A new optimization algorithm, namely the forest algorithm (FA), is introduced for the first time. This algorithm simulates trees' growth, reproduction and death in a forest to perform optimization. In the algorith...
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ISBN:
(纸本)9781479942626
A new optimization algorithm, namely the forest algorithm (FA), is introduced for the first time. This algorithm simulates trees' growth, reproduction and death in a forest to perform optimization. In the algorithm, trees and branches represent a collection of trial solutions and parameters needed to be optimized respectively, and three mechanisms, i.e. growth, proliferation and death, are employed for improving trees' vitality, which is a factor defined to evaluate the fitness of trial solutions. This algorithm in general execute a global optimization by operating on a group of trial solutions in parallel, but its growth mechanism, which adopts a parameter sweeping method, is a local optimization, so it combines the ability to find global optima of the global optimization and the fast convergence of the local optimization. Several numerical experiments are conducted, in which the performance of the FA in terms of the global optimization capability, accuracy and efficiency is evaluated and compared to that of some widely-used global optimization algorithms such as the Genetic algorithm (GA) and the Particle Swarm Optimization (PSO). Results shown the FA is able to perform global optimization effectively and with high accuracy.
The material removal rate (MRR) is an essential indicator for regulating process parameters and evaluating machining effects in the polishing process. However, as is known to all, it is difficult to accurately describ...
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The material removal rate (MRR) is an essential indicator for regulating process parameters and evaluating machining effects in the polishing process. However, as is known to all, it is difficult to accurately describe the MRR in theoretical approach due to the fact that it is affected by multi-parameter coupling such as pressure, velocity, abrasives, and the complex machining environment makes it challenging to monitor the MRR. This paper proposed a novel model based on the combination of the genetic algorithm and deep belief network (DBN) in order to predict MRR in the polishing process. First, the random forest algorithm was applied to select the parameter variables having a significant influence on the MRR, and these parameter variables were arranged as the input variables of the DBN model. Second, the hyperparameters of network were optimized by using the genetic algorithm with more powerful global search ability. Finally, the network model was trained and tested with the aid of the dataset provided by the PHM2016 Data Challenge, which resulted in an root mean square error value of 2.5487 and an R2 value of 0.9937 for the complete dataset. Meanwhile, the prediction error was reduced by more than 2.2% compared with predictive models available in the literature. The results show that the model investigated in this study more accurately predicts the MRR in the polishing process.
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