Design of truss-structures for minimum weight using genetic algorithms
Finite Elements in Analysis and Design
A novel optimization approach for minimum cost design of trusses
Computers and Structures
An improved genetic algorithm with initial population strategy and self-adaptive member grouping
Computers and Structures
A heuristic particle swarm optimization method for truss structures with discrete variables
Computers and Structures
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This paper presents two randomized line search techniques, each combined with a genetic algorithm (GA), to improve the convergence and the accuracy ratio for discrete sizing optimization of truss structures. The first technique is a simple one-dimensional line search in which design variable axes are selected randomly as search directions. The second is a line search technique whose search direction is determined randomly by fitness function values and differences in the genotypes of individuals. To apply the above-mentioned line search techniques without difficulty, real coding is adopted for discrete problems. The line search techniques are applied to discrete optimization problems of minimum-weight truss structures subjected to stress and displacement constraints. The proposed techniques provide convergence to better solutions than a conventional GA.