MEALPY: An open-source library for latest meta-heuristic algorithms in Python

Thieu, Nguyen Van; Mirjalili, S. Ali

doi:10.1016/j.sysarc.2023.102871

Cited by 94 publications

(9 citation statements)

References 120 publications

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“…All 20 metaheuristic algorithms are used to optimize the weights for the ELM network. Note that the implemented codes for all metaheuristic algorithms are available at 34 .…”

Section: Methodsmentioning

confidence: 99%

“…To address these problems, in this paper, we introduce three state-of-the-art mathematical-inspired metaheuristic algorithms for training the ELM network: Pareto-like sequential sampling (PSS), weighted mean of vectors (INFO), and the Runge–Kutta optimizer (RUN). The rationale for adopting these three recommended algorithms derives from the theoretical idea of the No-Free Lunch theorem 34 , which states that no single algorithm outperforms all other algorithms on all problems. Thus, proposing three algorithms is expected to yield superior effectiveness and performance capabilities.…”

Section: Introductionmentioning

confidence: 99%

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Integrated metaheuristic algorithms with extreme learning machine models for river streamflow prediction

Van Thieu,

Nguyen,

Sherif

et al. 2024

Sci Rep

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Accurate river streamflow prediction is pivotal for effective resource planning and flood risk management. Traditional river streamflow forecasting models encounter challenges such as nonlinearity, stochastic behavior, and convergence reliability. To overcome these, we introduce novel hybrid models that combine extreme learning machines (ELM) with cutting-edge mathematical inspired metaheuristic optimization algorithms, including Pareto-like sequential sampling (PSS), weighted mean of vectors (INFO), and the Runge–Kutta optimizer (RUN). Our comparative assessment includes 20 hybrid models across eight metaheuristic categories, using streamflow data from the Aswan High Dam on the Nile River. Our findings highlight the superior performance of mathematically based models, which demonstrate enhanced predictive accuracy, robust convergence, and sustained stability. Specifically, the PSS-ELM model achieves superior performance with a root mean square error of 2.0667, a Pearson’s correlation index (R) of 0.9374, and a Nash–Sutcliffe efficiency (NSE) of 0.8642. Additionally, INFO-ELM and RUN-ELM models exhibit robust convergence with mean absolute percentage errors of 15.21% and 15.28% respectively, a mean absolute errors of 1.2145 and 1.2105, and high Kling-Gupta efficiencies values of 0.9113 and 0.9124, respectively. These findings suggest that the adoption of our proposed models significantly enhances water management strategies and reduces any risks.

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“…All 20 metaheuristic algorithms are used to optimize the weights for the ELM network. Note that the implemented codes for all metaheuristic algorithms are available at 34 .…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Integrated metaheuristic algorithms with extreme learning machine models for river streamflow prediction

Van Thieu,

Nguyen,

Sherif

et al. 2024

Sci Rep

View full text Add to dashboard Cite

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“…During the experiment, all metaheuristic optimization algorithms to be compared were programmed and implemented using the open source Python library MEALPY 2.4.1 [36] to ensure the consistent execution of different optimization strategies and effective comparison of the results produced.…”

Section: Model Performance Evaluationmentioning

confidence: 99%

“…SCA-BP [36] 0 The stability of different models was comparatively analyzed in an all-round way using box plots showing the results of 17 models trained on the test set for 10 times (Figure 8). The results show that, in terms of MAE, the SAEO-BP neural network has the highest stability, followed by the IAEO-BP and VCS-BP neural networks; in terms of the other three performance indicators, the SAEO-BP has a high stability, second only to the IAEO-BP neural network.…”

Section: Comparative Analysis Of the Performance Of The Saeo-bp Neura...mentioning

confidence: 99%

A Chlorophyll-a Concentration Inversion Model Based on Backpropagation Neural Network Optimized by an Improved Metaheuristic Algorithm

Wang,

Cui,

2024

Remote Sensing

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Chlorophyll-a (Chl-a) concentration monitoring is very important for managing water resources and ensuring the stability of marine ecosystems. Due to their high operating efficiency and high prediction accuracy, backpropagation (BP) neural networks are widely used in Chl-a concentration inversion. However, BP neural networks tend to become stuck in local optima, and their prediction accuracy fluctuates significantly, thus posing restrictions to their accuracy and stability in the inversion process. Studies have found that metaheuristic optimization algorithms can significantly improve these shortcomings by optimizing the initial parameters (weights and biases) of BP neural networks. In this paper, the adaptive nonlinear weight coefficient, the path search strategy “Levy flight” and the dynamic crossover mechanism are introduced to optimize the three main steps of the Artificial Ecosystem Optimization (AEO) algorithm to overcome the algorithm’s limitation in solving complex problems, improve its global search capability, and thereby improve its performance in optimizing BP neural networks. Relying on Google Earth Engine and Google Colaboratory (Colab), a model for the inversion of Chl-a concentration in the coastal waters of Hong Kong was built to verify the performance of the improved AEO algorithm in optimizing BP neural networks, and the improved AEO algorithm proposed herein was compared with 17 different metaheuristic optimization algorithms. The results show that the Chl-a concentration inversion model based on a BP neural network optimized using the improved AEO algorithm is significantly superior to other models in terms of prediction accuracy and stability, and the results obtained via the model through inversion with respect to Chl-a concentration in the coastal waters of Hong Kong during heavy precipitation events and red tides are highly consistent with the measured values of Chl-a concentration in both time and space domains. These conclusions can provide a new method for Chl-a concentration monitoring and water quality management for coastal waters.

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“…Additionally [6], selected 20 benchmark functions from CEC2014 and CEC2015 to assess the Improved Sea Lion Optimization algorithm against other techniques. Moreover [7], proposed a specialized library for metaheuristic algorithms and utilized functions from CEC2017 to evaluate the performance of these algorithms. Notably, recent metaheuristic algorithms, such as the Q-learning based Vegetation Evolution algorithm [8], have incorporated Opfunu library functions for testing on the CEC2020 function set, as well as engineering problems and WSN coverage optimization problems.…”

Section: Introductionmentioning

confidence: 99%

Opfunu: An Open-source Python Library for Optimization Benchmark Functions

Van Thieu

2024

Journal of Open Research Software

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Opfunu is a Python library designed to address the need for a comprehensive suite of benchmark functions for numerical optimization algorithms. It offers a rich collection of functions, including all those used in the Congress on Evolutionary Computation (CEC) competition between 2005 and 2022, alongside over 200 traditional functions with varying complexities and dimensions. Opfunu is built on top of Numpy for ease of use, fast computation, and adheres to a modular structure. The library is freely available on GitHub, promoting open-source development and encouraging the reuse of these benchmark functions by researchers and students working in the field of optimization algorithms.

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MEALPY: An open-source library for latest meta-heuristic algorithms in Python

Cited by 94 publications

References 120 publications

Integrated metaheuristic algorithms with extreme learning machine models for river streamflow prediction

Integrated metaheuristic algorithms with extreme learning machine models for river streamflow prediction

A Chlorophyll-a Concentration Inversion Model Based on Backpropagation Neural Network Optimized by an Improved Metaheuristic Algorithm

Opfunu: An Open-source Python Library for Optimization Benchmark Functions

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