSPRO, and it uses the positive features of the PSO to further improve the CSS. In order to show the higher performance of the HCSSPSO, it is implemented and applied to some engineering problems. These structures are benchmark examples which are optimized by many other methods and are suitable for comparison. Results of the present algorithm show its better performance and higher convergence rate for the problem studied.

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

[2]  S. Wu,et al.  Steady-state genetic algorithms for discrete optimization of trusses , 1995 .

[3]  A. Kaveh,et al.  Charged system search for optimum grillage system design using the LRFD-AISC code , 2010 .

[4]  Kalyanmoy Deb,et al.  GeneAS: A Robust Optimal Design Technique for Mechanical Component Design , 1997 .

[5]  E. Sandgren,et al.  Nonlinear Integer and Discrete Programming in Mechanical Design Optimization , 1990 .

[6]  Carlos A. Coello Coello,et al.  An empirical study about the usefulness of evolution strategies to solve constrained optimization problems , 2008, Int. J. Gen. Syst..

[7]  Ali Kaveh,et al.  ENGINEERING DESIGN OPTIMIZATION USING A HYBRID PSO AND HS ALGORITHM , 2013 .

[8]  A. Kaveh,et al.  An enhanced charged system search for configuration optimization using the concept of fields of forces , 2011 .

[9]  Ashok Dhondu Belegundu,et al.  A Study of Mathematical Programming Methods for Structural Optimization , 1985 .

[10]  P. Fourie,et al.  The particle swarm optimization algorithm in size and shape optimization , 2002 .

[11]  Siamak Talatahari,et al.  A CHARGED SYSTEM SEARCH WITH A FLY TO BOUNDARY METHOD FOR DISCRETE OPTIMUM DESIGN OF TRUSS STRUCTURES , 2010 .

[12]  Du Ming-zhu,et al.  An improved Templeman's algorithm for the optimum design of trusses with discrete member sizes , 1986 .

[13]  L. A. Schmit,et al.  Discrete-continuous variable structural synthesis using dual methods , 1980 .

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

[15]  S. N. Kramer,et al.  An Augmented Lagrange Multiplier Based Method for Mixed Integer Discrete Continuous Optimization and Its Applications to Mechanical Design , 1994 .

[16]  Siamak Talatahari,et al.  Geometry and topology optimization of geodesic domes using charged system search , 2011 .

[17]  Ali Kaveh,et al.  Cost optimization of a composite floor system, one-way waffle slab, and concrete slab formwork using a charged system search algorithm , 2012 .

[18]  A. Kaveh,et al.  A novel heuristic optimization method: charged system search , 2010 .

[19]  George I. N. Rozvany,et al.  DCOC: An optimality criteria method for large systems Part I: theory , 1992 .

[20]  A. Kaveh,et al.  Magnetic charged system search: a new meta-heuristic algorithm for optimization , 2012, Acta Mechanica.

[21]  Carlos A. Coello Coello,et al.  Constraint-handling in genetic algorithms through the use of dominance-based tournament selection , 2002, Adv. Eng. Informatics.

[22]  A. Kaveh,et al.  A novel hybrid charge system search and particle swarm optimization method for multi-objective optimization , 2011, Expert Syst. Appl..

[23]  R. Haftka,et al.  Elements of Structural Optimization , 1984 .

[24]  Russell C. Eberhart,et al.  A new optimizer using particle swarm theory , 1995, MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science.

[25]  Jasbir S. Arora,et al.  Introduction to Optimum Design , 1988 .

[26]  K. Lee,et al.  A new structural optimization method based on the harmony search algorithm , 2004 .

[27]  Siamak Talatahari,et al.  Particle swarm optimizer, ant colony strategy and harmony search scheme hybridized for optimization of truss structures , 2009 .

[28]  Carlos A. Coello Coello,et al.  Use of a self-adaptive penalty approach for engineering optimization problems , 2000 .

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

[30]  Leandro dos Santos Coelho,et al.  Gaussian quantum-behaved particle swarm optimization approaches for constrained engineering design problems , 2010, Expert Syst. Appl..

[31]  Ling Wang,et al.  An effective co-evolutionary particle swarm optimization for constrained engineering design problems , 2007, Eng. Appl. Artif. Intell..

[32]  Ali Kaveh,et al.  Simultaneous analysis, design and optimization of structures using the force method and supervised charged system search algorithm , 2013 .