Improved Cat Swarm Optimization Algorithm for Assembly Sequence Planning

Guo, Jianwen; Sun, Zhuo; Tang, Hong Mei; Li, Yin; Zhang, Zhicong

doi:10.2174/1874444301507010792

Cited by 12 publications

(8 citation statements)

References 14 publications

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“…One of the nature-inspired meta-heuristic optimization algorithms is Cat Swarm Optimization (CSO), which proposed in 2007 by Chu and Tsai [50] and improved in 2015 by Guo et al [51] .The CSO inspired by the cats' behavior. For this purpose, two modes, including seeking mode and tracing mode are proposed.…”

Section: B Cat Swarm Optimization Algorithmmentioning

confidence: 99%

Derivation of Optimized Equations for Estimation of Dispersion Coefficient in Natural Streams Using Hybridized ANN With PSO and CSO Algorithms

et al. 2020

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In this paper, a new hybrid model is developed to improve the accuracy in the prediction of the longitudinal dispersion coefficient (Kx) and the derivation of novel optimized explicit equations for natural streams. For this purpose, an artificial neural network (ANN) is hybridized with particle swarm optimization (PSO) and cat swarm optimization (CSO) algorithms. The CSO and PSO are used to find the optimum values of biases and weights in ANN structure and formulate the results as novel explicit predictive equations than the classical black-box methods. The hydraulic parameters of the natural stream and some geometric parameters were utilized for the model developments. Eight different input combinations are used as the input vectors to ANN, ANN-PSO, and ANN-CSO models, whereas the dispersion coefficient (Kx) is the target model output. The developed models are trained and tested bya comprehensive reference data sets measured on streams in the United States, that were used previously by Tayfur and Singh (2005) in ANN models. The main aims, novelty, and contributions of the present study are 1) improving the accuracy of classical ANN-based Kx predictions by hybridizing with CSO and PSO. 2) Performing sensitive analysis of ANN, ANN-CSO, and ANN-PSO based on input combinations 3) derivation of novel explicit optimized ANN-CSO, ANN-PSO, equations for predicting Kx rather than the classical ANN black-box methods. The results depicted that the highest accuracy and superiority were attained by the ANN-PSO model, with input variables of B, H, U, U*, followed by ANN-CSO and ANN. By using the optimized trained black box ANN models, two novel explicit predictive equations are derived, and their results are compared with the empirical equations. Comparative assessments confirmed significant improvements in the hybrid equations' results than the classical ANN and previously published equations. The developed novel equations can be used to estimate the Kx in one-dimensional pollutant transfer models that are essential for the pollution studies in environmental river engineering practices.

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Section: B Cat Swarm Optimization Algorithmmentioning

confidence: 99%

Derivation of Optimized Equations for Estimation of Dispersion Coefficient in Natural Streams Using Hybridized ANN With PSO and CSO Algorithms

et al. 2020

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“…In order to search for all feasible assembly sequence schemes and find the optimal assembly sequence, the complexity of searching for the optimal sequence will increase toward the direction of exhaustive search, and it is difficult to obtain a relatively optimal assembly sequence in a short time; this challenge has become one of the important driving forces to encourage the research of computerized assembly sequence planning [12]. In order to solve the ASP (Assembly Sequence Planning) problem, researchers used a variety of optimization algorithms to optimize the ASP problem, such as Ant colony optimization algorithm (ACO) [13], genetic algorithm (GA) [14,15], immune algorithm (IA) [16], neural networks (NN) [17], scatter search algorithm (SSA) [18], and other heuristic methods [19][20][21]. At present, researchers have made remarkable progress in solving ASP optimization problems, but there are still some problems that need to be solved urgently.…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Research on Optimizing Selection and Optimizing Matching Technologies of Aeroengine Fan Rotor Blades

Chen

Gao

et al. 2021

Shock and Vibration

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Aiming at the problem of low resources utilization of rotating blades in the selection process of aeroengine fan rotor blades, this paper takes the first-order bending dispersion, first-order torque dispersion, and gravitational moment difference of rotor blades as the selection criteria and takes the minimum remaining blades as the optimization goal. An intelligent selection algorithm of blades based on the collocation degree of blades is proposed and achieves the efficient selection and full utilization of rotating blades. Aiming at the problem of multiple installations and multiple adjustments and low assembly success rate of fan rotor blades, this paper takes the gravity moment difference of the two blades at the diagonal position of 180° as the constraint and takes the minimum residual unbalance as the optimization objective, adopts the improved simulated annealing algorithm to optimize the assembly sequence of rotating blades, and greatly reduces the residual unbalance of blades, which is beneficial to reduce the number of assembly adjustments of blades. The optimizing selection and optimizing matching methods of rotating blades realize the full utilization and efficient assembly of blades and lays a foundation for the reliability and robustness of the assembly quality and service performance of blades.

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“…Studies on ASP have implemented different heuristics optimization algorithms such as genetic algorithm, simulated annealing, evolutionary algorithm, ant colony optimization algorithm, and immune and other heuristic methods [10][11][12][13][14][15][16][17].…”

Section: Related Workmentioning

confidence: 99%

Assembly Sequence Planning Using Artificial Neural Networks for Mechanical Parts Based on Selected Criteria

Suszyński

Peta

2021

Applied Sciences

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The proposed model of the neural network describes the task of planning the assembly sequence on the basis of predicting the optimal assembly time of mechanical parts. In the proposed neural approach, the k-means clustering algorithm is used. In order to find the most effective network, 10,000 network models were made using various training methods, including the steepest descent method, the conjugate gradients method, and Broyden–Fletcher–Goldfarb–Shanno algorithm. Changes to network parameters also included the following activation functions: linear, logistic, tanh, exponential, and sine. The simulation results suggest that the neural predictor would be used as a predictor for the assembly sequence planning system. This paper discusses a new modeling scheme known as artificial neural networks, taking into account selected criteria for the evaluation of assembly sequences based on data that can be automatically downloaded from CAx systems.

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Improved Cat Swarm Optimization Algorithm for Assembly Sequence Planning

Cited by 12 publications

References 14 publications

Derivation of Optimized Equations for Estimation of Dispersion Coefficient in Natural Streams Using Hybridized ANN With PSO and CSO Algorithms

Derivation of Optimized Equations for Estimation of Dispersion Coefficient in Natural Streams Using Hybridized ANN With PSO and CSO Algorithms

[Retracted] Research on Optimizing Selection and Optimizing Matching Technologies of Aeroengine Fan Rotor Blades

Assembly Sequence Planning Using Artificial Neural Networks for Mechanical Parts Based on Selected Criteria

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