چکیده مقاله
Optimal design of truss structures is an active branch of research in optimization Three main classes of truss optimization include size, geometry and topology Extensive research in a range of different types of optimizing methods have been done Nowadays many of optimization algorithms are inspired by natural phenomena such as genetic algorithm, particle swarm and ants colonies These, so called metaheuristic algorithms, produce random initial solutions and improve their efficiency during the process of optimizing, and search for global optimum In order to overcome the disadvantages of genetic algorithm high computational cost of the slow convergence rate in solving engineering optimization problems and particle swarm algorithm falling into local optimum and premature convergence , these two algorithms are combined to reach better solutions and increased stability In hybrid algorithms, the main advantages of using the particle swarm optimization include directing the agents toward the global best obtained by the swarm and the local best obtained by the agent itself so that the genetic algorithm is improved in performance In this paper, size and topology of trusses are optimized using hybrid genetic particle swarm HGAPSO algorithms To optimize truss weight, complex design variables, cross section of members and node connectivity, are selected as discrete design variables, so that desired constraints such as stress and displacement restrictions and buckling of members are satisfied Finally, some design examples are tested using the new method compared to other heuristic algorithms to demonstrate the effectiveness of the present work
کلیدواژهها
نویسندگان
m Askarian
Msc student of Civil Engineering, Shiraz University
M.R Maheri
Professor of Civil Engineering, Shiraz University
S. Shojaee
m Askarian Msc student of Civil Engineering, Shiraz UniversityM.R Maheri Professor of Civil Engineering, Shiraz University
شیوه ارجاع
Askarian, m and Maheri, M.R and Shojaee, S.,1391,Size and topology optimization of trusses using hybrid genetic-particle swarm algorithms,9th International Congress on Civil Engineering,Isfahan
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