Multiple global optima location using differential evolution, clustering, and local search

Dominico, Gabriel

doi:10.1016/j.asoc.2021.107448

Cited by 13 publications

(7 citation statements)

References 36 publications

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“…It highlights the effectiveness of the LSTM+PSO model in making predictions on the dataset. The LSTM+PSO can be advantageous in terms of exploration, exploitation [34], [35], stochastic search, optimal capability, and the ability to handle global and local optima [36], [37]. PSO is known for its ability to explore the search space effectively.…”

Section: Discussionmentioning

confidence: 99%

Drier Bed Adsorption Predictive Model with Enhancement of Long Short-Term Memory and Particle Swarm Optimization

Yusoff,

Sallehud-din,

Tahir

et al. 2023

IJACSA

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The drier bed adsorption processes remove moisture from gases and liquids by ensuring product quality, extending equipment lifespan, and enhancing safety in various applications. The longevity of adsorption beds is quantified by net loading capacity values that directly impact the effectiveness of the moisture removal process. Predictive modeling has emerged as a valuable tool to enhance drier bed adsorption systems. Despite the increasing significance of predictive modeling in enhancing the efficiency of drier bed adsorption processes, the existing methodologies frequently exhibit deficiencies in accuracy and flexibility, which are crucial for optimizing process performance. This research investigates the effectiveness of a hybrid approach combining Long Short-Term Memory and Particle Swarm Optimization (LSTM+PSO) as a proposed method to predict the net loading capacity of a drier bed. The train-test split ratios and rolling origin technique are explored to assess model performance. The findings reveal that LSTM+PSO with a 70:30 train-test split ratio outperform other methods with the lowest error. Bed 1 exhibits an RMSE of 1.31 and an MSE of 0.91, while Bed 2 archives RMSE and MSE values of 0.81 and 0.72, respectively and Bed 3 with an RMSE of 0.19 and an MSE of 0.13, followed by Bed 4 with an RMSE of 0.67 and an MSE of 0.36. Bed 5 exhibits an RMSE of 0.42 and an MSE of 0.34. Furthermore, this research compares LSTM+PSO with LSTM and conventional predictive methods: Support Vector Regression, Seasonal Autoregressive Integrated Moving Average with Exogenous Variables, and Random Forest.

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Section: Discussionmentioning

confidence: 99%

Drier Bed Adsorption Predictive Model with Enhancement of Long Short-Term Memory and Particle Swarm Optimization

Yusoff,

Sallehud-din,

Tahir

et al. 2023

IJACSA

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“…These methods were the winners of this competition in 2013, 2015, 2016, 2018, and 2019, respectively. • MMO methods that have been recently published in top journals and reported their results on this test suite, including ANDE [36], LBPADE [20], DIDE [21], FBK-DE [34], MMDE [40], TS-ABC [52], and NCjDE-2LS ar [38]. The results of these methods have been directly excerpted from the corresponding publications.…”

Section: Comparison With Other Mmo Methodsmentioning

confidence: 99%

“…For TS-ABC [52], only the results for f = 10 −4 have been reported in the corresponding publication; therefore, for this method, the reported PR represents the averaged PR over three function tolerances. For NCjDE-2LS ar [38], PR has been reported for f = 10 −3 and f = 10 −5 only; therefore, for this method, the calculated PR is the average of the reported PR for these two tolerances.…”

Section: Comparison With Other Mmo Methodsmentioning

confidence: 99%

“…Automatic niching differential evolution (ANDE) [36] combines affinity propagation clustering [37] for niching with a contour prediction approach and a two-level local search to estimate the approximate location of the optimum. NCjDE-2LS ar [38] combines jDE with density-based spatial clustering of applications with noise (DBSCAN) [38], two local search mechanisms and an external archive. A few studies have employed other clustering methods, such as Kmeans clustering [39], and clustering solutions based on the similarity of their values [40].…”

Section: A Related Studiesmentioning

confidence: 99%

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Static and Dynamic Multimodal Optimization by Improved Covariance Matrix Self-Adaptation Evolution Strategy With Repelling Subpopulations

Ahrari

Elsayed

Sarker

et al. 2022

IEEE Trans. Evol. Computat.

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The covariance matrix self-adaptation evolution strategy with repelling subpopulations (RS-CMSA-ES) is one of the most successful multimodal optimization methods currently available. However, some of its components may become inefficient in certain situations. This study introduces the second variant of this method, called RS-CMSA-ESII. It improves the adaptation schemes for the normalized taboo distances of the archived solutions and the covariance matrix of the subpopulation, the termination criteria for the subpopulations, and the way in which the infeasible solutions are treated. It also improves the time complexity of RS-CMSA-ES by updating the initialization procedure of a subpopulation and developing a more accurate metric for determining critical taboo regions. The effects of these modifications are illustrated by designing controlled numerical simulations. RS-CMSA-ESII is then compared with the most successful and recent niching methods for multimodal optimization on a widely adopted test suite. The results obtained reveal the superiority of RS-CMSA-ESII over these methods, including the winners of the competition on niching methods for multimodal optimization in previous years. Besides, this study extends RS-CMSA-ESII to dynamic multimodal optimization and compares it with a few recently proposed methods on the modified moving peak benchmark functions.

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“…According to different design inspirations, metaheuristic optimization algorithms are divided into single solution-based metaheuristic optimization algorithms and population-based metaheuristic optimization algorithms [ 11 ]. Since only one solution of the single solution-based metaheuristic optimization algorithm participates in the optimization process, the search for the whole solution space is not thorough enough, which result in the algorithm easily falling into a locally optimal solution [ 12 ]. The population-based metaheuristic algorithm involves a population in the optimization process.…”

Section: Introductionmentioning

confidence: 99%

An improved African vultures optimization algorithm based on tent chaotic mapping and time-varying mechanism

Fan

Wang

2021

PLoS ONE

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Metaheuristic optimization algorithms are one of the most effective methods for solving complex engineering problems. However, the performance of a metaheuristic algorithm is related to its exploration ability and exploitation ability. Therefore, to further improve the African vultures optimization algorithm (AVOA), a new metaheuristic algorithm, an improved African vultures optimization algorithm based on tent chaotic mapping and time-varying mechanism (TAVOA), is proposed. First, a tent chaotic map is introduced for population initialization. Second, the individual’s historical optimal position is recorded and applied to individual location updating. Third, a time-varying mechanism is designed to balance the exploration ability and exploitation ability. To verify the effectiveness and efficiency of TAVOA, TAVOA is tested on 23 basic benchmark functions, 28 CEC 2013 benchmark functions and 3 common real-world engineering design problems, and compared with AVOA and 5 other state-of-the-art metaheuristic optimization algorithms. According to the results of the Wilcoxon rank-sum test with 5%, among the 23 basic benchmark functions, the performance of TAVOA has significantly better than that of AVOA on 13 functions. Among the 28 CEC 2013 benchmark functions, the performance of TAVOA on 9 functions is significantly better than AVOA, and on 17 functions is similar to AVOA. Besides, compared with the six metaheuristic optimization algorithms, TAVOA also shows good performance in real-world engineering design problems.

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Multiple global optima location using differential evolution, clustering, and local search

Cited by 13 publications

References 36 publications

Drier Bed Adsorption Predictive Model with Enhancement of Long Short-Term Memory and Particle Swarm Optimization

Drier Bed Adsorption Predictive Model with Enhancement of Long Short-Term Memory and Particle Swarm Optimization

Static and Dynamic Multimodal Optimization by Improved Covariance Matrix Self-Adaptation Evolution Strategy With Repelling Subpopulations

An improved African vultures optimization algorithm based on tent chaotic mapping and time-varying mechanism

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