Improved Genetic Algorithm with Two-Level Approximation Method for Laminate Stacking Sequence Optimization by Considering Engineering Requirements

Laminated composites have been widely applied in aerospace structures; thus optimization of the corresponding stacking sequences is indispensable. Genetic algorithms have been popularly adopted to cope with the design of stacking sequences which is a combinatorial optimization problem with complicated manufacturing constraints, but they often exhibit high computational costs with many structural analyses. A genetic algorithm using a two-level approximation (GATLA) method was proposed previously by the authors to obtain the optimal stacking sequences, which requires significantly low computational costs. By considering practical engineering requirements, this method possesses low applicability in complicated structures with multiple laminates. What is more, it has relatively high dependence on some genetic algorithm control parameters. To address these problems, now we propose an improved GA with two-level approximation (IGATLA) method which includes improved random initial design, adaptive penalty fitness function, adaptive crossover probability, and variable mutation probability, as well as enhanced validity check criterion for multiple laminates. The efficiency and feasibility of these improvements are verified with numerical applications, including typical numerical examples and industrial applications. It is shown that this method is also able to handle large, real world, industrial analysis models with high efficiency

Structural optimization with an automatic mode identification method for tracking global vibration mode

<p>This article presents a mode identification method for structural optimization with global mode constraints to overcome the mode switching problem. In engineering design, the natural frequencies of global vibrations for a complex structure, the orders of which would not be constant in optimization loops, are usually very difficult to constrain. In this case, an incorrect constraint may lead to an unreliable design. A mode identification technique based on modal effective mass fraction is implemented to track the global modes such that the constraints will be updated subsequently and the optimizer can run correctly. A study case with comparison to traditional modal assurance criterion approaches demonstrates the advantages of this technique. An optimization framework has been developed with the new proposed mathematical model. Two numerical optimization examples, of a space truss and a simplified satellite structure, are presented to demonstrate the feasibility and applicability of this process.</p

Oncological risk of proximal gastrectomy for proximal advanced gastric cancer after neoadjuvant chemotherapy

Abstract Purpose This study assesses the metastasis rate of the key distal lymph nodes (KDLN) that are not routinely dissected in proximal gastrectomy, aiming to explore the oncological safety of proximal gastrectomy for upper gastric cancer who underwent neoadjuvant chemotherapy. Methods We analyzed a cohort of 150 patients with proximal locally advanced gastric cancer (cT3/4 before chemotherapy) from two high-volume cancer centers in China who received preoperative neoadjuvant chemotherapy (NAC) and total gastrectomy with lymph node dissection. Metastasis rate of the KDLN (No.5/6/12a) and the risk factors were analyzed. Results Key distal lymph node metastasis was detected in 10% (15/150) of patients, with a metastasis rate of 6% (9/150) in No. 5 lymph nodes, 6.7% (10/150) in No. 6 lymph nodes, and 2.7% (2/75) in No. 12a lymph nodes. The therapeutic value index of KDLN as one entity is 5.8. Tumor length showed no correlation with KDLN metastasis, while tumor regression grade (TRG) emerged as an independent risk factor (OR: 1.47; p-value: 0.04). Of those with TRG3 (no response to NAC), 80% (12/15) was found with KDLN metastasis. Conclusion For cT3/4 proximal locally advanced gastric cancer patients, the risk of KDLN metastasis remains notably high even after NAC. Therefore, proximal gastrectomy is not recommended; instead, total gastrectomy with thorough distal lymphadenectomy is the preferred surgical approach

Local Modal Frequency Improvement with Optimal Stiffener by Constraints Transformation Method

Local modal vibration could adversely affect the dynamical environment, which should be considered in the structural design. For the mode switching phenomena, the traditional structural optimization method for problems with specific order of modal frequency constraints could not be directly applied to solve problems with local frequency constraints. In the present work, a novel approximation technique without mode tracking is proposed. According to the structural character, three reasonable assumptions, unchanged mass matrix, accordant modal shape, and reversible stiffness matrix, have been used to transform the optimization problem with local frequency constraints into a problem with nodal displacement constraints in the local area. The static load case is created with the modal shape equilibrium forces, then the displacement constrained optimization is relatively easily solved to obtain the optimal design, which satisfies the local frequency constraints as well. A numerical example is used to verify the feasibility of the proposed approximation method. Then, the method is further applied in a satellite structure optimization problem

Large-Scale Truss Topology and Sizing Optimization by an Improved Genetic Algorithm with Multipoint Approximation

Truss size and topology optimization problems have recently been solved mainly by many different metaheuristic methods, and these methods usually require a large number of structural analyses due to their mechanism of population evolution. A branched multipoint approximation technique has been introduced to decrease the number of structural analyses by establishing approximate functions instead of the structural analyses in Genetic Algorithm (GA) when GA addresses continuous size variables and discrete topology variables. For large-scale trusses with a large number of design variables, an enormous change in topology variables in the GA causes a loss of approximation accuracy and then makes optimization convergence difficult. In this paper, a technique named the label–clip–splice method is proposed to improve the above hybrid method in regard to the above problem. It reduces the current search domain of GA gradually by clipping and splicing the labeled variables from chromosomes and optimizes the mixed-variables model efficiently with an approximation technique for large-scale trusses. Structural analysis of the proposed method is extremely reduced compared with these single metaheuristic methods. Numerical examples are presented to verify the efficacy and advantages of the proposed technique

Large-Scale Truss Topology and Sizing Optimization by an Improved Genetic Algorithm with Multipoint Approximation

Truss size and topology optimization problems have recently been solved mainly by many different metaheuristic methods, and these methods usually require a large number of structural analyses due to their mechanism of population evolution. A branched multipoint approximation technique has been introduced to decrease the number of structural analyses by establishing approximate functions instead of the structural analyses in Genetic Algorithm (GA) when GA addresses continuous size variables and discrete topology variables. For large-scale trusses with a large number of design variables, an enormous change in topology variables in the GA causes a loss of approximation accuracy and then makes optimization convergence difficult. In this paper, a technique named the label&ndash;clip&ndash;splice method is proposed to improve the above hybrid method in regard to the above problem. It reduces the current search domain of GA gradually by clipping and splicing the labeled variables from chromosomes and optimizes the mixed-variables model efficiently with an approximation technique for large-scale trusses. Structural analysis of the proposed method is extremely reduced compared with these single metaheuristic methods. Numerical examples are presented to verify the efficacy and advantages of the proposed technique