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An enhanced whale optimization algorithm for large scale optimization problems

KNOWLEDGE-BASED SYSTEMS 2021年 233卷 107543-107543页

作者： Chakraborty, Sanjoy Saha, Apu Kumar Chakraborty, Ratul Saha, Moumita Natl Inst Technol Agartala Dept Comp Sci & Engn Agartala India Iswar Chandra Vidyasagar Coll Dept Comp Sci & Engn Belonia Tripura India Natl Inst Technol Agartala Dept Math Agartala India Maharaja Bir Bikram Coll Dept Stat Agartala Tripura India Directorate Informat Technol Agartala Tripura India

whale optimization algorithm was developed based on the prey-catching characteristics of the humpback whales. Due to its simple structure and efficiency, the researchers employed the algorithm to address numerous disciplines' numerous problems. The profound analysis of the whale optimization algorithm discloses that the algorithm suffers from low exploration ability, lesser accuracy, and early convergence. Additionally, performance of the whale optimization algorithm and most of its variants in high-dimensional optimization problems is not satisfactory. This study proposes a new variant with several modifications to the basic whale optimization algorithm to solve high-dimensional problems. A unique selection parameter is introduced in the whale optimization algorithm to balance the algorithm's global and local search phase. The co-efficient vectors A and C are modified and used effectively to explore and exploit the search region better. In the exploration phase, random movement is allowed to reduce the computational burden of the algorithm. An inertia weight is introduced in the exploitation phase for exhaustive search nearby the best solution. The proposed algorithm evaluates twenty-five benchmark functions using dimensions 100, 500, 1000, and 2000 and compared the results with the whale optimization algorithm and its variants. The estimated outcomes are also compared with seven basic metaheuristic algorithms. Finally, statistical analysis, complexity analysis, and convergence analysis are performed to establish the algorithm's efficacy. All the test result suggests better performance of the proposed algorithm on higher-dimensional problems. (c) 2021 Elsevier B.V. All rights reserved.

关键词： whale optimization algorithm High dimensional problem Benchmark function Friedman's test Nemenyi multiple comparison tests Boxplot

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An urban short-term traffic flow prediction model based on wavelet neural network with improved whale optimization algorithm

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SUSTAINABLE CITIES AND SOCIETY 2021年 69卷 102858-102858页

作者： Du, Wangdi Zhang, Qingyong Chen, Yuepeng Ye, Ziliu Wuhan Univ Technol Sch Automat Wuhan Hubei Peoples R China Wuhan Univ Technol Wuhan Hubei Peoples R China

To solve the problem of urban traffic congestion is a major challenge to modern society. Short-term urban traffic flow prediction is the key to realizing traffic control and vehicle guidance on the relief of traffic congestion to make a better city experience. Short-term urban traffic flow is characterized by a high level of time-variation, nonlinearity and randomness. To tackle this problem and to improve the performance of short-term traffic flow prediction, the author of this paper comes up with an improved whale optimization algorithm (IWOA) and tries to introduce it into the wavelet neural network (WNN) so as to optimize its initial weights and wavelet factors via iteration and to establish a short-term traffic flow prediction model based on Wavelet Neural Network with Improved whale optimization algorithm (IWOA-WNN). This model appears to predict traffic flow in a more effective manner and it can remedy the defects of wavelet neural network which usually leads to low prediction accuracy and slow response. The experimental results show that compared with the pure WNN model and WOAWNN model, the IWOA-WNN model has turned out to be a great improvement in terms of absolute error and percentage of absolute error.

关键词： Short-term traffic flow prediction whale optimization algorithm Wavelet neural network

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optimization of bilateral filter parameters using a whale optimization algorithm

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RESEARCH IN MATHEMATICS 2022年第1期9卷 Article: 2140863页

作者： Nabahat, Mehrdad Khiyabani, Farzin Modarres Navmipour, Nima Islamic Azad Univ Dept Math Tabriz Branch Tabriz Iran Islamic Azad Univ Dept Comp Engn Tabriz Branch Tabriz Iran

Noise removal and restoration are important topics in image processing. The filter-based denoising techniques can effectively reduce the image noise but sometimes lose image quality and information, such as blurring the edges of the image. In this paper, at first, a denoising filter is proposed by combining the whale optimization algorithm (WOA) and bilateral filter. Then, a WOA-based Richardson-Lucy (R-L) algorithm is applied to restore the image. The bilateral filter performance is largely dependent on the proper parameter selection. The bilateral filter parameters are optimized by using the weighted sum of the peak signal-to-noise ratio (PSNR) and the structural similarity index measure (SSIM) as a fitness function of the WOA algorithm to design the proposed filter. For the restoration purpose, the point spread function (PSF) of the Richardson-Lucy (R-L) algorithm is optimized by using the weighted sum of the PSNR and the second derivative like measure enhancement (SDME) as a fitness function of the WOA. The performance of the proposed denoising technique is compared with classical image denoising filters, PSO-based bilateral filters on various images and the performance of the suggested restoration technique are also compared with the blind deconvolution technique (BD) and PSO-based restoration method (BDPSO) on denoised images, with the experimental findings demonstrating that the suggested method outperforms the others. The proposed technique in the current paper can be implemented in different image applications such as medical and satellite images, etc.

关键词： Image denoising image restoration whale optimization algorithm particle swarm optimization bilateral filter Richardson-Lucy algorithm blind deconvolution technique

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COVID-19 X-ray image segmentation by modified whale optimization algorithm with population reduction

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COMPUTERS IN BIOLOGY AND MEDICINE 2021年 139卷 104984-104984页

作者： Chakraborty, Sanjoy Saha, Apu Kumar Nama, Sukanta Debnath, Sudhan Natl Inst Technol Dept Comp Sci & Engn Agartala Tripura India Iswar Chandra Vidyasagar Coll Dept Comp Sci & Engn Belonia Tripura India Natl Inst Technol Dept Math Agartala Tripura India Maharaja Bir Bikram Univ Dept Appl Math Agartala Tripura India Maharaja Bir Bikram Univ Dept Chem Agartala Tripura India

Coronavirus disease 2019 (COVID-19) has caused a massive disaster in every human life field, including health, education, economics, and tourism, over the last year and a half. Rapid interpretation of COVID-19 patients' X-ray images is critical for diagnosis and, consequently, treatment of the disease. The major goal of this research is to develop a computational tool that can quickly and accurately determine the severity of an illness using COVID-19 chest X-ray pictures and improve the degree of diagnosis using a modified whale optimization method (WOA). To improve the WOA, a random initialization of the population is integrated during the global search phase. The parameters, coefficient vector (A) and constant value (b), are changed so that the algorithm can explore in the early stages while also exploiting the search space extensively in the latter stages. The efficiency of the proposed modified whale optimization algorithm with population reduction (mWOAPR) method is assessed by using it to segment six benchmark images using multilevel thresholding approach and Kapur's entropy-based fitness function calculated from the 2D histogram of greyscale images. By gathering three distinct COVID-19 chest X-ray images, the projected algorithm (mWOAPR) is utilized to segment the COVID-19 chest X-ray images. In both benchmark pictures and COVID-19 chest X-ray images, comparisons of the evaluated findings with basic and modified forms of metaheuristic algorithms supported the suggested mWOAPR's improved performance.

关键词： whale optimization algorithm Image segmentation Multilevel thresholding Kapur's entropy COVID-19 chest X-ray image

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A novel whale optimization algorithm integrated with Nelder-Mead simplex for multi-objective optimization problems

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KNOWLEDGE-BASED SYSTEMS 2021年 212卷 106619-106619页

作者： Abdel-Basset, Mohamed Mohamed, Reda Mirjalili, Seyedali Zagazig Univ Fac Comp & Informat Zagazig 44519 Egypt Torrens Univ Australia Ctr Artificial Intelligence Res & Optimisat Brisbane Qld 4006 Australia Yonsei Univ YFL Yonsei Frontier Lab Seoul South Korea King Abdulaziz Univ Jeddah Saudi Arabia

Recently, several meta-heuristics and evolutionary algorithms have been proposed for tackling optimization problems. Such methods tend to suffer from degraded performance when solving multi objective optimization problems due to addressing the conflicting goals of finding accurate estimation of Pareto optimal solutions and increasing their distribution across all objectives. In this paper, the whale optimization algorithm (WOA) is improved and extended to solve such multi-objective optimization problems with the purpose of alleviating these drawbacks. The improvements include: (1) modifying the distance control factor of the standard WOA to contain values generated dynamically instead of a fixed one, (2) the trade-off between moving toward the opposite of the best solution and its original values based on a certain probability to prevent stuck into local minima, and (3) accelerating the convergence and coverage using Nelder-Mead method and the Pareto Archived Evolution Strategy (PAES). The proposed algorithm is tested on three benchmark multi-objective test functions (DTLZ, CEC 2009, and GLT), including 25 test functions, to verify its effectiveness by comparing with nine robust multi-objective algorithms. The experiments demonstrate the superiority of the proposed algorithm compared to some of the existing multi-objective algorithms in the literature. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Multi-objective optimization Nelder-Mead method whale optimization algorithm Dynamic distance Opposition based learning optimization algorithm Artificial Intelligence Swarm Intelligence WOA

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WOA-TLBO: whale optimization algorithm with Teaching-learning-based optimization for global optimization and facial emotion recognition

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APPLIED SOFT COMPUTING 2021年 110卷 107623-107623页

作者： Lakshmi, A. Vijaya Mohanaiah, P. JNTUA Dept ECE Anantapur AP India NBKR Inst Sci & Technol Dept ECE Vijayanagar India

The whale optimization algorithm (WOA) is a recently developed algorithm that is based on the chasing mechanism of humpback whales. Benefiting from the unique structure, WOA has virtuous global search capability. One of the drawbacks of this algorithm is the slow convergence rate that limits its real-world application. In resolving complicated global optimization problems, without any exertion for adequate fine-tuning preliminary constraints, Teaching-learning-based optimization (TLBO) is smooth to plunge into local optimal, but it has a fast convergence speed. By given the features of WOA and TLBO, an active hybrid WOA-TLBO algorithm is proposed for resolving optimization difficulties. To explore the enactment of the proposed WOA-TLBO algorithm, several experimentations are accompanied by regular benchmark test functions and compared with six other algorithms. The investigational outcomes indicate the more magnificent concert of the proposed WOA-TLBO algorithm for the benchmark function results. The proposed method has also been applied to the Facial Emotion Recognition (FER) functional problem. FER is the thought-provoking investigation zone that empowers us to classify the expression of the human face in everyday life. Centered on the portions' actions in the human face, the maximum of the standard approaches fail to distinguish the expressions precisely as the expressions. In this paper, we have proposed FER's productive process using WOA-TLBO based MultiSVNN (Multi-Support Vector Neural Network). Investigational outcomes deliver an indication of the virtuous enactment of the proposed technique resolutions in terms of accurateness. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Meta-heuristic whale optimization algorithm WOA-TLBO Facial emotion recognition Population-based algorithm Teaching-learning-based optimization

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A novel enhanced whale optimization algorithm for global optimization

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COMPUTERS & INDUSTRIAL ENGINEERING 2021年 153卷 107086-107086页

作者： Chakraborty, Sanjoy Saha, Apu Kumar Sharma, Sushmita Mirjalili, Seyedali Chakraborty, Ratul Iswar Chandra Vidysagar Coll Dept Comp Sci & Engn Belonia Tripura India Natl Inst Technol Agartala Dept Comp Sci & Engn Agartala Tripura India Natl Inst Technol Agartala Dept Math Agartala Tripura India Torrens Univ Australia Ctr Artificial Intelligence Res & Optimisat Brisbane Qld 4006 Australia Yonsei Univ YFL Yonsei Frontier Lab Seoul South Korea Maharaja Bir Bikram Coll Dept Stat Agartala Tripura India King Abdulaziz Univ Jeddah Saudi Arabia

One of the main issues with heuristics and meta-heuristics is the local optima stagnation phenomena. It is often called premature convergence, which refers to the assumption of a locally optimal solution as the best solution for an optimization problem and failure in finding the global optimum. whale optimization algorithm (WOA) has demonstrated its merits in the optimization area. Though WOA is an effective algorithm, it may suffer from a low exploration of the search space. In this work, an enhanced WOA (WOAmM) is proposed. The mutualism phase from Symbiotic Organisms Search (SOS) is modified and integrated with WOA to alleviate premature convergence's inherent drawback. The addition of a modified mutualism phase with WOA makes the algorithm a balanced one to explore search space more extensively and avoid wasting computational resources in excessive exploitation. The proposed WOAmM method is tested on 36 benchmark functions and IEEE CEC 2019 function suite. It is compared with a wide range of algorithms, including improved WOAs and other meta-heuristics. Statistical analyses and convergence analysis are performed as well to examine its effectiveness and convergence speed. In addition, six real-world engineering optimization problems are solved by the proposed method. The performance is compared with a wide range of existing algorithms to inspect the problem-solving capability of WOAmM. The results demonstrate the performance improvement of WOAmM and its superiority over different algorithms.

关键词： whale optimization algorithm Mutualism phase Meta-heuristic Benchmark function IEEE CEC 2019 functions Real-world problem

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Enhancing whale optimization algorithm with Levy Flight for coverage optimization in wireless sensor networks

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COMPUTERS & ELECTRICAL ENGINEERING 2021年 94卷 107359-107359页

作者： Deepa, R. Venkataraman, Revathi SRM Inst Sci & Technol Dept Comp Sci & Engn Kattankulathur Tamil Nadu India

Coverage optimization is one of the most essential pre-requisites in Wireless Sensor Networks (WSNs) which plays a significant and impactful role in the field of environmental monitoring, surveillance, socio-economic Cyber-networkings, etc. The whale optimization algorithm (WOA) is a swarm intelligence based Search-algorithm while browsing for an optimal solution, but, it suffers from the poor & inconsistent exploration problem and that causes trapping of local optima in randomly deployed nodes that fail to guarantee network coverage. To resolve the issue, an innovative study has been researched which presents an embedded coverage optimization WSN and is based on Levy Flight mechanism with WOA (LWOA) .This updates the current search of location for positioning the sensors in the field. This mechanism can enhance and balance the exploration ability of WOA, which allows trapping of the local optima. This thichnically enhanced and updated proposal LWOA is validated by 25 benchmark optimization functions and is compared with existing Particle Swarm optimization and WOA. From the experimental results, it can be construed and proved that the performance of Levy WOA (LWOA) has significantly improved the global search capacity and increase the efficiency of convergence, which immensely enhances the efficacy of coverage of nodes inturn amplifying the overall performance of the network. Finally, by using the K-Nearest Neighbour KNN(centroid) approach nearly 33% of nodes were optimized.

关键词： Coverage optimization whale optimization algorithm Levy Flights K-Nearest Neighbor (centroid)

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Hybrid whale optimization algorithm with gathering strategies for high-dimensional problems

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EXPERT SYSTEMS WITH APPLICATIONS 2021年 179卷 115032-115032页

作者： Zhang, Xinming Wen, Shaochen Henan Normal Univ Coll Comp & Informat Engn Xinxiang Henan Peoples R China Engn Lab Intelligence Business & Internet Things Xinxiang Henan Peoples R China

In order to solve the problems, such as insufficient search ability and low search efficiency, of whale optimization algorithm (WOA) in solving high-dimensional problems, a novel Hybrid WOA with Gathering strategies (HWOAG) is proposed in this paper. Firstly, an individual-based updating way is used in HWOAG instead of the dimension-based updating one of WOA to reduce the computational complexity and to be more suitable for highdimensional problems. Secondly, a random opposition learning strategy is embedded into the individual-based WOA to form an opposition learning WOA (OWOA), and Grey Wolf Optimizer (GWO) is integrated into OWOA to form an OWOA with GWO (OWOAG) so as to improve the global search ability of WOA. Finally, two standalone OWOAGs are formulated to balance exploration and exploitation better. The two OWOAGs adopt strategies such as switching parameter tuning, random differential disturbance and global-best spiral operator to get stronger search ability. A lot of experimental results on high-dimensional (i.e. 1000-, 2000-, 4000- and 8000dimensional) benchmark functions and clustering datasets for Fuzzy C-Means (FCM) optimization show that HWOAG has stronger search ability and higher search efficiency than WOA and quite a few state-of-the-art algorithms and that all the strategies gathered to WOA are effective. The source codes of the proposed algorithm HWOAG are available at https://***/kangzhai/HWOAG.

关键词： optimization algorithm whale optimization algorithm Grey Wolf optimizer High-dimensional problems Fuzzy C-means

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A novel data clustering approach based on whale optimization algorithm

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EXPERT SYSTEMS 2021年第3期38卷 e12657-e12657页

作者： Singh, Tribhuvan Siksha O Anusandhan Deemed Univ Dept Comp Sci & Engn Bhubaneswar Odisha India

Data clustering is an important technique of data mining in which the objective is to partition N data objects into K clusters that minimize the sum of intra-cluster distances between each data object to its nearest centroid. This is an optimization problem, and various optimization algorithms have been suggested for capturing the position vectors of optimal centroids. However, in these approaches, the problem of local entrapment is very common due to weak exploration mechanism. In this paper, a novel approach based on a whale optimization algorithm (WOA) is suggested for data clustering. The performance of the suggested approach is validated using 14 benchmark datasets of the UCI machine learning repository. The experimental results and various statistical tests have justified the efficacy of the suggested approach.

关键词： data clustering data mining k‐ means optimization problems whale optimization algorithm

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