Mine blast algorithm for optimization of truss structures with discrete variables

The charged system search (CSS) algorithm is utilized for design of frame structures. The algorithm is inspired by the laws in physics. The CSS utilizes a number of charged particles which influence each other based on their fitness values and their separation distances considering the governing law of Coulomb. A comparison between the characteristics of the CSS algorithm and other well-known meta-heuristics is performed to indicate their similarities and differences. Some benchmark frame examples are optimized with the CSS algorithm. Comparison of the results of CSS with some other meta-heuristic algorithms shows the robustness of the new algorithm.

Charged system search for optimal design of frame structures

Water cycle, mine blast and improved mine blast algorithms for discrete sizing optimization of truss structures

This study is concerned with encoding types such as value encoding and binary encoding for continuous and discrete optimization with a GA, which is coded in FORTRAN and considers stress and displacement constraints, in view of weight minimization of truss structures. Moreover, when continuous optimization is performed, the challenge of huge search space due to the effort of considering continuous set of design variables is overcame by a mechanism introduced as restricted range approach (RRA) in this study. In comparison with the literature, it is concluded that the program developed in this study can be effectively used in the weight minimization of truss structures. It is also came to the conclusion that value encoding overcomes the adverse effects of Hamming-cliff, and that value encoding requires less computer memory and time, never destroys the fit chromosome.

Weight minimization of trusses with genetic algorithm

Structural optimization with frequency constraints is highly nonlinear dynamic optimization problems. Genetic algorithm (GA) has greater advantage in global optimization for nonlinear problem than optimality criteria and mathematical programming methods, but it needs more computational time and numerous eigenvalue reanalysis. To speed up the design process, an adaptive eigenvalue reanalysis method for GA-based structural optimization is presented. This reanalysis technique is derived primarily on the Kirsch's combined approximations method, which is also highly accurate for case of repeated eigenvalues problem. The required number of basis vectors at every generation is adaptively determined and the rules for selecting initial number of basis vectors are given. Numerical examples of truss design are presented to validate the reanalysis-based frequency optimization. The results demonstrate that the adaptive eigenvalue reanalysis affects very slightly the accuracy of the optimal solutions and significantly reduces the computational time involved in the design process of large-scale structures.

María Belén Prendes Gero

Papers

Design optimization of 3D steel structures: Genetic algorithms vs. classical techniques

A modified elitist genetic algorithm applied to the design optimization of complex steel structures