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Novel parameter extraction for Single, Double, and three diodes photovoltaic models based on robust adaptive arithmetic optimization algorithm and adaptive damping method of Berndt-Hall-Hall-Hausman

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SOLAR ENERGY 2022年 243卷 35-61页

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 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 Obuda Univ Univ Res & Innovat Ctr H-1034 Budapest Hungary

Experimental data-oriented parameter extraction helps in giving an accurate assessment to forecast the output current of the photovoltaic cells. This may be very beneficial in the modeling, optimization, partial shading, and assessment of the PV systems both practically and conceptually. Attributed to the reason that hybrid techniques incorporate the advantages of two or more algorithms, they outperform stochastic methods in solving the given complex multi-dimensional and multi-model optimization problems. Many authors have proposed hybrid methods to improve precision, steadiness, and overall efficiency. However, most of the algorithms concentrate on the methodology in the literature, and little attention has been paid to the objective function formulation, leading to a theoretical void in this area of research. In this work, we present a robust adaptive arithmetic optimization algorithm based on the adaptive damping Berndt-hall-hall-Hausman (RaAOAAdBHHH) approach to efficacity determine the parameters of the single, double, and three diode PV model. The experimental results demonstrate that the proposed RaAOAAdBHHH approach successfully minimizes error to zero with rapid convergence, as determined by different statistical criteria and compared to experimental data.

关键词： Photovoltaic cell Meta-heuristic Parameter extraction arithmetic optimization algorithm Berndt-Hall-Hall-Hausman algorithm

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An enhanced arithmetic optimization algorithm for global maximum power point tracking of photovoltaic systems under dynamic irradiance patterns

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ENERGY SOURCES PART A-RECOVERY UTILIZATION AND ENVIRONMENTAL EFFECTS 2022年第4期44卷 10116-10134页

作者： Thota, Rajasekar Sinha, Nidul Natl Inst Technol Silchar Dept Elect Engn Silchar Assam India

The photovoltaic power system is highly dependent on the quantity of solar irradiance incident on it and its temperature. These parameters are dynamically changing with time due to climatic changes, shadows formed by clouds, trees, buildings, and so on, resulting in multiple maximum power points (MPP). Out of which one is global MPP (GMPP) and the rest are local MPP (LMPP). The GMPP varies dynamically along with the weather conditions and connected load. Many GMPP tracking (GMPPT) algorithms were developed which are inefficient and ineffective under dynamic irradiance conditions. This paper proposes a new enhanced arithmetic optimization algorithm based on the levy flight (AOA-LF) as a GMPPT method, which improves the tracking efficiency and tracking speed because of its good exploration and exploitation due to the long jump with a variable step size. The suggested AOA-LF GMPPT method is implemented in MATLAB/SIMULINK and tested with eight different dynamic irradiance patterns whose tracking curves are compared with the existing GMPPT algorithms like JAYA-LF, DFO, AOA, PSO, and P&O, which reveals that it is excellent in the tracking of GMPP with increment in efficiency as high as 19% when compared with P&O method and the least time to reach the GMPP with percentage decrement as high as 69% as compared to the PSO method. Also, the proposed AOA-LF GMPPT method is validated with the help of OPAL-RT OP4510 Real-Time Digital Simulator. Hence, the proposed algorithm shows superior performance with zero steady-state oscillations and also enhanced exploration and exploitation.

关键词： arithmetic optimization algorithm Levy flight GMPPT photovoltaic power system dynamic irradiance real-time digital simulator

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A guided epsilon-dominance arithmetic optimization algorithm for effective multi-objective optimization in engineering design problems

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MULTIMEDIA TOOLS AND APPLICATIONS 2024年第11期83卷 31673-31700页

作者： Zouache, Djaafar Abualigah, Laith Boumaza, Farid Univ Mohamed El Bachir El Ibrahimi Dept Comp Sci Bordj Bou Arreridj Algeria Univ Sci & Technol Houari Boumediene LRIA Lab Algiers Algeria Al al Bayt Univ Prince Hussein Bin Abdullah Fac Informat Technol Comp Sci Dept Mafraq 25113 Jordan Lebanese Amer Univ Dept Elect & Comp Engn Byblos 13505 Lebanon Al Ahliyya Amman Univ Hourani Ctr Appl Sci Res Amman 19328 Jordan Middle East Univ MEU Res Unit Amman 11831 Jordan Appl Sci Private Univ Appl Sci Res Ctr Amman 11931 Jordan Univ Sains Malaysia Sch Comp Sci George Town 11800 Malaysia Sunway Univ Malaysia Sch Engn & Technol Petaling Jaya 27500 Malaysia Univ Oran1 Dept Comp Sci Oran Algeria

The arithmetic optimization algorithm (AOA) was recently proposed as a solution for single-objective real continuous problems and has demonstrated superior performance in various contexts. This paper presents a multi-objective version of the algorithm to solve multi-objective problems. The MOAOA employs an archive repository to keep and retrieve the non-dominated solutions produced during optimization. The leaders are then selected from the population archive to lead the solutions of the main population toward the potential search locations. The epsilon-dominance and crowding distance strategies balance the convergence and diversity of the obtained Pareto set. To assess the effectiveness and efficiency of the proposed algorithm, it was tested on various dimensions of multi-objective benchmarks, among them: five unconstrained test functions taken from ZDT-series and four multi-objective constrained tests functions (BNH, SRN, TNK, OSY). Also, it is evaluated on four multi-objective structural design problems, such as welded beam design, speed-reduced design, disk brake design, and four-bar truss design. The algorithm is compared with three algorithms widely used in multi-objectives optimization, such as MSSA, MOEA-D, and MOGWO. The comparison results demonstrate that MOAOA outperforms all other algorithms in terms of both convergence and diversity of solutions, achieving a score of (100%,100%) in the ZDT tests, (75%,50%) in the constrained test functions, and (75%,75%) for the structural design problems.

关键词： Multi-objective optimization Crowding distance Epsilon-dominance arithmetic optimization algorithm Engineering design application

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Illumination estimation via random vector functional link based on improved arithmetic optimization algorithm

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COLOR RESEARCH AND APPLICATION 2022年第3期47卷 644-656页

作者： Liu, Xiangqi Zhou, Zhiyu Hangzhou Dianzi Univ Coll Mech Engn Informat Engn Sch Hangzhou Peoples R China Hangzhou Dianzi Univ Sch Mech Engn Hangzhou Peoples R China Zhejiang Sci Tech Univ Sch Informat Sci & Technol Hangzhou Peoples R China

In order to solve the problem that the existing illumination estimation methods have low accuracy in image restoration, a random vector functional link illumination estimation algorithm based on whale optimization arithmetic algorithm is proposed in this article. First, in order to improve the optimization accuracy of arithmetic optimization algorithm, the whale optimization algorithm is applied to optimize the initial population of the arithmetic optimization algorithm. Then, we utilize the improved arithmetic optimization algorithm to improve the input weight and hidden layer offset of random vector functional link, and exclude the uncertainty caused by the random generation of random vector functional link's input weight and hidden layer bias. Finally, the proposed whale arithmetic optimization algorithm-random vector functional link model is applied to SFU lab dataset and Gehler-Shi dataset for illumination estimation. Compared with the other earlier illumination estimation algorithms, the proposed illumination estimation model in this article has better effect and higher stability. In addition, due to the diagonal mapping method for color correction and image restoration, the proposed illumination estimation algorithm can also get better results in image restoration.

关键词： arithmetic optimization algorithm illumination estimation random vector functional link whale optimization algorithm

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A Multi-strategy Enhanced arithmetic optimization algorithm and Its Application in Path Planning of Mobile Robots

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NEURAL PROCESSING LETTERS 2024年第1期56卷 18-18页

作者： Deng, Xuzhen He, Dengxu Qu, Liangdong Guangxi Minzu Univ Sch Math & Phys Nanning 530006 Guangxi Peoples R China Guangxi Minzu Univ Sch Artificial Intelligence Nanning 530006 Guangxi Peoples R China

A multi-strategy enhanced arithmetic optimization algorithm called MSEAOA is proposed to address the issues of low population diversity, imbalanced exploration and exploitation capabilities, and low accuracy of optimal solution in the arithmetic optimization algorithm. Firstly, using the good point set strategy for population initialization to improve population diversity and thus accelerate convergence speed. Secondly, we integrate the exploration and exploition capabilities of differential self-learning strategy, best example learning strategy, and second-order differential perturbation strategy balancing algorithm. Finally, the introduction of somersault foraging strategy improves the accuracy of the optimal solution. We select 14 classical benchmark test functions and the CEC2019 function test set to test the optimization ability of MSEAOA, and apply MSEAOA to the path planning problem of mobile robots. MSEAOA is compared with other meta-heuristic optimization algorithms, and the experimental results are statistically analyzed by the Wilcoxon rank-sum test. The simulation experimental results show that MSEAOA performs the best among 14 benchmark functions, but for 10 CEC2019 functions, MSEAOA has the best optimization performance among 5 of them (50%). In the path optimization problem of mobile robots, the path obtained by MSEAOA is also the best among all algorithms, its path shortening rate exceeds 8.8% in 83% of environments. The results indicate that MSEAOA is a reliable algorithm suitable for function optimization and practical optimization problems.

关键词： arithmetic optimization algorithm Multi-strategy enhancement Benchmark functions Path planning

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Hybrid arithmetic optimization algorithm with hunger games search for global optimization

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MULTIMEDIA TOOLS AND APPLICATIONS 2022年第20期81卷 28755-28778页

作者： Mahajan, Shubham Abualigah, Laith Pandit, Amit Kant Shri Mata Vaishno Devi Univ Sch Elect & Commun Katra 182320 J&k India Amman Arab Univ Fac Comp Sci & Informat Amman 11953 Jordan Univ Sains Malaysia Sch Comp Sci George Town 11800 Malaysia

Recently, many population-dependent methods have been proposed. Despite their acceptance in many applications, we are still exploring suggested methods to solve actual problems. Consequently, researchers need to change and refine their procedures significantly based on the major evolutionary processes to achieve quicker convergence, more consistent equilibrium with high-quality performance and optimization. Therefore, a new hybrid method using Hunger Games Search (HGS) and arithmetic optimization algorithm (AOA) is proposed in this paper. HGS is a recently proposed population-dependent optimization method that stabilizes the features and efficiently performs unconstrained and constrained problems. In contrast, AOA is a modern meta-heuristic optimization method. They can be applied to different problems, including image processing, machine learning, wireless networks, power systems, engineering design etc. The proposed method is analyzed in context with HGS and AOA. Each method is tested on the same parameters like population size and no. of iteration to evaluate the performance. The proposed method (AOA-HGS) is assessed by varying the dimensions on 23 functions (F1-F23). The impact of varying dimensions is a standard test utilized in previous studies for optimizing test functions that show the effect of varying dimensions on the efficiency of AOA-HGS. From this, it is noted that it works efficiently for both high and low dimensional problems. In high dimensional problem population, dependent methods give efficient search results. The AOA-HGS is very competitive and superior compared with others on test functions. No. of optimization methods obtained optimum results, but AOA-HGS has the best when compared with all. So, AOA-HGS is capable of getting optimum results.

关键词： Hunger games search arithmetic optimization algorithm Engineering design problems Metaheuristic Swarm intelligence optimization methods

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A novel improved arithmetic optimization algorithm for optimal design of PID controlled and Bode's ideal transfer function based automobile cruise control system

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EVOLVING SYSTEMS 2022年第3期13卷 453-468页

作者： Izci, Davut Ekinci, Serdar Kayri, Murat Eker, Erdal Batman Univ Dept Elect & Automat TR-72060 Batman Turkey Batman Univ Dept Comp Engn TR-72100 Batman Turkey Van Yuzuncu Yil Univ Dept Comp & Instruct Technol Educ Van Turkey Mus Alparslan Univ Vocat Sch Social Sci Mus Turkey

This paper considers the development of a novel hybrid metaheuristic algorithm which is proposed to achieve an optimum design for automobile cruise control (ACC) system by using a proportional-integral-derivative (PID) controller based on Bode's ideal transfer function. The developed algorithm (AOA-NM) adopts one of the recently published metaheuristic algorithms named the arithmetic optimization algorithm (AOA) to perform explorative task whereas another well-known local search method known as Nelder-Mead (NM) simplex search to perform exploitative task. The developed hybrid algorithm was initially tested on well-known benchmark functions by comparing the results with only its original version since AOA has already been shown to be better than other state-of-the-art algorithms. The statistical results obtained from benchmark functions have demonstrated better capability of AOA-NM. Furthermore, a PID controller based on Bode's ideal transfer function was adopted to regulate an ACC system optimally. Statistical, convergence rate, time domain and frequency domain analyses were performed by comparing the performance of AOA-NM with AOA. The respective analyses have shown better capability of the proposed hybrid algorithm. Moreover, the capability of the proposed AOA-NM based PID control scheme was compared with other available approaches in the literature by using time domain analysis. The latter case has also confirmed enhanced capability of the proposed approach for regulating an ACC system which further verified the ability of the proposed AOA-NM algorithm. Lastly, other recently reported and effective metaheuristic algorithms were also used to assess the performance of the proposed approach. The obtained comparative results further confirmed the AOA-NM to be a greater tool to achieve more successful results for ACC system.

关键词： arithmetic optimization algorithm Nelder-Mead simplex search Bode's ideal transfer function PID controller Automobile cruise control system

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Logarithmic spiral search based arithmetic optimization algorithm with selective mechanism and its application to functional electrical stimulation system control

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SOFT COMPUTING 2022年第22期26卷 12257-12269页

作者： Ekinci, Serdar Izci, Davut Al Nasar, Mohammad Rustom Abu Zitar, Raed Abualigah, Laith Batman Univ Dept Comp Engn TR-72100 Batman Turkey Batman Univ Dept Elect & Automat TR-72060 Batman Turkey Aldar Univ Coll Dept Informat Technol Dubai U Arab Emirates Sorbonne Univ Abu Dhabi Sorbonne Ctr Artificial Intelligence Abu Dhabi 38044 U Arab Emirates Amman Arab Univ Fac Comp Sci & Informat Amman 11953 Jordan Univ Sains Malaysia Sch Comp Sci George Town 11800 Malaysia

A biomedical application of a novel metaheuristic optimizer is proposed in this paper by constructing an enhanced arithmetic optimization algorithm (AOA). The latter algorithm was constructed using the logarithmic spiral (Ls) search mechanism from the whale optimization algorithm and the greedy selection scheme from the differential evolution algorithm. The proposed algorithm (Ls-AOA) was tested against unimodal and multimodal benchmark functions and demonstrated better capability comparatively using other efficient metaheuristic algorithms reported in the literature. The constructed Ls-AOA algorithm was then proposed to design a proportional-integral-derivative (PID) controller employed in a functional electrical stimulation (FES) system for the first time. The initial statistical and convergence profile assessment showed better performance of the proposed algorithm. The comparative analyses for transient and frequency responses were performed for the PID-controlled FES system using the original AOA, sine-cosine and particle swarm optimization algorithms and the traditional Ziegler-Nichols tuning scheme. Similarly, the FES system tuned with the latter methods was also assessed for disturbance rejection and noise elimination. All the comparative analyses demonstrated that the proposed Ls-AOA has the greater capability for the challenging biomedical FES system.

关键词： Functional electrical stimulation PID controller Logarithmic spiral search mechanism Greedy selection scheme arithmetic optimization algorithm

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UAV path planning in mountain areas based on a hybrid parallel compact arithmetic optimization algorithm

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NEURAL COMPUTING & APPLICATIONS 2023年 1-16页

作者： Wang, Ruo-Bin Wang, Wei-Feng Geng, Fang-Dong Pan, Jeng-Shyang Chu, Shu-Chuan Xu, Lin North China Univ Technol Sch Informat Sci & Technol Beijing 100144 Peoples R China North China Univ Technol Beijing Urban Governance Res Ctr Beijing 100144 Peoples R China Univ South Australia STEM Adelaide 5095 Australia Shandong Univ Sci & Technol Coll Comp Sci & Engn Qingdao 266590 Peoples R China Flinders Univ S Australia Coll Sci & Engn 1284 South Rd Tonsley SA 5042 Australia

Unmanned Aerial Vehicle (UAV) path planning is one of the core components of its entire autonomous control system. The main challenge lies in efficiently obtaining an optimal flight route in complex environments, especially in mountain areas. To address this, we propose a novel version of arithmetic optimization algorithm (AOA), named parallel and compact AOA (PCAOA). In PCAOA, the compact technique can save the memory of UAV and shorten the calculation time, and the parallel technique can quicken the convergence speed and improve the solution accuracy. In addition, the flight path generated by PCAOA is smoothed with cubic B-spline curves, making the path suitable for a UAV. The performance of PCAOA is demonstrated on 23 benchmark functions. Experimental results show that PCAOA achieves competitive results. Finally, the simulation studies are conducted to verify that PCAOA can successfully acquire a feasible and effective route in different mountain areas.

关键词： UAV path planning Parallel technique Compact technique arithmetic optimization algorithm

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Hybrid Aquila optimizer with arithmetic optimization algorithm for global optimization tasks

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SOFT COMPUTING 2022年第10期26卷 4863-4881页

作者： Mahajan, Shubham Abualigah, Laith Pandit, Amit Kant Altalhi, Maryam Shri Mata Vaishno Devi Univ Sch Elect & Commun Katra 182320 Jammu & Kashmir India Amman Arab Univ Fac Comp Sci & Informat Amman 11953 Jordan Univ Sains Malaysia Sch Comp Sci George Town Malaysia Taif Univ Coll Business Adm Dept Management Informat Syst POB 11099 At Taif 21944 Saudi Arabia

Many population-dependent solutions have recently been suggested. Despite their widespread adoption in many applications, we are still researching using suggested methods to solve real-world problems. As a result, researchers must significantly adjust and refine their procedures based on the main evolutionary processes to ensure faster convergence, consistent equilibrium with high-quality results, and optimization. Thus, a new hybrid method using Aquila optimizer (AO) and arithmetic optimization algorithm (AOA) is proposed in this paper. AO and AOA are both modern meta-heuristic optimization methods. They can be applied to different problems, including image processing, machine learning, wireless networks, power systems, engineering design etc. The proposed approach is examined concerning AO and AOA. To determine results, each procedure is evaluated using the same parameters, such as population size and several iterations. By changing the dimensions, the proposed approach (AO-AOA) is evaluated. The impact of varying dimensions is a standard test that has been used in previous studies to optimize test functions that demonstrate the influence of varying dimensions on the efficiency of AO-AOA. It is clear from this that it fits well with both high- and low-dimensional problems. Population-based methods achieve efficient search results in high-dimensional problems.

关键词： arithmetic optimization algorithm Aquila optimizer Engineering design problems Metaheuristic optimization methods

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