Nonlinear inertia weight in particle swarm optimization

Borowska, Bożena

doi:10.1109/stc-csit.2017.8098790

Cited by 11 publications

(5 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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“…The PSO algorithm is an evolutionary algorithm proposed by Kennedy and Eberhart that imitates bird foraging behavior through particles. It relies on the memory of the bird's own position and the information exchange mechanism within the swarm, so that the bird can quickly locate the target position [44][45][46]. The PSO algorithm can greatly reduce the possibility of falling into the local optimal solution, and has the characteristics of easy implementation and fast convergence.…”

Section: Neural Network Prediction Model Based On Particle Swarm Opti...mentioning

confidence: 99%

Prediction Model of Greenhouse Tomato Yield Using Data Based on Different Soil Fertility Conditions

et al. 2023

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Tomato yield prediction plays an important role in agricultural production planning and management, market supply and demand balance, and agricultural risk management. To solve the problems of low accuracy and high uncertainty of tomato yield prediction methods in solar greenhouses, based on experimental data for water and fertilizer consumption by greenhouse tomatoes in different regions over many years, this paper investigated the prediction models of greenhouse tomato yields under three different soil fertility conditions (low, medium, and high). Under these three different soil fertility conditions, greenhouse tomato yields were predicted using the neural network prediction model (NN), the neural network prediction model based on particle swarm optimization (PSO–NN), the neural network prediction model based on an adaptive inertia weight particle swarm optimization algorithm (AIWPSO–NN), and the neural network prediction model based on the improved particle swarm optimization algorithm (IPSO–NN). The experimental results demonstrate that the evaluation indexes (mean square error, mean absolute error, and R2) of the IPSO–NN prediction model proposed in this paper were superior to the other three prediction models (i.e., NN prediction model, AIWPSO–NN prediction model, and IPSO–NN prediction model) under three different soil fertility conditions. Among them, compared with the NN prediction model, the MSE of the other three prediction models under high soil fertility decreased to 0.0082, 0.0041, and 0.0036; MAE decreased to 0.0759, 0.0511, and 0.0489; R2 decreased to 0.8641, 0.9323, and 0.9408. These results indicated that the IPSO–NN prediction model had a higher predictive ability for greenhouse tomato yields under three different soil fertility conditions. In view of the important role of tomato yield prediction in greenhouses, this technology may be beneficial to agricultural management and decision support.

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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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Nonlinear inertia weight in particle swarm optimization

Cited by 11 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

Prediction Model of Greenhouse Tomato Yield Using Data Based on Different Soil Fertility Conditions

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

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