Optimization of laminate stacking sequence for maximum buckling load using the ant colony optimization (ACO) metaheuristic

Aymerich, Francesco; Serra, Mauro

doi:10.1016/j.compositesa.2007.10.011

Cited by 79 publications

(48 citation statements)

References 17 publications

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“…The success of the metaheuristic optimization algorithm depends on the balance between the algorithm's exploration (diversification) and exploitation (intensification) capability [44,[60][61][62][63]. This study proposes variable changing methods for the CDI and MDI of SBX and PM, respectively.…”

Section: Variable Exploration and Exploitation Balancing Approachesmentioning

confidence: 99%

Multiobjective Automatic Parameter Calibration of a Hydrological Model

Jung

Choi

Kim

2017

Water

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This study proposes variable balancing approaches for the exploration (diversification) and exploitation (intensification) of the non-dominated sorting genetic algorithm-II (NSGA-II) with simulated binary crossover (SBX) and polynomial mutation (PM) in the multiobjective automatic parameter calibration of a lumped hydrological model, the HYMOD model. Two objectives-minimizing the percent bias and minimizing three peak flow differences-are considered in the calibration of the six parameters of the model. The proposed balancing approaches, which migrate the focus between exploration and exploitation over generations by varying the crossover and mutation distribution indices of SBX and PM, respectively, are compared with traditional static balancing approaches (the two dices value is fixed during optimization) in a benchmark hydrological calibration problem for the Leaf River (1950 km 2 ) near Collins, Mississippi. Three performance metrics-solution quality, spacing, and convergence-are used to quantify and compare the quality of the Pareto solutions obtained by the two different balancing approaches. The variable balancing approaches that migrate the focus of exploration and exploitation differently for SBX and PM outperformed other methods.

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Section: Variable Exploration and Exploitation Balancing Approachesmentioning

confidence: 99%

Multiobjective Automatic Parameter Calibration of a Hydrological Model

Jung

Choi

Kim

2017

Water

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“…Karakaya and Soykasap 13 maximized the critical buckling load factor for different design cases in order to design the optimum composite plates with conventional fiber angles. Stacking sequence optimization of laminated composites for maximization of buckling load factor has also been considered with other stochastic optimization methods in the literature such as generalized pattern search algorithm, 13 simulated annealing (SA), 14 Tabu search, 16 scatter search, 17 ant colony optimization (ACO) 18,19 algorithms. In addition to standard meta-heuristic algorithms, in the literature, buckling load factor maximization problems are also considered with various hybrid algorithms (HAs) to find the optimum stacking sequence designs.…”

Section: Introductionmentioning

confidence: 99%

Buckling optimization of composite laminates using a hybrid algorithm under Puck failure criterion constraint

Deveci

Aydın

Artem

2016

Journal of Reinforced Plastics and Composites

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In this study, an optimization procedure is proposed to find the optimum stacking sequence designs of laminated composite plates in different fiber angle domains for maximum buckling resistance. A hybrid algorithm combining genetic algorithm and trust region reflective algorithm is used in the optimization to obtain higher performance and improve the quality of solutions. As a novelty, Puck fiber and inter-fiber failure criteria are directly implemented to the optimization problems as nonlinear function constraints, which have allowed more consistent and feasible results. The performance of the hybrid algorithm is demonstrated by comparing with the individual performances of genetic and trust region reflective algorithms via test problems from the literature. Also, a study is performed to exhibit the effectiveness of the selected failure criterion as constraint among the other common criteria. The proposed procedure is used to solve many problems including various design considerations. The results indicate that reliable stacking sequence designs can be achieved in specific configurations even for the composite plates subjected to superior buckling loads when Puck physically based (3D) failure theory is considered as a first ply failure constraint in the buckling optimization.

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“…(1). Aymerich and Serra [3] explain that a candidate solution is then constructed step-by-step by choosing probabilistically the orientations of the stacks of the laminate. This tour construction is repeated applying the two sub-rules from Eq.…”

Section: Ant Colony System (Acs) Applied To Laminated Compositesmentioning

confidence: 99%

“…ACO was inspired by the observation of the behavior of real ants (Dorigo and Socha [4]). Regarding the ant colony optimization applied to laminated composites, Aymerich and Serra [3] presented the maximization of buckling load using ACO; in Abachizadeh and Tahani [1] a multi-objective optimal design for maximization of fundamental frequency and minimization of cost is studied and Wang et al [16,17] proposed a modified ant colony algorithm for stacking sequence optimization of rectangular laminate and composite stiffened panels for improving buckling strength.…”

Section: Introductionmentioning

confidence: 99%

An ant colony algorithm applied to lay-up optimization of laminated composite plates

Koide

Franã§A

Luersen

2013

Lat. Am. j. solids struct.

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Ant colony optimization (ACO) is a class of heuristic algorithms proposed to solve optimization problems. The idea was inspired by the behavior of real ants, related to their ability to find the shortest path between the nest and the food source. ACO has been applied successfully to different kinds of problems. So, this manuscript describes the development and application of an ACO algorithm to find the optimal stacking sequence of laminated composite plates. The developed ACO algorithm was evaluated on four examples for symmetric and balanced lay-up. The results of the first three cases, in which the classical lamination theory was used to obtain the structural response of rectangular plates, were compared to those obtained from the literature using genetic algorithms (GA) and other ACO algorithm. The fourth example investigates the maximization of fundamental frequencies of rectangular plates with central holes, where the structural response was obtained by finite element analysis, showing that this optimization technique may be successfully applied to a broad class of stacking sequence problems for laminated composites.

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Optimization of laminate stacking sequence for maximum buckling load using the ant colony optimization (ACO) metaheuristic

Cited by 79 publications

References 17 publications

Multiobjective Automatic Parameter Calibration of a Hydrological Model

Multiobjective Automatic Parameter Calibration of a Hydrological Model

Buckling optimization of composite laminates using a hybrid algorithm under Puck failure criterion constraint

An ant colony algorithm applied to lay-up optimization of laminated composite plates

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