Open AccessJournal Article
Fuzzy optimization of geometrical nonlinear space truss design
O Kelesoglu,M Ulker +1 more
TLDR
A general algorithm for nonlinear space truss system optimization with fuzzy constraints and fuzzy parameters for multiobjective fuzzy optimization techniques was formed with ANSYS parametric dimensional language.Abstract:
This paper presents a general algorithm for nonlinear space truss system optimization with fuzzy constraints and fuzzy parameters. The analysis of the space truss system is performed with the ANSYS program. The algorithm of multiobjective fuzzy optimization techniques was formed with ANSYS parametric dimensional language. In the formulation of the design problem, weight and minimum displacement are considered the objective functions. Three design examples are presented to demonstrate the application of the algorithm.read more
Citations
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A hybrid particle swarm and ant colony optimization for design of truss structures
Ali Kaveh,S Talatahar +1 more
TL;DR: The proposed algorithm is based on the particle swarm optimizer with passive congregation and ant colony optimization and tested on several benchmark trusses from literature to demonstrate the effectiveness of the presented method.
Journal ArticleDOI
Enhancing particle swarm optimization algorithm using two new strategies for optimizing design of truss structures
TL;DR: This work develops an augmented particle swarm optimization (AugPSO) algorithm using two new strategies,: boundary-shifting and particle-position-resetting, which is more robust than the PSO and PSOPC algorithms.
Journal ArticleDOI
A New Theoretical Approach for Robust Truss Optimization with Uncertain Load Directions
TL;DR: In this paper, the authors present a new theoretical model and a problem-specific metaheuristic approach when the only source of uncertainty is the variability of the applied load directions, which is independent from the theoretical description of the uncertainty which may be either probabilistic (stochastic) or possibilistic (fuzzy).
Journal ArticleDOI
Structural optimization under uncertainty in loading directions: Benchmark results
TL;DR: In this paper, the authors present benchmark results for structural optimization when the only source of uncertainty is the variability of the applied load directions, and the result of the optimization is a performance measure minimal design which is invariant to the investigated uncertainty type and satisfies the response constraints.
Journal ArticleDOI
Interval analysis based robust truss optimization with continuous and discrete variables using mix-coded genetic algorithm
TL;DR: In this paper, an interval analysis based robust optimization method combined with the improved genetic algorithm is proposed for solving the problem of optimizing truss structures in the presence of uncertain parameters considering both continuous and discrete design variables.
References
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Journal ArticleDOI
Fuzzy programming and linear programming with several objective functions
TL;DR: It is shown that solutions obtained by fuzzy linear programming are always efficient solutions and the consequences of using different ways of combining individual objective functions in order to determine an “optimal” compromise solution are shown.
Book ChapterDOI
Fuzzy Mathematical Programming
TL;DR: Fuzzy linear programming belongs to goal programming in the sense that implicitly or explicitly aspiration levels have to be defined at which the membership functions of the fuzzy sets reach their maximum or minimum.
Journal ArticleDOI
Multi‐objective optimization of fuzzy structural systems
TL;DR: A method of solving a fuzzy multi-objective structural optimization problem using ordinary single- objective programming techniques is presented.
Journal ArticleDOI
Nonlinear Membership Functions in Multiobjective Fuzzy Optimization of Mechanical and Structural Systems
TL;DR: An application of fuzzy mathematical programming techniques to multiple objective design problems is presented, and it is seen that optimum designs for both examples are strongly influenced by the sign of the membership satiation coefficient.
Journal ArticleDOI
Multiobjective fuzzy optimization techniques for engineering design
TL;DR: The results indicate that the α-cut approach provides the results in a parametric form while the λ-formulation yields an overall compromise solution to the design problem.