Nonlinear inertia weight in particle swarm optimization

Borowska, Bożena

doi:10.1109/stc-csit.2017.8098790

Cited by 10 publications

(4 citation statements)

References 30 publications

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“…Three testing data sets with different parameter combinations were selected to test the generalization ability and robustness of the prediction model. We performed a simulation to compare the accuracy of the proposed method with that of other evolutionary algorithms [17]- [21]. The dataset of flank wear is publicly available at [27].…”

Section: B Flank Wear Prediction Resultsmentioning

confidence: 99%

“…The advantage of the BP algorithm is its fast convergence ability; however, BP might cause the system to fall into local optimal solutions. Hence, evolutionary algorithms, such as differential evolution (DE) [18]- [20], particle swarm optimization (PSO) [21], and artificial bee colony [22], have been proposed to search for global optimal solutions. DE algorithms have the advantages of a simple structure, low computational complexity, and low parameter setting requirements.…”

Section: Introductionmentioning

confidence: 99%

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Using an Interval Type-2 Fuzzy Neural Network and Tool Chips for Flank Wear Prediction

et al. 2020

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The precision of part machining is influenced by the tool life. Tools gradually wear out during the cutting process, which reduces the machining accuracy. Many studies have used machining parameters and sensor signals to predict flank wear; however, these methods have many limitations related to sensor installation, which is not only time-consuming and costly but also impractical in industry. This paper proposes an interval type-2 fuzzy neural network (IT2FNN) based on the dynamic-group cooperative differential evolution algorithm for flank wear prediction. Moreover, the Taguchi method is used to design cutting experiments for collecting experimental data and reducing the number of experiments. The CIE-xy color chromaticity values, spindle speed, feed per tooth, cutting depth, and cutting time are used as inputs of the IT2FNN, and the output is the flank wear value. The experimental results indicate that the proposed method can effectively predict flank wear with higher efficiency than other algorithms. INDEX TERMS Flank wear, chip surface, color calibration, interval type-2 fuzzy neural network, differential evolution

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Section: B Flank Wear Prediction Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Using an Interval Type-2 Fuzzy Neural Network and Tool Chips for Flank Wear Prediction

et al. 2020

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“…Nevertheless, many other authors use a variety of strategies to dynamically adjust IW, for instance, applying a linear decrease of IW from 0.9 to 0.4 during the first half of optimization, and restarting the cycle in the second half 36 . Other inertia adjustment PSO variants include nonlinear reduction, 37 selective multiple IW, 38 dynamic nonlinear changed IW, 39 nonlinear natural logarithm IW, 40 stability‐based adaptive IW, 41 fuzzy adaptive IW, 42 and many others.…”

Section: Related Workmentioning

confidence: 99%

Improved particle swarm optimizer for problems with variable evaluation time: Application to asymptotic homogenization

Argilaga

Guo

2022

Numerical Meth Engineering

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This work presents a new particle swarm optimizer (PSO)-based metaheuristic algorithm designed to reduce overall computational cost without compromising optimization's precision in functions with variable evaluation time. The algorithm exploits the evaluation time gradient in addition to the convergence gradient attempting to reach the same convergence precision following a more economical path. The particle's newly incorporated time information is usually in contradiction to the past memories of best function evaluations thus degrading convergence. A technique is proposed in order to modulate the new cognitive input that consists of progressive reducing of its weight in order to confer the algorithm the appropriate time-convergence balance. Results show that the proposed algorithm not only provides computational economy, but also unexpectedly improves convergence per se due to a better exploration in the initial stages of optimization. Its application in asymptotic homogenization of a cracked poroelastic medium confirms its superior performance compared to a series of alternative optimization algorithms. The proposed algorithm improvement allows to extend the applicability of PSO and PSO-based algorithms to problems that were previously thought to be too computationally expensive for population-based approaches.

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“…In order to match the network structure of the model with the characteristics of stock price data, we introduce the nonlinear dynamic inertia weight improved particle swarm optimization (NIWPSO) (Borowska 2017). According to the adjustment of ω, the algorithm can flexibly adjust the global optimization ability and local optimization ability, and improve the accuracy of LSTM parameter optimization.…”

Section: Parameter Optimization-niwpsomentioning

confidence: 99%

Portfolio Decision Model Based on NIWPSO-LSTM

Feng¹,

Chen²,

Hou³

2022

Proceedings of the 2022 3rd International Conference on E-Commerce and Internet Technology (ECIT 2022)

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As a cross product between computer science and financial science, the primary purpose of quantitative investment is to explain the formation principle of financial asset prices and to predict the future price of financial assets. With gold and bitcoin daily price data from London Bullion Market Association and NASDAQ, we develop a model that uses only the past stream of daily prices to date to determine each day if the trader should buy, hold, or sell their assets in their portfolio. Firstly, we choose the Long Short-Term Memory neural network based on the improved particle swarm algorithm (NIWPSO-LSTM) to predict the price. We use metabolic grey model (MGM(1,1)) to correct the price in the initial period, to make up for the shortcomings of LSTM which needs a large number of training sets to achieve better prediction results. Secondly, we quantify the return and risk of portfolio investment, establish a nonlinear programming model, and use Monte Carlo simulation method to solve the initial solution. Last, we use indicators to verify the accuracy of our model. All the results show that our model is able to provide extraordinary decision support in a real investment environment.

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Nonlinear inertia weight in particle swarm optimization

Cited by 10 publications

References 30 publications

Using an Interval Type-2 Fuzzy Neural Network and Tool Chips for Flank Wear Prediction

Using an Interval Type-2 Fuzzy Neural Network and Tool Chips for Flank Wear Prediction

Improved particle swarm optimizer for problems with variable evaluation time: Application to asymptotic homogenization

Portfolio Decision Model Based on NIWPSO-LSTM

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