A novel hybrid system based on a new proposed algorithm—Multi-Objective Whale Optimization Algorithm for wind speed forecasting

Wang, Jianzhou; Du, Pei; Niu, Tong; Yang, Wendong

doi:10.1016/j.apenergy.2017.10.031

Cited by 259 publications

(78 citation statements)

References 38 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%

See 1 more Smart Citation

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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“…For shortterm wind speed forecasting, Ma et al [34] developed a wind speed prediction model by using singular spectrum analysis (SSA) to derive a noise removal sequence corresponding to a real sequence to predict short-term wind speeds. Wang et al [35] developed a hybrid wind speed prediction model using complete ensemble empirical mode decomposition (CEEMD), multiobjective whale optimization, and an Elman neural network. Meng et al [36] proposed a short-term wind speed prediction hybrid model combining data preprocessing with an artificial neural network and various optimization methods.…”

Section: Introductionmentioning

confidence: 99%

Research on Hybrid Wind Speed Prediction System Based on Artificial Intelligence and Double Prediction Scheme

Nie

Zhang

et al. 2020

Complexity

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Wind energy analysis and wind speed modeling have a significant impact on wind power generation systems and have attracted significant attention from many researchers in recent decades. Based on the inherent characteristics of wind speed, such as nonlinearity and randomness, the prediction of wind speed is considered to be a challenging task. Previous studies have only considered point prediction or interval measurement of wind speed separately and have not combined these two methods for prediction and analysis. In this study, we developed a novel hybrid wind speed double prediction system comprising a point prediction module and interval prediction module to compensate for the shortcomings of existing research. Regarding point prediction in the developed double prediction system, a novel nonlinear integration method based on a backpropagation network optimized using the multiobjective evolutionary algorithm based on decomposition was successfully implemented to derive the final prediction results, which enable further improvement of the accuracy of point prediction. Based on point prediction results, we propose an interval prediction method that constructs different intervals according to the classification of different data features via fuzzy clustering, which provides reliable interval prediction results. e experimental results demonstrate that the proposed system outperforms existing methods in engineering applications and can be used as an effective technology for power system planning.

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“…16 Tharwat et al used WOA with Support Vector Machines (SVMs) for predicting drug toxicity prior its development. Other recent fruitful applications of WOA include software testing, 23 wind speed prediction, 24 and power systems. Ling et al applied Lévy flight trajectory to WOA for enhancing its precision in finding best global optima and convergence rate.…”

mentioning

confidence: 99%

“…Abdel-Basset et al 22 introduced a novel modified version of WOA for cryptanalysis of a well-known Merkle-Hellman public key cryptosystem. Other recent fruitful applications of WOA include software testing, 23 wind speed prediction, 24 and power systems. 25,26 The traditional sequential WOA operated on a single machine has proven its performance in solving optimization problems when compared with other well-known EAs.…”

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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A novel hybrid system based on a new proposed algorithm—Multi-Objective Whale Optimization Algorithm for wind speed forecasting

Cited by 259 publications

References 38 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

Research on Hybrid Wind Speed Prediction System Based on Artificial Intelligence and Double Prediction Scheme

Distributed Whale Optimization Algorithm based on MapReduce

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