A fuzzy discrete harmony search algorithm applied to annual cost reduction in radial distribution systems

Ameli, Kazem; Alfi, Alireza; Aghaebrahimi, M. R.

doi:10.1080/0305215x.2015.1120299

Cited by 33 publications

(7 citation statements)

References 54 publications

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“…However PSO is mostly used for real-value parameter optimization. PSO belongs to the family of natureinspired metaheuristic algorithms, which includes genetic algorithms, evolution strategies, harmony search, or ant colony optimization, among others, and they have shown a good performance in engineering and mathematical optimization problems (Arqub and Abo-Hammour , 2014;Mousavi and Alfi , 2015;Arab and Alfi , 2015;Ameli et al , 2016;Pahnehkolaei et al , 2017).…”

Section: Single and Multi-objective Particle Swarm Optimizationmentioning

confidence: 99%

Multi-objective evolutionary optimization of prediction intervals for solar energy forecasting with neural networks

Galván

Valls

Cervantes

et al. 2017

Information Sciences

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In the context of forecasting for renewable energy, it is common to produce point forecasts but it is also important to have information about the uncertainty of the forecast. To this extent, instead of providing a single measure for the prediction, lower and upper bound for the expected value for the solar radiation are used (prediction interval). This estimation of optimal prediction intervals requires simultaneous optimization of two objective measures: on one hand, it is important that the coverage probability of the interval is as close as possible to a given target value. On the other, in order to bound uncertainty, intervals must be narrow; this means that there is a trade-off between both objectives, as narrow intervals reduce the coverage probability for those solutions, as the actual value of solar radiation is more likely to fall outside the predicted margins. In this work we propose a multi-objective evolutionary approach that is able to optimize both goals simultaneously. The proposal uses neural networks to create complex non-linear models whose outputs are the upper and lower limits of the prediction intervals. Results have been compared with a single-objective optimization of similar neural network architectures and a baseline algorithm (quantile estimation with gradient boosting). Results show that the neural network is able to provide accurate results. Also, the multi-objective approach is able to obtain the best results and has also the advantage that a single run is able to obtain prediction intervals for any target coverage probability.

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Section: Single and Multi-objective Particle Swarm Optimizationmentioning

confidence: 99%

Multi-objective evolutionary optimization of prediction intervals for solar energy forecasting with neural networks

Galván

Valls

Cervantes

et al. 2017

Information Sciences

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“…PNLBSS methods can rapidly converge based on standard gradient descent methods; however, satisfactory results are sometimes difficult to be obtained due to the existence of many local minima in complex nonlinear spaces. Over the last decades, evolutionary algorithms (EAs) have gained considerable attention to obtain a global optimal solution, such as particle swarm optimization (PSO) (Yang et al., 2016), memetic algorithm (Mousavi and Alfi, 2015), genetic algorithm (Deb and Srivastava, 2012), harmony search algorithm (Ameli et al., 2016), water cycle algorithm (Pahnehkolaei et al., 2017), and search group algorithm (Noorbin and Alfi, 2018), which have been successfully applied to solve optimization problems in practical applications. In this study, PSO (Pornsing, 2014) is used to update the parameters in PNLBSS, and crossover and mutation are used in PSO to avoid particle premature convergence.…”

Section: Introductionmentioning

confidence: 99%

Post-nonlinear blind source separation with kurtosis constraints using augmented Lagrangian particle swarm optimization and its application to mechanical systems

Cheng

Chu

et al. 2019

Journal of Vibration and Control

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To accurately estimate source signals from their post-nonlinear mixtures, a post-nonlinear blind source separation (PNLBSS) method with kurtosis constraints is proposed based on augmented Lagrangian particle swarm optimization (PSO). First, an improved contrast function is presented by combining mutual information of the separated signals and kurtosis ranges of source signals. Second, an augmented Lagrangian multiplier method is used to convert PNLBSS into an unconstrained pseudo-objective optimization problem. Then, improved PSO is applied to update the parameters in complex nonlinear spaces. Finally, numerical case studies and experimental case studies are provided to evaluate the performance of the proposed method. By adding the kurtosis ranges constraints, the estimation accuracy of source signals could be improved, which would benefit vibration and acoustic monitoring and control.

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“…Zong and Kim proposed harmony search algorithm (HS) [ 29 ], which simulates musicians relying on their own memory to adjust the tone of each musical instrument in the band to achieve a wonderful state of harmony. Ameli and Alfi proposed a discrete harmony search algorithm (DHS), and applied it in the optimization of the capacitor position in the distribution system, and achieved a good result [ 30 ]. In these heuristic search algorithms, the PSO algorithm is relatively simple and easy to implement, and therefore is widely used in various optimization problems.…”

Section: Introductionmentioning

confidence: 99%

A Method of Biomedical Information Classification based on Particle Swarm Optimization with Inertia Weight and Mutation

Zhang

Chen

et al. 2018

Open Life Sciences

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With the rapid development of information technology and biomedical engineering, people can get more and more information. At the same time, they begin to study how to apply the advanced technology in biomedical information. The main research of this paper is to optimize the machine learning method by particle swarm optimization (PSO) and apply it in the classification of biomedical data. In order to improve the performance of the classification model, we compared the different inertia weight strategies and mutation strategies and their combinations with PSO, and obtained the best inertia weight strategy without mutation, the best mutation strategy without inertia weight and the best combination of the two. Then, we used the three PSO algorithms to optimize the parameters of support vector machine in the classification of biomedical data. We found that the PSO algorithm with the combination of inertia weight and mutation strategy and the inertia weight strategy that we proposed could improve the classification accuracy. This study has an important reference value for the prediction of clinical diseases.

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A fuzzy discrete harmony search algorithm applied to annual cost reduction in radial distribution systems

Cited by 33 publications

References 54 publications

Multi-objective evolutionary optimization of prediction intervals for solar energy forecasting with neural networks

Multi-objective evolutionary optimization of prediction intervals for solar energy forecasting with neural networks

Post-nonlinear blind source separation with kurtosis constraints using augmented Lagrangian particle swarm optimization and its application to mechanical systems

A Method of Biomedical Information Classification based on Particle Swarm Optimization with Inertia Weight and Mutation

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