Parameter estimation of photovoltaic cells using an improved chaotic whale optimization algorithm

Oliva, Diego; Elaziz, Mohamed Abd; Hassanien, Aboul Ella

doi:10.1016/j.apenergy.2017.05.029

Cited by 533 publications

(222 citation statements)

References 54 publications

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“…The WOA algorithm is one of the nature‐inspired optimization algorithms which is inspired from the bubble net hunting process of the humpback whales and can be used in different optimization problems …”

Section: Wao Based On Chaos Theorymentioning

confidence: 99%

“…[41][42][43] The WOA algorithm is one of the nature-inspired optimization algorithms which is inspired from the bubble net hunting process of the humpback whales and can be used in different optimization problems. [44][45][46] The algorithm starts with a random vector of variables as the whale's population to find the global solution for the optimization problem. The bubble-net feeding process of the humpback whale is A mathematically modeled as follows:…”

Section: The Original Waomentioning

confidence: 99%

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Brain tumor diagnosis based on artificial neural network and a chaos whale optimization algorithm

Gong

Gao

Abza

2019

Computational Intelligence

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Accurate and early detection of the brain tumor region has a great impact on the choice of treatment, its success rate, and the follow‐up of the disease process over time. This study presents a new bioinspired technique for the early detection of the brain tumor area to improve the chance of completely healing. The study presents a multistep technique to detect the brain tumor area. Herein, after image preprocessing and image feature extraction, an artificial neural network is used to determine the tumor area in the image. The method is based on using an improved version of the whale optimization algorithm for optimal selection of the features and optimizing the artificial neural network weights for classification. Simulation results of the proposed method are applied to FLAIR, T1, and T2 datasets and are compared with different algorithms. Three performance indexes including correct detection rate, false acceptance rate, and false rejection rate are selected for the system performance analysis. Final results showed the superiority of the proposed method toward the other similar methods.

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Section: Wao Based On Chaos Theorymentioning

confidence: 99%

Section: The Original Waomentioning

confidence: 99%

Brain tumor diagnosis based on artificial neural network and a chaos whale optimization algorithm

Gong

Gao

Abza

2019

Computational Intelligence

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“…13 Aljarah et al used WOA for training multilayer perceptron (MLP) neural networks. 16 Tharwat et al used WOA with Support Vector Machines (SVMs) for predicting drug toxicity prior its development. 15 Oliva et al enhanced WOA by combining it with chaotic maps that are used for better estimating the parameters of Photo-Voltaic (PV) cells using circuit models.…”

mentioning

confidence: 99%

“…15 Oliva et al enhanced WOA by combining it with chaotic maps that are used for better estimating the parameters of Photo-Voltaic (PV) cells using circuit models. 16 Tharwat et al used WOA with Support Vector Machines (SVMs) for predicting drug toxicity prior its development. 17 Dao et al proposed a planning algorithm used for optimizing mobile robot path planning problems 18 ; the algorithm is based on a multi-objective approach and is integrated with WOA.…”

mentioning

confidence: 99%

Distributed Whale Optimization Algorithm based on MapReduce

Khalil

Alshayeji

Ahmad

2018

Concurrency and Computation

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Whale Optimization Algorithm (WOA) is a recent swarm intelligence based meta-heuristic optimization algorithm, which simulates the natural behavior of bubble-net hunting strategy of humpback whales and has been successfully applied to solve complex optimization problems in a wide range of disciplines. However, when applied to large-size problems, its performance degrades due to the need of massive computational work load. Distributed computing is one of the effective ways to improve the scalability of WOA for solving large-scale problems. In this paper, we propose a simple and robust distributed implementation of WOA using Hadoop MapReduce named MR-WOA. MapReduce paradigm is adopted as the distribution model since it is one of the most mature technologies to develop parallel algorithms which automatically handles communication, load balancing, data locality, and fault tolerance. The design of MR-WOA is discussed in details using the MapReduce paradigm. Experiments are conducted for a set of well-known benchmarks for evaluating the quality, speedup, and scalability of MR-WOA. The conducted experiments reveal that our approach achieves a promising speedup. For some benchmarks, speedup scales linearly with increasing the number of computational nodes.

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“…WOA easy to implement and has few adjustment parameters, which make it superior than particle swarm optimization (PSO), grey wolf optimizer (GWO) algorithm, and gravitational search algorithm (GSA), etc. As a result, WOA has attracted much attention and has been applied to handle many practical engineering application problems, such as training multi-layer perceptron in neural network [2], tracking MPP of photovoltaic system [3], optimizing the active and reactive power dispatch problem [4], feature selection [5], parameter estimation of photovoltaic cells [6], multilevel thresholding image segmentation [7], and so on.…”

Section: Introductionmentioning

confidence: 99%

Whale optimization algorithm with nonlinear control parameter

Zhong

Long

2017

MATEC Web Conf.

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Abstract. Whale optimization algorithm (WOA) is a relatively novel intelligence optimization technique which has been shown to be competitive to other population-based algorithms. However, the control parameter is a major factor to affect the algorithm's convergence precision and speed. At present, few of a them are aiming at control parameter setting in WOA algorithm. This paper proposes corresponding improved WOA algorithm with different nonlinear adjustment strategy of control parameter by adopting a sinusoid, cosine, tangential, logarithmic and quadratic curves. The experimental results for six benchmark test functions show that the proposed nonlinear adjustment strategies are superior to the classical linear strategy.

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Parameter estimation of photovoltaic cells using an improved chaotic whale optimization algorithm

Cited by 533 publications

References 54 publications

Brain tumor diagnosis based on artificial neural network and a chaos whale optimization algorithm

Brain tumor diagnosis based on artificial neural network and a chaos whale optimization algorithm

Distributed Whale Optimization Algorithm based on MapReduce

Whale optimization algorithm with nonlinear control parameter

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