Nonpenalty Machine Learning Constraint Handling Using PSO‐SVM for Structural Optimization

Rosso, Marco Martino; Cucuzza, Raffaele; Trapani, Fabio Di; Marano, Giuseppe Carlo

doi:10.1155/2021/6617750

Cited by 28 publications

(16 citation statements)

References 47 publications

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“…Although the PSO algorithm already possesses two kinds of memories (cognitive and social), most of the information about the swarm visited positions is discarded, and a better exploitation of the past particles positions remains to be fully determined. In another recent work [18], a first promising step in that direction has been already made. In [18], the PSO has been hybridized with a machine learning algorithm, the support vector machine (SVM).…”

Section: Discussionmentioning

confidence: 99%

“…In a different recent contribution of the authors [18], some further novel approaches to deal with constraints have been presented, considering a hybridization of the PSO with a machine learning support vector machine. However, the current paper presents a completely different approach based on handling constraints directly based on information which can be retrieved from the swarm positions in terms of objective function and constraints violations.…”

Section: Introductionmentioning

confidence: 99%

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Enhanced Multi-Strategy Particle Swarm Optimization for Constrained Problems with an Evolutionary-Strategies-Based Unfeasible Local Search Operator

et al. 2022

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Nowadays, optimization problems are solved through meta-heuristic algorithms based on stochastic search approaches borrowed from mimicking natural phenomena. Notwithstanding their successful capability to handle complex problems, the No-Free Lunch Theorem by Wolpert and Macready (1997) states that there is no ideal algorithm to deal with any kind of problem. This issue arises because of the nature of these algorithms that are not properly mathematics-based, and the convergence is not ensured. In the present study, a variant of the well-known swarm-based algorithm, the Particle Swarm Optimization (PSO), is developed to solve constrained problems with a different approach to the classical penalty function technique. State-of-art improvements and suggestions are also adopted in the current implementation (inertia weight, neighbourhood). Furthermore, a new local search operator has been implemented to help localize the feasible region in challenging optimization problems. This operator is based on hybridization with another milestone meta-heuristic algorithm, the Evolutionary Strategy (ES). The self-adaptive variant has been adopted because of its advantage of not requiring any other arbitrary parameter to be tuned. This approach automatically determines the parameters’ values that govern the Evolutionary Strategy simultaneously during the optimization process. This enhanced multi-strategy PSO is eventually tested on some benchmark constrained numerical problems from the literature. The obtained results are compared in terms of the optimal solutions with two other PSO implementations, which rely on a classic penalty function approach as a constraint-handling method.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Enhanced Multi-Strategy Particle Swarm Optimization for Constrained Problems with an Evolutionary-Strategies-Based Unfeasible Local Search Operator

et al. 2022

Self Cite

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show abstract

“…The design indexes of reflector are affected by many independent dimensional parameters, which include the height of the reflector (x 1 ), the spacing distance of the reinforcement (x 2 ), the thickness of the reinforcement (x 3 ), the wall thickness of the outer ring of the reflector (x 4 ) and the minimum mirror thickness of the reflector (x 5 ). The functional relationship between the input random variables and the output response is shown in Equation ( 7)- (9).…”

Section: Preliminary Work For Reflectormentioning

confidence: 99%

“…Li used topology optimization and integration analysis for the lightweight design and mounting of a 760 mm diameter SiC reflector [6]. In addition to the above-mentioned topology optimization methods, intelligent algorithms and surrogate models have been applied to the optimal design of mechanical structures in recent years [7][8][9][10][11][12]. Kihm et al used genetic algorithm to optimize the dimensional parameters of the mirror [13].…”

Section: Introductionmentioning

confidence: 99%

Bayesian Regularization Algorithm Based Recurrent Neural Network Method and NSGA-II for the Optimal Design of the Reflector

Zhang

Sun

2022

Machines

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The optical-mechanical system of a space camera is composed of several complex components, and the effects of several factors (weight, gravity, modal frequency, temperature, etc.) on its system performance need to be considered during ground tests, launch, and in-orbit operation. In order to meet the system specifications of the optical camera system, the dimensional parameters of the optical camera structure need to be optimized. There is a highly nonlinear functional relationship between the dimensional parameters of the optical machine structure and the design indexes. The traditional method takes a significant amount of time for finite element calculation and is less efficient. In order to improve the optimization efficiency, a recurrent neural network prediction model based on the Bayesian regularization algorithm is proposed in this paper, and the NSGA-II is used to globally optimize multiple prediction objectives of the prediction model. The reflector of the space camera is used as an example to predict the weight, first-order modal frequency, and gravitational mirror deformation root mean square of the reflector, and to complete the lightweight design. The results show that the prediction model established by BR-RNN-NSGA-II offers high prediction accuracy for the design indexes of the reflector, which all reach over 99.6%, and BR-RNN-NSGA-II can complete the multi-objective optimization search efficiently and accurately. This paper provides a new idea of optimization of optical machine structure, which enriches the theory of complex structure design.

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“…e results showed that the proposed approach was sufficiently general and robust to handle structural configuration having significantly different structural deficiencies. Rosso et al [14] proposed a new nonpenalty-based constraint handling approach for particle swarm optimization, adopting a supervised classification machine learning method, the support vector machine. e results showed that the new approach represented a valid alternative to solve constrained optimization problems even in structural optimization field.…”

Section: Introductionmentioning

confidence: 99%

Static Topology Optimization and Dynamic Characteristics Analysis of 6000T Compression‐Shear Test Machine Frame

Wu¹,

Cui

et al. 2021

Advances in Civil Engineering

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This work aims to achieve the 6000t compression-shear test machine frame design with the lightweight. The force condition of the compression-shear test machine frame under limited working conditions is first analyzed, and the static analysis of the compression-shear test machine frame is performed using ABAQUS. Then, taking the volume of the frame of the compression-shear testing machine as the constraint condition, the topology optimization of the compression-shear testing machine frame is performed using the variable density method of topology optimization, and the model is reconstructed accordingly. Finally, not only the static characteristics of the frame before and after optimization but also the modal characteristics of the frame before and after optimization and the dynamic characteristics after sudden unloading are compared and analyzed. The results show that the weight of the frame decreases by 14.5% after optimization, and the maximum static stress of the frame is still less than the yield strength of the material; the maximum displacement is still less than the allowable maximum displacement, which meets the requirements of static strength and stiffness. The natural frequency of each mode is much greater than the working frequency, which meets the requirements of dynamic stiffness. After sudden unloading, the maximum dynamic stress of beams, columns, and base of the frame are less than the yield strength of materials, which meets the requirements of dynamic strength.

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Nonpenalty Machine Learning Constraint Handling Using PSO‐SVM for Structural Optimization

Cited by 28 publications

References 47 publications

Enhanced Multi-Strategy Particle Swarm Optimization for Constrained Problems with an Evolutionary-Strategies-Based Unfeasible Local Search Operator

Enhanced Multi-Strategy Particle Swarm Optimization for Constrained Problems with an Evolutionary-Strategies-Based Unfeasible Local Search Operator

Bayesian Regularization Algorithm Based Recurrent Neural Network Method and NSGA-II for the Optimal Design of the Reflector

Static Topology Optimization and Dynamic Characteristics Analysis of 6000T Compression‐Shear Test Machine Frame

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