Journal ArticleDOI
Sizing and layout design of truss structures under dynamic and static constraints with an integrated particle swarm optimization algorithm
Ali Mortazavi,Vedat Toan +1 more
- Vol. 51, pp 239-252
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TLDR
This study tests the performance of an integrated particle swarm optimization algorithm (iPSO), a new particle swarm optimizer integrated with the improved fly-back mechanism and the weighted particle concept, in four weight minimization of truss structures with sizing and layout variables under multiple frequency constraints.Abstract:
Display Omitted An integrated particle swarm optimization algorithm for weight minimization of truss structures with sizing and layout variables is developed;.Optimization problems with frequency constraints and static constraints are solved;.The new algorithm is competitive with other state-of-the-art metaheuristic algorithms. Natural frequencies offer useful knowledge on the dynamic response of the structures. It is possible to avoid from the destructive effects of dynamic loads on the structures by optimizing layout and size of their subject to constraints on natural frequencies. Since optimization problems including frequency constraints are highly nonlinear, this kind of problems forms a challenging area to test the performance of the different optimization techniques. This study tests the performance of an integrated particle swarm optimization algorithm (iPSO), a new particle swarm optimizer integrated with the improved fly-back mechanism and the weighted particle concept, in four weight minimization of truss structures with sizing and layout variables under multiple frequency constraints. Optimization results demonstrate that the new algorithm is competitive with other state-of-the-art metaheuristic algorithms in dynamic and static structural optimization problems.read more
Citations
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Sizing, layout and topology design optimization of truss structures using the Jaya algorithm
TL;DR: The results demonstrate that JA can obtain better designs than those of the other state-of-the-art metaheuristic and gradient-based optimization methods in terms of optimized weight, standard deviation and number of structural analyses.
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Interactive search algorithm: A new hybrid metaheuristic optimization algorithm
TL;DR: The achieved numerical results demonstrate that the proposed method is competitive with other well-established metaheuristic methods.
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Modified symbiotic organisms search for structural optimization
TL;DR: This paper proposes a modification of a nature inspired Symbiotic Organisms Search (SOS) algorithm to enhance its efficacy of accuracy in search together with exploration by introducing an adaptive benefit factor and modified parasitism vector and results show that MSOS is more reliable and efficient as compared to the basis SOS algorithm and other state-of theart algorithms.
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Hybrid Grey Wolf Optimizer Using Elite Opposition-Based Learning Strategy and Simplex Method
TL;DR: This paper introduces the elite opposition-based learning strategy and simplex method into GWO, and proposes a hybrid grey optimizer using elite opposition (EOGWO), which shows a better optimization performance and robustness.
Journal ArticleDOI
Inverse problem based differential evolution for efficient structural health monitoring of trusses
Sujin Bureerat,Nantiwat Pholdee +1 more
TL;DR: DE search performance for structural damage detection can be considerably improved by integrating RBF into its procedure, and the new algorithm is the best for all test problems.
References
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Proceedings ArticleDOI
Particle swarm optimization
TL;DR: A concept for the optimization of nonlinear functions using particle swarm methodology is introduced, and the evolution of several paradigms is outlined, and an implementation of one of the paradigm is discussed.
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An Introduction to the Finite Element Method
TL;DR: Second-order Differential Equations in One Dimension: Finite Element Models (FEM) as discussed by the authors is a generalization of the second-order differential equation in two dimensions.
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Theoretical and numerical constraint-handling techniques used with evolutionary algorithms: a survey of the state of the art
TL;DR: A comprehensive survey of the most popular constraint-handling techniques currently used with evolutionary algorithms, including approaches that go from simple variations of a penalty function, to others, more sophisticated, that are biologically inspired on emulations of the immune system, culture or ant colonies.
Solving Constrained Nonlinear Optimization Problems with Particle Swarm Optimization
Xiaohui Hu,Russell C. Eberhart +1 more
TL;DR: Particle Swarm Optimization is an efficient and general solution to solve most nonlinear optimization problems with nonlinear inequality constraints with preserving feasibility strategy employed to deal with constraints.
Journal ArticleDOI
An improved particle swarm optimizer for mechanical design optimization problems
TL;DR: An improved particle swarm optimizer (PSO) for solving mechanical design optimization problems involving problem-specific constraints and mixed variables such as integer, discrete and continuous variables is presented.