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A reinforcement learning-based hybrid Aquila Optimizer and improved arithmetic optimization algorithm for global optimization

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EXPERT SYSTEMS WITH APPLICATIONS 2023年第1期224卷

作者： Liu, Haiyang Zhang, Xingong Zhang, Hanxiao Li, Chunyan Chen, Zhaohui Chongqing Univ Sci & Technol Sch Intelligent Technol & Engn Chongqing 401331 Peoples R China Chongqing Normal Univ Sch Math Sci Chongqing 401331 Peoples R China Chongqing Univ Sci & Technol Sch Math Phys & Data Sci Chongqing 401331 Peoples R China Chongqing Univ Sci & Technol Sch Math Phys & Data Sci 20 Daxuecheng East Rd Chongqing 401331 Peoples R China

This study constructs a reinforcement learning-based hybrid algorithm for Aquila Optimizer (AO) and improved arithmetic optimization algorithm (IAOA). The point of the hybrid algorithm is that Q-learning can dynamically select the AO and the IAOA at different stages for different problems. In arithmetic optimization algorithm (AOA), the mathematical optimization acceleration (MOA) function is restructured to balance global search and local exploitation, which can effectively stay away from the local optimum. Moreover, an improved reward function is modeled for Q-learning, which makes our hybrid algorithm more efficient and accurate. A set of benchmark functions and two engineering optimization problems are employed to test the performance of the proposed hybrid algorithm in this paper. Compared with other algorithms, the results show that the proposed hybrid algorithm has higher convergence speed and accuracy.

关键词： Reinforcement learning Aquila Optimizer arithmetic optimization algorithm Q -learning

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Metaheuristics exposed: Unmasking the design pitfalls of arithmetic optimization algorithm in benchmarking

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APPLIED SOFT COMPUTING 2024年 160卷

作者： Deng, Lingyun Liu, Sanyang Xidian Univ Sch Math & Stat Xian 710126 Peoples R China

This work unveils design flaws within most metaheuristics, with a specific focus on issues associated with the arithmetic optimization algorithm (AOA). Despite being a simple metaheuristic optimizer inspired by mathematical operations, AOA holds promise for addressing complex real-world applications. However, a thorough analysis of its search mechanism reveals a heavy dependence on problem bounds for the quality of solutions obtained by AOA. Additionally, discrepancies between algorithm descriptions and implementations in AOA can mislead users and impede progress within the metaheuristic community. Experimental simulations conducted on various standard benchmarks including their shifted versions indicate a structural bias in AOA, leading to artificially high accuracy in fitting standard test functions but poor performance when applied to shifted benchmarks. Finally, we give a critical cause analysis and conclude this article by highlighting valuable research avenues in this field.

关键词： arithmetic optimization algorithm Design flaw Metaheuristics Shifted benchmarks Structural bias

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An improved arithmetic optimization algorithm for training feedforward neural networks under dynamic environments

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KNOWLEDGE-BASED SYSTEMS 2023年第1期263卷

作者： Golcuk, Ilker Ozsoydan, Fehmi Burcin Durmaz, Esra Duygu Izmir Bakircay Univ Dept Ind Engn TR-35665 Izmir Turkiye Dokuz Eylul Univ Fac Engn Dept Ind Engn TR-35397 Izmir Turkiye Gazi Univ Fac Engn Dept Ind Engn TR-06570 Ankara Turkiye

This paper proposes an improved arithmetic optimization algorithm (AOA) to train artificial neural networks (ANNs) under dynamic environments. Despite many successful applications of metaheuristic training of ANNs, these studies assume static environments, which might not be realistic in real-world nonstationary processes. In this study, the training of ANNs is modeled as a dynamic optimization problem, and the proposed AOA is used to optimize connection weights and biases of the ANN under the presence of concept drift. The proposed method is designed to work for classification tasks. The performance of the proposed algorithm has been tested on twelve dynamic classification problems. Comparative analysis with state-of-the-art metaheuristic optimization algorithms has been provided. The superiority of the compared algorithms has been verified using nonparametric statistical tests. The results show that the improved AOA outperforms compared algorithms in training ANNs under dynamic environments. The findings demonstrate the potential of improved AOA for dynamic data-driven applications.(c) 2023 Elsevier B.V. All rights reserved.

关键词： arithmetic optimization algorithm Artificial neural networks Concept drift Dynamic optimization

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An opposition learning and spiral modelling based arithmetic optimization algorithm for global continuous optimization problems

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2022年第0期113卷

作者： Yang, Yang Gao, Yuchao Tan, Shuang Zhao, Shangrui Wu, Jinran Gao, Shangce Zhang, Tengfei Tian, Yu-Chu Wang, You-Gan Nanjing Univ Posts & Telecommun Coll Automat Nanjing 210023 Peoples R China Nanjing Univ Posts & Telecommun Coll Artificial Intelligence Nanjing 210023 Peoples R China Wuhan Univ Technol Sch Sci Wuhan 430070 Peoples R China Queensland Univ Technol Sch Math Sci Brisbane Qld 4001 Australia Univ Toyama Fac Engn Toyama 9308555 Japan Queensland Univ Technol Sch Comp Sci Brisbane Qld 4001 Australia

In engineering applications, many real-world optimization problems are nonlinear with multiple local optimums. Traditional algorithms that require gradients are not suitable for these problems. Meta-heuristic algorithms are popularly employed to deal with these problems because they can promisingly jump out of local optima and do not need any gradient information. The arithmetic optimization algorithm (AOA), a recently developed meta-heuristic algorithm, uses arithmetic operators (multiplication, division, subtraction, and addition) to solve optimization problems including nonlinear ones. However, the exploration and exploitation of AOA are not effective to handle some complex optimization problems. In this paper, an opposition learning and spiral modelling based AOA, namely OSAOA, is proposed for enhancing the optimization performance. It improves AOA from two perspectives. In the first perspective, the opposition-based learning (OBL) is committed to taking both candidate solutions and their opposite solutions into consideration for improving the global search with a high probability of jumping out of local minima. Then, the spiral modelling is introduced as the second perspective, which is particularly useful in getting the solutions gathering faster and accelerating the convergence speed in the later stage. In addition, OSAOA is compared with other existing advanced meta-heuristic algorithms based on 23 benchmark functions and four engineering problems: the three-bar truss design, the cantilever beam design, the pressure vessel design, and the tubular column design. From our simulations and engineering applications, the proposed OSAOA can provide better optimization results in dealing with these real-world optimization problems.

关键词： arithmetic optimization algorithm Opposition-based learning Spiral modelling Meta-heuristic Continuous optimization problem

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The arithmetic optimization algorithm

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COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING 2021年 376卷 113609-113609页

作者： Abualigah, Laith Diabat, Ali Mirjalili, Seyedali Elaziz, Mohamed Abd Gandomi, Amir H. Amman Arab Univ Fac Comp Sci & Informat Amman 11953 Jordan New York Univ Abu Dhabi Div Engn Abu Dhabi 129188 U Arab Emirates NYU Tandon Sch Engn Dept Civil & Urban Engn Brooklyn NY 11201 USA Torrens Univ Australia Ctr Artificial Intelligence Res & Optimisat Brisbane Qld Australia Yonsei Univ YFL Yonsei Frontier Lab Seoul South Korea Zagazig Univ Dept Math Fac Sci Zagazig Egypt Wuhan Univ Technol Sch Comp Sci & Technol Wuhan Peoples R China Univ Technol Sydney Fac Engn & Informat Technol Ultimo NSW 2007 Australia

This work proposes a new meta-heuristic method called arithmetic optimization algorithm (AOA) that utilizes the distribution behavior of the main arithmetic operators in mathematics including (Multiplication (M), Division (D), Subtraction (S), and Addition (A)). AOA is mathematically modeled and implemented to perform the optimization processes in a wide range of search spaces. The performance of AOA is checked on twenty-nine benchmark functions and several real-world engineering design problems to showcase its applicability. The analysis of performance, convergence behaviors, and the computational complexity of the proposed AOA have been evaluated by different scenarios. Experimental results show that the AOA provides very promising results in solving challenging optimization problems compared with eleven other well-known optimization algorithms. Source codes of AOA are publicly available at http://***/matlabcentral/fileexchange/8474 and https://***/projects. (C) 2020 The Author(s). Published by Elsevier B.V.

关键词： arithmetic optimization algorithm AOA algorithm optimization Meta-heuristics Heuristic Artificial Intelligence Grey Wolf Optimizer Whale optimization algorithm Genetic algorithm Particle Swarm optimization Benchmark Computational Intelligence

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optimization of VGG16 utilizing the arithmetic optimization algorithm for early detection of Alzheimer's disease

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2022年第0期74卷 103455-103455页

作者： Deepa, N. Chokkalingam, S. P. Saveetha Inst Med & Tech Sci Saveetha Sch Engn Dept Comp Sci & Engn Chennai Tamilnadu India

Early detection and prevention of Alzheimer's disease (AD) is an important and challenging task. Determining a precise and accurate diagnosis of Alzheimer's disease in its early stages is the most significant challenge. As a result, various research for the early detection of Alzheimer's disease was conducted. However, these techniques have a number of drawbacks, including higher computational costs, failure to incorporate data from multiple modalities, performance degradation due to data distributions between training and testing data, inability to record brain affected regions, longer processing time, etc. To tackle these issues, we proposed Optimized VGG-16 architecture using arithmetic optimization algorithm (Optimized VGG-16 using AOA) for AD classification. Three major components are involved in this study such as pre-processing, segmentation, and classification. The CAT12 toolkit is used to process the format of T1-weighted MRI images during pre-processing. The image enhancement techniques normalize the uneven light distribution in which the linear contrast stretching enhances the image contrast level. Finally, an Optimized VGG-16 using AOA effectively classifies the AD classes such as normal, mild dementia (severe cognitive decline), and late dementia (very severe cognitive decline) classes. The dataset images are chosen from Alzheimer's disease Neuroimaging Initiative (ADNI), the Open Access Series of Imaging Studies (OASIS) dataset, and Single Individual volunteer for Multiple Observations across Networks (SIMON) databases. The experimental investigations provided superior classification performances than other existing methods.

关键词： Alzheimer 's disease ADNI dataset An adaptive ROI VGG-16 arithmetic optimization algorithm

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Kalman filtering and sine arithmetic optimization algorithm (KSAOA) for wireless sensor network clustering

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DIGITAL SIGNAL PROCESSING 2024年 149卷

作者： Yuan, Hao Li, Hongbing Wu, Tianwen Zeng, Die Wang, Yuning Zhang, Wei Chongqing Three Gorges Univ Chongqing Municipal Key Lab Intelligent Informat P Chongqing 404120 Peoples R China Chongqing Three Gorges Univ Internet Things & Intelligent Control Technol Chon Chongqing 404120 Peoples R China Chongqing Three Gorges Univ Chongqing Key Lab Geol Environm Monitoring & Disas Chongqing 404120 Peoples R China

Hierarchical routing is a crucial approach for enhancing the energy efficiency of Wireless Sensor Networks (WSN). The selection of sensor clusters poses a Non -deterministic Polynomial (NP) problem, typically addressed using meta -heuristic algorithms. However, existing research has not adequately considered the observation error of node energy and the limited convergence performance of their meta -heuristic algorithms. To address these challenges, we proposes a novel approach called KSAOA (Kalman filtering and Sine arithmetic optimization algorithm) for WSN clustering, aimed at prolonging the service lifetime and enhancing the network balance of WSN. The proposed model combines Kalman filtering for estimating node energy and the Sine arithmetic optimization algorithm (SAOA) for clustering nodes. SAOA builds upon the arithmetic optimization algorithm (AOA) by introducing a sine factor to extend the exploration boundary and a levy flight strategy to improve local exploration. Additionally, greedy algorithms are employed in the data transfer phase to enhance operational efficiency. The experimental results demonstrate the effectiveness of the proposed method in tabular and graphical form. The results are analyzed through comparative experiments to show that the proposed strategy is effective in improving the balance of network lifetime and network energy consumption.

关键词： Wireless sensor network Kalman filter arithmetic optimization algorithm Node energy Cluster strategy

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Parameter extraction of single, double, and three diodes photovoltaic model based on guaranteed convergence arithmetic optimization algorithm and modified third order Newton Raphson methods

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RENEWABLE & SUSTAINABLE ENERGY REVIEWS 2022年 162卷

作者： Ridha, Hussein Mohammed Hizam, Hashim Mirjalili, Seyedali Othman, Mohammad Lutfi Ya'acob, Mohammad Effendy Ahmadipour, Masoud Univ Putra Malaysia Fac Engn Dept Elect & Elect Engn Serdang 43400 Malaysia Univ Putra Malaysia Fac Engn Adv Lightning & Power & Energy Res ALPER Serdang 43400 Malaysia Univ Al Mustansiriyah Dept Comp Engn Baghdad 10001 Iraq Torrens Univ Australia Ctr Artificial Intelligence Res & Optimizat Fortitude Valley Brisbane Qld 4006 Australia Yonsei Univ Yonsei Frontier Lab Seoul South Korea Univ Putra Malaysia Fac Engn Dept Proc & Food Engn Serdang 43400 Selangor Malaysia Univ Teknol MARA Coll Engn Sch Elect Engn Shah Alam 40450 Selangor Malaysia

Extraction of the photovoltaic (PV) model parameters is critical for forecasting these systems' energy output. Numerous research have reviewed and presented approaches for figuring out the PV models parameter optimization problem in the literature. However, few studies have been undertaken to construct the objective function, or no review papers have been published on the applied methodologies for solving the equations of nonlinear, multi-variable, and complicated PV models based on the datasheet information or actual experimental data. Therefore, this study seeks to first explore the acquired approaches to solve the equations of PV models. Then, utilizing actual measured laboratory data collected under a variety of environmental circumstances, a hybrid approach for efficiently determining unknown the parameters of the single, double, and three diodes PV model has been developed. The proposed guaranteed convergence arithmetic optimization algorithm based on efficient modified third order Newton Raphson (GCAOAEmNR) method highlights important contributions to the literature in terms of methodology (explorer-exploiter phases) and objective function design. The experimental findings exhibit that the GCAOAEmNR minimizes the error to zero under diverse statistical criteria and environmental conditions. Moreover, the GCAOAEmNR beats all well-published approaches in the literature in terms of accuracy, stability, and convergence rate while requiring a reasonable amount of processing time.

关键词： Photovoltaic cell I-V curve arithmetic optimization algorithm Newton raphson method optimization

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Multi-objective scheduling of IoT-enabled smart homes for energy management based on arithmetic optimization algorithm: A Node-RED and NodeMCU module-based technique

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KNOWLEDGE-BASED SYSTEMS 2022年 247卷 1页

作者： Bahmanyar, Danial Razmjooy, Navid Mirjalili, Seyedali Tafresh Univ Dept Elect & Control Engn Tafresh *** Iran Islamic Azad Univ Ardabil Branch Young Researchers & Elite Club Ardebil *** Iran Torrens Univ Australia Ctr Artificial Intelligence Res & Optimizat Brisbane Australia Yonsei Univ Yonsei Frontier Lab YFL Seoul South Korea

The home energy management system (HEMS) based on advanced internet of things (IoT) technology has attracted the special attention of engineers in the field of smart grid (SG), which has the task of the demand side management (DSM) and helps to control the equality between demand and electricity supply. The main performance of HEMS is based on the optimal scheduling of home appliances because it manages power consumption by automatically controlling the loads and transferring them from peak hours to off-peak hours. This paper presents a multi-objective version of a newly introduced metaheuristic, called arithmetic optimization algorithm (AOA) to discover optimal scheduling of the home appliances, which is called Multi-Objective arithmetic optimization algorithm (MOAOA). Furthermore, the HEMS architecture has been programmed based on the Raspberry Pi minicomputer with Node-RED and NodeMCU modules. HEMS uses the MOAOA algorithm to find the optimal schedule pattern to reduce daily electricity costs, reduce the peak to average ratio (PAR), and increase user comfort (UC). Real-time pricing (RTP) and critical peak pricing (CPP) signals are presumed as energy tariffs. Simulations are performed in two different scenarios: (I) appliance scheduling scheme and (II) appliance scheduling scheme with the integration of renewable energy sources (RES). The results of MOAOA are compared with Multi-Objective Particle Swarm optimization (MOPSO), Multi-Objective Gray Wolf Optimizer (MOGWO), and Multi-Objective Antlion optimization (MOALO) algorithms. The results demonstrate that the use of the presented scheme remarkably reduces the cost of electricity consumption as well as PAR, in addition to the integration of MOAOA with RES, which greatly increases user comfort. (C)& nbsp;2022 Elsevier B.V. All rights reserved.

关键词： Multi-objective optimization arithmetic optimization algorithm Internet of things Home Energy Management System Real-time pricing

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A novel study on forecasting the airfoil self-noise, using a hybrid model based on the combination of CatBoost and arithmetic optimization algorithm

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EXPERT SYSTEMS WITH APPLICATIONS 2023年第PartA期229卷

作者： Rastgoo, Amir Khajavi, Hamed KN Toosi Univ Technol Dept Civil Engn Tehran Iran

One of the important challenges in aircraft design is the light, inexpensive, and self-noise-free design of airfoil. Since the self-noise generated by rotor blades interacting with turbulent flow produced in their boundary layer can impact how well the system works. Predicting aerofoil self-noise can help shorten computational times and improve cost-effectiveness because traditional computational fluid dynamics methods flow has more time con-sumption. Therefore, this paper proposes a forecasting approach using the hybrid model of the CatBoost algo-rithm. In this way, the hyperparameters of the CatBoost are tuned by twelve optimizers to increase precision and speed up forecasting. The precision of the hybrid models is evaluated by statistical evaluation indices such as Coefficient of determination (R2), Root Mean Square Error (RMSE), Mean Absolute Error (MSE), and relative absolute error (MAE). The obtained results show that the CatBoost combination with the arithmetic Optimi-zation algorithm better operates optimally and depicts lower error values compared to other optimizers. Also, the values of the coefficient of determination for training and testing data with 0.9999 and 0.9706, respectively, demonstrate that arithmetic Optimizer is the most precise optimizer among studied metaheuristic algorithms.

关键词： Airfoil self-noise prediction arithmetic optimization algorithm CatBoost algorithm Machine learning Metaheuristic algorithms

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