Simplified binary cat swarm optimization

Siqueira, Hugo Valadares; Santana, Clodomir J.; Macedo, Mariana; Figueiredo, Elliackin; Gokhale, A.P.; Bastos-Filho, Carmelo J. A.

doi:10.3233/ica-200618

Cited by 37 publications

(25 citation statements)

References 74 publications

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“…More recently, recurrent neural networks (RNN) and variants like LSTM networks, introduced by Hochreiter and Schmidhuber (1997), have received great attention in solving regression problems in various engineering fields (Chen et al, 2020;Hua et al, 2019;Divina et al, 2020;Torres et al, 2018;Zhang et al, 2019). Moreover, much attention has been paid to the optimization of such models, which are quite sensitive to the parameters setting (Charte et al, 2020;Siqueira et al, 2020;Thurnhofer-Hemsi et al, 2020). Ribeiro et al (2019) compared Seasonal Autoregressive Integrated Moving Average (SARIMA), Seasonal Autoregressive Integrated Moving Average Exogenous model (SARIMAX), and a hybrid SARIMAX-LSTM for forecasting the concrete dam's displacements with a report that the hybrid model is capable of providing a better forecast accuracy.…”

Section: Related Workmentioning

confidence: 99%

“…More recently, recurrent neural networks (RNN) and variants like LSTM networks, introduced by Hochreiter and Schmidhuber (1997), have received great attention in solving regression problems in various engineering fields (Chen et al., 2020; Hua et al., 2019; Ni et al., 2020; Ni et al., 2020; Divina et al., 2020; Torres et al., 2018; Torres, Troncoso, et al., 2019; Zhang et al., 2019). Moreover, much attention has been paid to the optimization of such models, which are quite sensitive to the parameters setting (Charte et al., 2020; Siqueira et al., 2020; Thurnhofer‐Hemsi et al., 2020). Ribeiro et al.…”

Section: Related Workmentioning

confidence: 99%

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Deformation forecasting of a hydropower dam by hybridizing a long short‐term memory deep learning network with the coronavirus optimization algorithm

Bui

Torres

Gutiérrez‐Avilés

et al. 2022

Computer aided Civil Eng

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The safety operation and management of hydropower dam play a critical role in social-economic development and ensure people's safety in many countries; therefore, modeling and forecasting the hydropower dam's deformations with high accuracy is crucial. This research aims to propose and validate a new model based on deep learning long short-term memory (LSTM) and the coronavirus optimization algorithm (CVOA), named CVOA-LSTM, for forecasting the deformations of the hydropower dam. The second-largest hydropower dam of Vietnam, located in the Hoa Binh province, is focused. Herein, we used the LSTM to establish the deformation model, whereas the CVOA was utilized to optimize the three parameters of the LSTM, the number of hidden layers, the learning rate, and the dropout. The efficacy of the proposed CVOA-LSTM model is assessed by comparing its forecasting performance with state-of-the-art benchmarks, sequential minimal optimization for support vector regression, Gaussian process, M5' model tree, multilayer perceptron neural network, reduced error pruning tree, random tree, random forest, and radial basis function neural network. The result shows that the proposed CVOA-LSTM model has high forecasting capability (R 2 = 0.874, root mean square error = 0.34, mean absolute error = 0.23) and outperforms the benchmarks. We conclude that CVOA-LSTM is a new tool that can be considered to forecast the hydropower dam's deformations.

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Section: Related Workmentioning

confidence: 99%

“…More recently, recurrent neural networks (RNN) and variants like LSTM networks, introduced by Hochreiter and Schmidhuber (1997), have received great attention in solving regression problems in various engineering fields (Chen et al., 2020; Hua et al., 2019; Ni et al., 2020; Ni et al., 2020; Divina et al., 2020; Torres et al., 2018; Torres, Troncoso, et al., 2019; Zhang et al., 2019). Moreover, much attention has been paid to the optimization of such models, which are quite sensitive to the parameters setting (Charte et al., 2020; Siqueira et al., 2020; Thurnhofer‐Hemsi et al., 2020). Ribeiro et al.…”

Section: Related Workmentioning

confidence: 99%

Deformation forecasting of a hydropower dam by hybridizing a long short‐term memory deep learning network with the coronavirus optimization algorithm

Bui

Torres

Gutiérrez‐Avilés

et al. 2022

Computer aided Civil Eng

View full text Add to dashboard Cite

show abstract

“…Inspired by the natural relationships of groups of animals, swarm-based algorithms, such as Particle Swarm Optimization (PSO) [4][5][6], the Artificial Bee Colony algorithm [7], Bacterial Foraging Optimization (BFO) [8], Cat Swarm Optimization (CSO) [9,10], and Ant Colony Optimization (ACO) [11], among others, provided sufficient evidence of efficiency and effectiveness in finding the optimal solutions to complex optimization problems [12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Swarm-Inspired Algorithms to Optimize a Nonlinear Gaussian Adaptive PID Controller

et al. 2021

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This work deals with metaheuristic optimization algorithms to derive the best parameters for the Gaussian Adaptive PID controller. This controller represents a multimodal problem, where several distinct solutions can achieve similar best performances, and metaheuristics optimization algorithms can behave differently during the optimization process. Finding the correct proportionality between the parameters is an arduous task that often does not have an algebraic solution. The Gaussian functions of each control action have three parameters, resulting in a total of nine parameters to be defined. In this work, we investigate three bio-inspired optimization methods dealing with this problem: Particle Swarm Optimization (PSO), the Artificial Bee Colony (ABC) algorithm, and the Whale Optimization Algorithm (WOA). The computational results considering the Buck converter with a resistive and a nonlinear load as a case study demonstrated that the methods were capable of solving the task. The results are presented and compared, and PSO achieved the best results.

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“…The resulting binary problems are very complex and demand efficient algorithms to solve them. Usually, these problems are solved via population-based metaheuristics, such as GAs (Xue et al , 2021), SS (Martí et al , 2006), MAs (Nguyen and Sudholt, 2020), ant colony optimization (Wang and Han, 2021), brain-storm optimization (Nucamendi-Guillén et al , 2018), artificial bee colony (Santana et al , 2019; Akay et al , 2021), cat swarm optimization (Siqueira et al , 2021), weighted superposition attraction algorithm (Baykasoğlu et al , 2018), etc. The improvement of current algorithms and the proposition of novel ones is an interesting and prolific research direction.…”

Section: Introductionmentioning

confidence: 99%

Solving binary programming problems using homotopy theory ideas

Alanís-López

Casas-Ramírez

Camacho‐Vallejo

2021

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PurposeThe aim of the study is to show that merging two areas of mathematics – topology and discrete optimization – could result in a viable option to solve classical or specialized integer problems.Design/methodology/approachIn the paper, discrete topology concepts are applied to propose a metaheuristic algorithm that is capable to solve binary programming problems. Particularly, some of the homotopy for paths principles are used to explore the solution space associated with four well-known NP-hard problems herein considered as follows: knapsack, set covering, bi-level single plant location with order and one-max.FindingsComputational experimentation confirms that the proposed algorithm performs in an effective manner, and it is able to efficiently solve the sets of instances used for the benchmark. Moreover, the performance of the proposed algorithm is compared with a standard genetic algorithm (GA), a scatter search (SS) method and a memetic algorithm (MA). Acceptable results are obtained for all four implemented metaheuristics, but the path homotopy algorithm stands out.Originality/valueA novel metaheuristic is proposed for the first time. It uses topology concepts to design an algorithmic framework to solve binary programming problems in an effective and efficient manner.

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Simplified binary cat swarm optimization

Cited by 37 publications

References 74 publications

Deformation forecasting of a hydropower dam by hybridizing a long short‐term memory deep learning network with the coronavirus optimization algorithm

Deformation forecasting of a hydropower dam by hybridizing a long short‐term memory deep learning network with the coronavirus optimization algorithm

Swarm-Inspired Algorithms to Optimize a Nonlinear Gaussian Adaptive PID Controller

Solving binary programming problems using homotopy theory ideas

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