A modified equilibrium optimizer using opposition-based learning and novel update rules

Fan, Qiang; Huang, Haisong; Yang, Kai; Zhang, Songsong; Yao, Liguo; Xiong, Qiaoqiao

doi:10.1016/j.eswa.2021.114575

Cited by 49 publications

(21 citation statements)

References 72 publications

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“…This section of the paper comprehensively deliberates the results obtained by the proposed I-AVO algorithm and other selected algorithms, such as AVO, SMA, MPA, ( 39) [83], Chaotic GBO [64], Chaotic Jaya [108], and OBL-GWO. All the selected algorithms are combined with the NR method to get a fair result for performance comparison.…”

Section: Resultsmentioning

confidence: 99%

“…As a result, improving these methods is difficult because it necessitates more exertion in the optimization process, computing, statistics, and noise impacts. The chaotic drifted JAYA algorithm [79], improved JAYA [80], linear population size reduction based on success history adaptive DE (LSHADE) [46], GWO-PSO [81], GWO-Cuckoo search [82], chaotic GBO (CGBO) [64], Opposition-Based Learning EO (OBLEO) algorithm [83], OBLGWO [84], and others are some examples. The PV parameter estimation optimization problem is a hot topic right now.…”

Section: Introductionmentioning

confidence: 99%

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Parameter estimation of three-diode solar photovoltaic model using an Improved-African Vultures optimization algorithm with Newton–Raphson method

Kumar¹,

Mary²

2021

J Comput Electron

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Parameter identification and accurate photovoltaic (PV) modeling from basic I-V information are necessary for simulation, optimization, and control of the PV systems. Therefore, this paper proposes an Improved-African Vultures Optimization (I-AVO) algorithm, which combines the general Opposition-Based Learning (OBL) and Orthogonal Learning to extract the unknown parameters of the solar Photovoltaic (PV) modules accurately and effectually. The proposed I-AVO algorithm is developed from the basic version of the recently proposed African Vultures Optimization (AVO) algorithm. The solar PV parameters estimation problem is considered to be a complex optimization problem with the characteristics such as multidimensional, nonlinear, Transcendental, and multi-modal. Therefore, the basic variant of AVO struggles to produce the optimal and is stuck at local optima when it handles this complex optimization problem. Therefore, the I-AVO is formulated by combining the features of OL and OBL, along with the AVO, to generate the optimal solution. Out of various PV models, Three-Diode Model has been considered to determine the parameters. Furthermore, Newton-Raphson (NR) technique is discussed to solve the chaotic behavior of the I-V curve relation. The obtained results proved that the proposed I-AVO along with NR, called I-AVO-NR, can accurately obtain the optimal solution. The superiority of the proposed algorithm is proved to be better than other advanced algorithms based on the obtained results and their comparison. Based on the statistical test value obtained from Friedman's test, the proposed algorithm stood first among eight algorithms with the ranking value of 1.542 for two case studies.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Parameter estimation of three-diode solar photovoltaic model using an Improved-African Vultures optimization algorithm with Newton–Raphson method

Kumar¹,

Mary²

2021

J Comput Electron

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“…With the help of optimization techniques, a large number of problems encountered in different applied disciplines could be solved in a more efficient, accurate, and real-time way [5,6]. However, with the increasing complexity of global optimization problems nowadays, conventional mathematical methods based on gradient information are challenged by high-dimensional, suboptimal regions, and large-scale search ranges that cannot adapt to the real requirements [7,8]. The development of more effective tools to settle these complex NP-hard problems is an indivisible research hotspot.…”

Section: Introductionmentioning

confidence: 99%

“…With their own distinctive characteristics, these metaheuristics are commonly used in a variety of computing science fields, such as fault diagnosis [42], feature selection [43], engineering optimization [44], path planning [45], and parameters identification [46]. Nevertheless, it has been shown that the most basic algorithms still have the limitations of slow convergence, poor accuracy, and ease of getting trapped into the local optimum [7,15] in several applications. The non-free lunch (NFL) theorem indicates that there is no general algorithm that could be appropriate for all optimization tasks [47].…”

Section: Introductionmentioning

confidence: 99%

An Improved Gorilla Troops Optimizer Based on Lens Opposition-Based Learning and Adaptive β-Hill Climbing for Global Optimization

Xiao¹,

Sun²,

Guo³

et al. 2022

Computer Modeling in Engineering &Amp; Sciences

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Gorilla troops optimizer (GTO) is a newly developed meta-heuristic algorithm, which is inspired by the collective lifestyle and social intelligence of gorillas. Similar to other metaheuristics, the convergence accuracy and stability of GTO will deteriorate when the optimization problems to be solved become more complex and flexible. To overcome these defects and achieve better performance, this paper proposes an improved gorilla troops optimizer (IGTO). First, Circle chaotic mapping is introduced to initialize the positions of gorillas, which facilitates the population diversity and establishes a good foundation for global search. Then, in order to avoid getting trapped in the local optimum, the lens opposition-based learning mechanism is adopted to expand the search ranges. Besides, a novel local search-based algorithm, namely adaptive β-hill climbing, is amalgamated with GTO to increase the final solution precision. Attributed to three improvements, the exploration and exploitation capabilities of the basic GTO are greatly enhanced. The performance of the proposed algorithm is comprehensively evaluated and analyzed on 19 classical benchmark functions. The numerical and statistical results demonstrate that IGTO can provide better solution quality, local optimum avoidance, and robustness compared with the basic GTO and five other wellknown algorithms. Moreover, the applicability of IGTO is further proved through resolving four engineering design problems and training multilayer perceptron. The experimental results suggest that IGTO exhibits remarkable competitive performance and promising prospects in real-world tasks.

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“…The combination examples of metaheuristic algorithms with the OBL scheme can be found in the literature. Few of such enhanced metaheuristic algorithms can be listed as particle swarm optimization (Wang et al, 2011), firefly algorithm (Yu et al, 2015), grasshopper optimization algorithm (Ewees et al, 2018), salp swarm algorithm (Tubishat et al, 2020), crow search algorithm (Shekhawat and Saxena, 2020), equilibrium optimization (Fan et al, 2021), grey wolf optimization (Yu et al, 2021), and artificial electric field algorithm (Demirören et al, 2021). Those listed studies have shown that the combination of the OBL scheme with metaheuristic algorithms provides better convergence rate and exploration capabilities of the original forms.…”

Section: Introductionmentioning

confidence: 99%

Performance evaluation of a novel improved slime mould algorithm for direct current motor and automatic voltage regulator systems

İzci

Ekinci

Zeynelgil

et al. 2021

Transactions of the Institute of Measurement and Control

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This study deals with the controlling the speed of a direct current (DC) motor via a fractional order proportional–integral–derivative (FOPID) controller and maintaining the terminal voltage level of an automatic voltage regulator (AVR) via a proportional–integral–derivative plus second order derivative (PIDD2) controller. To adjust the parameters of those controllers, a novel improved slime mould algorithm (ISMA) is proposed. The latter is a novel metaheuristic algorithm developed in this work. The proposed algorithm aims to improve the original SMA in terms of exploration with the aid of a modified opposition-based learning scheme and in terms of exploitation with the aid of the Nelder–Mead simplex search method. A time domain objective function, which includes time response specifications of steady state error and maximum overshoot along with rise and settling times, is used as a performance index to design the FOPID controller-based DC motor system and PIDD2 controller-based AVR system. The performance of the proposed novel approaches for both systems are assessed through time and frequency domain simulations along with statistical tests which show the greater performance of the improved algorithm. Further to this, the efficacy of the proposed approaches for both systems is compared with other available and effective approaches in the literature. The extensive comparative results demonstrate the proposed method to be superior to those state-of-the-art approaches for both DC motor speed and AVR control systems.

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A modified equilibrium optimizer using opposition-based learning and novel update rules

Cited by 49 publications

References 72 publications

Parameter estimation of three-diode solar photovoltaic model using an Improved-African Vultures optimization algorithm with Newton–Raphson method

Parameter estimation of three-diode solar photovoltaic model using an Improved-African Vultures optimization algorithm with Newton–Raphson method

An Improved Gorilla Troops Optimizer Based on Lens Opposition-Based Learning and Adaptive β-Hill Climbing for Global Optimization

Performance evaluation of a novel improved slime mould algorithm for direct current motor and automatic voltage regulator systems

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