A Systematic and Meta-Analysis Survey of Whale Optimization Algorithm

Mohammed, Hardi M.; Umar, Shahla U.; Rashid, Tarik A.

doi:10.1155/2019/8718571

Cited by 181 publications

(112 citation statements)

References 61 publications

Supporting

Mentioning

110

Contrasting

Unclassified

Order By: Relevance

“…WOAGWO algorithm is implemented and evaluated against 23 benchmark functions [28], 25 benchmark functions from CEC2005, and 10 benchmark functions from CEC2019. The following subsections describe benchmark functions, experimental setup, evaluation criteria, statistical results, and evaluations of WOAGWO against other metaheuristic algorithms.…”

Section: Resultsmentioning

confidence: 99%

“…Because of having problems regarding local optima, the BAT algorithm is used with WOA for the exploration phase. The result of WOA-BAT showed that WOA-BAT improved well comparing to WOA and BAT algorithms [18]. The more detail of WOA modification and hybridization have been described in [18].…”

Section: Introductionmentioning

confidence: 91%

“…WOA has better performance against PSO, Gravitational Search Algorithm (GSA), DE and Feedforward Error Propagation (FEP) [16]. According to [18], WOA performs well in terms of convergence time and balancing between exploration and exploitation.…”

Section: Introductionmentioning

confidence: 99%

“…This algorithm shows a competitive result against other metaheuristic algorithms. For instance, PSO, DE, GSA and FEP [28]. It is very competitive in the exploitation phase compared to others while it has merit results in the exploration phase.…”

Section: Introductionmentioning

confidence: 99%

“…WOA has the following issues [21], [28] which are the main motivations of hybridizing GWO with WOA in this paper: 1) WOA suffers from avoiding local optima as it uses encircling search mechanism. 2) Improving solution in WOA after each iteration is not sufficient.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

A novel hybrid GWO with WOA for global numerical optimization and solving pressure vessel design

Mohammed

Rashid

2020

Neural Comput & Applic

Self Cite

View full text Add to dashboard Cite

A recent metaheuristic algorithm, such as Whale Optimization Algorithm (WOA), was proposed. The idea of proposing this algorithm belongs to the hunting behavior of the humpback whale. However, WOA suffers from poor performance in the exploitation phase and stagnates in the local best solution. Grey Wolf Optimization (GWO) is a very competitive algorithm comparing to other common metaheuristic algorithms as it has a super performance in the exploitation phase while it is tested on unimodal benchmark functions. Therefore, the aim of this paper is to hybridize GWO with WOA to overcome the problems. GWO can perform well in exploiting optimal solutions. In this paper, a hybridized WOA with GWO which is called WOAGWO is presented. The proposed hybridized model consists of two steps. Firstly, the hunting mechanism of GWO is embedded into the WOA exploitation phase with a new condition which is related to GWO. Secondly, a new technique is added to the exploration phase to improve the solution after each iteration. Experimentations are tested on three different standard test functions which are called benchmark functions: 23 common functions, 25 CEC2005 functions and 10 CEC2019 functions. The proposed WOAGWO is also evaluated against original WOA, GWO and three other commonly used algorithms. Results show that WOAGWO outperforms other algorithms depending on the Wilcoxon rank-sum test. Finally, WOAGWO is likewise applied to solve an engineering problem such as pressure vessel design. Then the results prove that WOAGWO achieves optimum solution which is better than WOA and Fitness Dependent Optimizer (FDO).

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 91%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A novel hybrid GWO with WOA for global numerical optimization and solving pressure vessel design

Mohammed

Rashid

2020

Neural Comput & Applic

Self Cite

View full text Add to dashboard Cite

show abstract

Metaheuristic Algorithms and Their Applications in Different Fields

Yaqoob,

Verma,

Aziz

2024

Metaheuristics for Machine Learning

View full text Add to dashboard Cite

Characterization of abnormalities in breast cancer images using nature‐inspired metaheuristic optimized convolutional neural networks model

Oyelade

Ezugwu

2021

Concurrency and Computation

View full text Add to dashboard Cite

Convolutional neural networks (CNN) are deep learning models widely reported as performing well in several image classification tasks. Training the networks for optimal performance is considered an NP-hard problem. The combination of a metaheuristic algorithm with CNN has been proposed to address this problem because the metaheuristic algorithms have efficient performance, parameter tuning, and stagnation prevention methods. Moreover, there is now an increasing need to apply the best performing metaheuristic algorithm to optimize the parameters, training and learning rate of the CNN models. This study aims to investigate the best performing metaheuristic algorithm for fine-tuning the weights, biases, and hyperparameters of CNN networks for solving the problem of characterization of abnormalities in breast images. Furthermore, hybrid models consisting of a CNN architecture and five representative metaheuristic algorithms to efficiently detect breast cancer abnormalities are presented. The adopted approach involves training a CNN network using genetic algorithm (GA), whale optimization algorithm (WOA), multiverse optimizer (MVO), satin bower optimization (SBO), and life choice-based optimization (LCBO) algorithms to optimize only weights and bias of the model. Two categories of experiments were carried out: the first involved training of the CNN model, while the second involved optimizing the training of CNN model with metaheuristic algorithms. A comparative analysis of the impact of the proposed GA, WOA, MVO, SBO, and LCBO metaheuristic algorithms on the performance of the CNN architecture was carried out. Empirical and statistical analyses are presented to validate further the findings obtained in the study. Results obtained showed that MVO, SBO and LCBO outperformed GA, WOA. The classification accuracy obtained for CNN-GA, CNN-WOA, CNN-MVO, CNN-SBO, and CNN-LCBO at the fifth epoch was 0.76, 0.75, 0.84, 0.80, and 0.86, respectively. At the same time, the traditional CNN achieved an accuracy of 0.66. The outcome of this study showed that contrary to widespread practice, physics-based, biology-based, and human-based optimization algorithms promise better performance parameter tuning for CNN as compared with evolutionary-based and swarm-based metaheuristic algorithms.

show abstract

A Systematic and Meta-Analysis Survey of Whale Optimization Algorithm

Cited by 181 publications

References 61 publications

A novel hybrid GWO with WOA for global numerical optimization and solving pressure vessel design

A novel hybrid GWO with WOA for global numerical optimization and solving pressure vessel design

Metaheuristic Algorithms and Their Applications in Different Fields

Characterization of abnormalities in breast cancer images using nature‐inspired metaheuristic optimized convolutional neural networks model

Contact Info

Product

Resources

About