A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Optimum Design of Structures with Quick Group Search Optimization Algorithm
Abstract Based on the basic principles of an optimization algorithm, group search optimization (GSO) algorithm, two improved GSO, named quick group search optimizer (QGSO) and quick group search optimizer with passive congregation (QGSOPC), are presented in this chapter to deal with structural optimization design tasks. The improvement of QGSO has three main aspects: first, increase the number of ‘ranger’ when the target stops going forward. Second, use the search strategy of particle swarm optimizer (PSO) by considering the best group member and the best personal member. Employ the step search strategy to replace the visual search strategy. Third, reproduce the ‘ranger’ with hybrid of the group best member and the personal best member. the QGSOPC is a hybrid QGSO with passive congregation. The QGSO is tested by planar and space truss structures with continuous variables and discrete variables. The QGSOPC is only tested by discrete variables. The calculation results of QGSO and QGSOPC are compared with that of the GSO and HPSO. The results show that the QGSO and QGSOPC algorithms can handle the constraint problems with discrete variables efficiently, and the QGSOPC has more efficient search ability, faster convergent rate and less iterative times to find out the optimum solution.
Optimum Design of Structures with Quick Group Search Optimization Algorithm
Abstract Based on the basic principles of an optimization algorithm, group search optimization (GSO) algorithm, two improved GSO, named quick group search optimizer (QGSO) and quick group search optimizer with passive congregation (QGSOPC), are presented in this chapter to deal with structural optimization design tasks. The improvement of QGSO has three main aspects: first, increase the number of ‘ranger’ when the target stops going forward. Second, use the search strategy of particle swarm optimizer (PSO) by considering the best group member and the best personal member. Employ the step search strategy to replace the visual search strategy. Third, reproduce the ‘ranger’ with hybrid of the group best member and the personal best member. the QGSOPC is a hybrid QGSO with passive congregation. The QGSO is tested by planar and space truss structures with continuous variables and discrete variables. The QGSOPC is only tested by discrete variables. The calculation results of QGSO and QGSOPC are compared with that of the GSO and HPSO. The results show that the QGSO and QGSOPC algorithms can handle the constraint problems with discrete variables efficiently, and the QGSOPC has more efficient search ability, faster convergent rate and less iterative times to find out the optimum solution.
Optimum Design of Structures with Quick Group Search Optimization Algorithm
Li, Lijuan (author) / Liu, Feng (author)
2011-01-01
46 pages
Article/Chapter (Book)
Electronic Resource
English
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