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A hybrid modified step whale optimization algorithm with Tabu Search for data clustering

JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES 2022年第3期34卷 832-839页

作者： Ghany, Kareem Kamal A. AbdelAziz, Amr Mohamed Soliman, Taysir Hassan A. Sewisy, Adel Abu El-Magd Beni Suef Univ Fac Comp & Artificial Intelligence Bani Suwayf 62111 Egypt Assiut Univ Fac Comp & Informat Assiut 71516 Egypt Saudi Elect Univ Coll Comp & Informat Riyadh 11673 Saudi Arabia

Clustering is a popular data analysis tool that groups similar data objects into disjoint clusters. Clustering algorithms have major drawbacks, such as: sensitivity to initialization and getting trapped in local optima. Swarm Intelligence (SI) have been combined with clustering techniques to enhance their performance. Despite their improvements, some SI methods, such as Particle Swarm optimization (PSO) and its rivals, had their drawbacks, such as low convergence rates and generating low quality solutions. This is due to storing a single best solution about solution space, which can lead swarm members to local optima. whale optimization algorithm (WOA) is a recent SI method, which has been proved as a global optimizer over multiple optimization problems. Unlikely, WOA uses the same concept as PSO, it uses the best solution to reposition swarm members. To overcome this problem, WOA needs to store multiple best solutions about solution space. Tabu Search (TS) is a meta-heuristic method, which uses memory components to explore and exploit search space. In this paper, we propose combining WOA with TS (WOATS) for data clustering. To assess the efficiency of WOATS, it has been applied in data clustering. WOATS uses an objective function inspired by partitional clustering to maintain the quality of clustering solutions. WOATS was tested over multiple real life datasets, which their sizes vary from small to large-sized datasets. WOATS was able to find centers with high quality in a small number of iterations regardless of the size of the dataset, which clarifies its ability to cover the solution space efficiently. The generated clusters were evaluated according to different outlier criteria: Davies-Bouldin Index and Dunn Index. According to these criteria, WOATS presented its superiority over multiple recent original and hybrid SI methods for different datasets. Results obtained by WOATS were better than the results of the other methods with big differences regarding to the

关键词： Data clustering Computational intelligence Data mining Swarm intelligence Tabu Search whale optimization algorithm

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whale optimization algorithm Based on Periodic Weight and Lévy Flight Disturbance Strategy

World Scientific Research Journal

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World Scientific Research Journal 2021年第9期7卷 129-140页

作者： Gang Yang

In view of the shortcomings of the standard whale optimization algorithm in solving high‐dimensional function optimization problems, such as slow convergence speed and low accuracy, this paper proposes an improved whale optimization algorithm which named ＂WLWOA＂ by using three strategies: piecewise update of convergence factor, introduction of periodic adaptive inertia weight and Lévy flight stagnation disturbance. The piecewise convergence factor is used to adjust the convergence speed of the algorithm. The adaptive inertia weight and Lévy flight stagnation disturbance strategy focus on optimizing the specific way of whale individual position update, so as to improve the standard WOA algorithm from multiple angles. The simulation test on 10 classical test functions is carried out. The results show that: compared with the standard whale algorithm, the improved whale algorithm has the characteristics of fast convergence speed, good stability and high solution accuracy, and the solution performance has been comprehensively and greatly improved, which proves the effectiveness of the three improved strategies.

关键词： whale optimization algorithm Piecewise convergence factor Periodic weight Lévy flight Function optimization

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A Modified whale optimization algorithm for Enhancing the Features Selection Process in Machine Learning 29

A Modified Whale Optimization Algorithm for Enhancing the Fe...

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29th Telecommunications Forum (TELFOR)

作者： Syed, Ezaz Uddin Masood, Mohsin Fouad, Mohamed Mostafa Glesk, Ivan AGES Consultants Ltd Elect Engineer Peshawar Pakistan Abasyn Univ Pakistan Dept Comp & Technol Peshawar Pakistan Arab Acad Sci Technol Maritime Transport Cairo Egypt Elect & Elect Dept Glasgow Lanark Scotland

ISBN: (纸本)9781665425858

In recent years, when there is an abundance of large datasets in various fields, the importance of feature selection problem has become critical for researchers. The real world applications rely on large datasets, which implies that datasets have hundreds of instances and attributes. Finding a better way of optimum feature selection could significantly improve the machine learning predictions. Recently, metaheuristics have gained momentous popularity for solving feature selection problem. whale optimization algorithm has gained significant attention by the researcher community searching to solve the feature selection problem. However, the exploration problem in whale optimization algorithm still exists and remains to be researched as various parameters within the whale algorithm have been ignored. This paper proposes a new and improved version of the whale algorithm entitled Modified whale optimization algorithm (MWOA) that hybrid with the machine learning models such as logistic regression, decision tree, random forest, K-nearest neighbor, support vector machine, naive Bayes model. To test this new approach and the performance, the breast cancer dataset was used for MWOA evaluation. The test results revealed the superiority of this model when compared to the results obtained by machine learning models.

关键词： Artificial intelligence dimension reduction feature selection problem machine learning Models whale optimization algorithm

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A Memetic Fuzzy whale optimization algorithm for Data Clustering

A Memetic Fuzzy Whale Optimization Algorithm for Data Cluste...

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IEEE Congress on Evolutionary Computation (IEEE CEC)

作者： Wu, Ze-Xue Huang, Ko-Wei Chen, Jui-Le Yang, Chu-Sing Natl Kaohsiung Univ Sci & Technol Dept Elect Engn Kaohsiung Taiwan Tajen Univ Dept Comp Sci & Entertainment Technol Pingtung Taiwan Natl Cheng Kung Univ Dept Elect Engn Inst Comp & Commun Engn Tainan Taiwan

ISBN: (纸本)9781728121536

Data clustering is a well-known data analysis methodology in the optimization community that can be formulated as a Np-hard problem. The main concept of the clustering procedure is to identify and assign data objects into sensible disjointed groups based on similarity measures, such as the Euclidean distance. In this case, data objects within a group are similar to each other with regard to the similarity measure, and each group is dissimilar to each other. Fuzzy clustering is also a popular and powerful research method with many applications, such as medical imaging processing with real words. In the fuzzy clustering research field, the fuzzy c-means (FCM) algorithm is one of the most important fuzzy clustering approaches for efficient and easy implementation;however, the FCM algorithm has a weakness in which it can easily become trapped in local optima. Thus, some metaheuristic algorithms, such as genetic algorithm (GA) and particle swarm optimization (PSO), can be combined with FCM to solve the clustering problem. Recently, a novel metaheuristic algorithm inspired by the social behavior of humpback whales, called the whale optimization algorithm (WOA), has been proposed. In addition, the WOA has obtained more efficient solutions than some popular metaheuristic algorithms, such as the gene GA and PSO. In this paper, a fuzzy clustering algorithm that called the memetic fuzzy whale optimization (MFWO) algorithm, is proposed to solve the data clustering problem. The performance of the proposed algorithm is evaluated using eight real-world UCI benchmarks, and the FCM and fuzzy PSO algorithms are compared. The experimental results show that the proposed MFWO algorithm is more efficient than the compared algorithms.

关键词： Clustering Fuzzy C-means whale optimization algorithm Meta-Heuristic algorithm

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Mobile Robot Path Planning Based Upon Updated whale optimization algorithm 10

Mobile Robot Path Planning Based Upon Updated Whale Optimiza...

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10th International Conference on Cloud Computing, Data Science and Engineering (Confluence)

作者： Chhillar, Ankit Choudhary, Ankur Amity Univ Comp Sci & Engn Noida Uttar Pradesh India Sharda Univ SET Comp Sci & Engn Greater Noida India

ISBN: (纸本)9781728127910

Automated navigation is an important feature of any mobile robot and is a challenge for a robot to plan the path in an unknown environment. We can categorize the path planning strategy of robots in two methods, first is the Classical Methods and second is Heuristic Methods. In this paper, a modified whale optimization algorithm is proposed that ensures an optimal collision-free path. In whale optimization algorithm (WOA) the fitness of any whale will be calculated by taking in consideration the target location and the obstacles in the search space. Many simulations were run in different search spaces to find the optimal path at the end of the iterations when the individual reaches the final destination (target location). The simulation and results show, the proposed modified WOA is feasible for mobile robot path selection problems.

关键词： whale optimization algorithm Path Planning

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RETRACTED: A hybrid whale optimization algorithm based on local search strategy for the permutation flow shop scheduling problem (Retracted article. See vol. 128, pg. 567, 2022)

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2018年 85卷 129-145页

作者： Abdel-Basset, Mohamed Manogaran, Gunasekaran El-Shahat, Doaa Mirjalili, Seyedali Zagazig Univ Fac Comp & Informat Zagazig Sharqiyah Egypt Univ Calif Davis Davis CA 95616 USA Griffith Univ Sch Informat & Commun Technol Nathan Campus Brisbane Qld 4111 Australia

The flow shop scheduling problem is one of the most important types of scheduling with a large number of real-world applications. In this paper, we propose a new algorithm that integrates the whale optimization algorithm (WOA) with a local search strategy for tackling the permutation flow shop scheduling problem. The Largest Rank Value (LRV) requires the algorithm to deal with the discrete search space of the problem. The diversity of candidate schedules is improved using a swap mutation operation as well. In addition to the insert-reversed block operation is adopted to escape from the local optima. The proposed hybrid whale algorithm (HWA) is incorporated with Nawaz-Enscore-Ham (NEH) to improve the performance of the algorithm. It is observed that HWA gives competitive results compared to the existing algorithms. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Flow shop scheduling Makespan whale optimization algorithm Hybrid algorithm Local search

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Link Prediction Based on whale optimization algorithm

Link Prediction Based on Whale Optimization Algorithm

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International Conference on New Trends in Computing Sciences (ICTCS)

作者： Barham, Reham Aljarah, Ibrahim Univ Jordan King Abdullah II Sch Informat Technol Amman Jordan

ISBN: (纸本)9781538605271

Link prediction problem has a wide variety of useful applications in diverse fields. For example, in bio-informatics, such as protein-protein interaction metabolic and diseases-gene networks, there are links between nodes, which indicate they have an interaction relationship. This paper presents a possible solution to the well known link prediction problem using a whale optimization algorithm (WOA), which is considered as one of the recent optimization algorithms. The link prediction problem is formulated as an optimization problem to predict the links in any type of the networks. Experimental results of the proposed algorithm (WOA-LP) on a number of real networks are good evidence that the proposed approach can enhance the link prediction accuracy.

关键词： algorithm Heuristic Link Prediction optimization Social Network Analysis Association whale optimization algorithm WOA

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A cross entropy and whale optimization algorithm based image segmentation for aerial images

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International Journal of Information Technology (Singapore) 2024年 1-15页

作者： Ahmed, Saifuddin Biswas, Anupam Department of Computer Science and Engineering National Institute of Technology Cachar Silchar 788010 India

There are several image segmentation techniques for image processing such as, thresholding, region based, edge based, watershed etc. However, the multilevel thresholding based image segmentation is the most widely used approach. This approach mostly suffers high computational complexity with the increasing number of threshold levels. The researchers have been using optimization techniques to resolve this issue. This paper presented a multilevel thresholding technique for segmenting aerial images using whale optimization algorithm (WOA). It uses Minimum Cross Entropy (MCE) as an objective function for selecting a set of optimum threshold values. The presented method is evaluated by using a set of standard aerial images. The evaluated performance of the proposed technique is compared with Bacterial Foraging optimization (BFO), Moth-flame Flame optimization (MFO) and Particle Swarm optimization (PSO). The overall performance and the quality of segmented images are evaluated based on the following parameters, Average Mean Structural Similarity Index (MSSIM), Average Feature Similarity Index (FSIM), Average Peak Signal to Noise Ratio (PSNR), average mean objective functions values and average CPU rum time values. In terms of quality of the segmented images based on MSSIM, FSIM and PSNR values the presented method performs better than other methods and we found the average CPU run time also to a considerable range. © Bharati Vidyapeeth's Institute of Computer Applications and Management 2024.

关键词： Cross entropy Image segmentation Multilevel thresholding optimization technique whale optimization algorithm

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TRIAD: whale optimization algorithm for 5G-IoT Resource Allocation Decision in Edge Computing 16

TRIAD: Whale Optimization Algorithm for 5G-IoT Resource Allo...

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16th Iberian Conference on Information Systems and Technologies (CISTI)

作者： Lieira, Douglas Dias Quessada, Matheus Sanches Cristiani, Andre Luis Immich, Roger Meneguette, Rodolfo Ipolito Univ Estadual Paulista UNESP DCCE Sao Jose Do Rio Preto SP Brazil Inst Fed Sao Paulo IFSP Catanduva Sao Jose Do Rio Preto SP Brazil Univ Fed Sao Carlos UFSCar Dept Comp Sao Carlos SP Brazil Univ Fed Rio Grande do Norte UFRN Inst Metropole Digital Natal RN Brazil Univ Sao Paulo Dept Sistemas Comp ICMC Sao Carlos SP Brazil

ISBN: (纸本)9789895465910

The massive growth in the number of 5G-IoT devices circulating in the world has increased the demand for computing resources in recent years. That way, it is necessary to search for the development of new solutions or improvements to existing ones. Edge computing is one of the solutions that have been used to improve the care of these types of devices. In this work, we proposed a mechanism that uses the whale optimization algorithm for 5G-IoT resource allocation decision in edge computing (TRIAD). The TRIAD was compared with the Greedy and Reliable techniques, available in the literature. The results show that the proposed algorithm had excellent efficiency in the service of the devices, in addition to denying fewer requests and blocking fewer devices during the search. The TRIAD, in some situations of the simulation, served approximately 265% more services, denied 56% less requests and blocked 65% less services.

关键词： edge computing internet of things meta-heuristic resource allocation whale optimization algorithm

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Improving Energy Usage in Wireless Sensor Networks by whale optimization algorithm 29

Improving Energy Usage in Wireless Sensor Networks by Whale ...

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29th Telecommunications Forum (TELFOR)

作者： Strumberger, Ivana Bezdan, Timea Ivanovic, Milica Jovanovic, Luka Singidunum Univ Informat & Comp Belgrade Serbia Singidunum Univ Tech Fac Belgrade Serbia

ISBN: (纸本)9781665425858

The most important challenge in wireless sensor networks is the energy saving by reducing the energy consumption of the sensor nodes. If the sensor node's energy is depleted, it could affect the connectivity of the network as a whole. The available energy of sensor nodes is therefore the most valuable resource, and optimization can enhance the lifetime of the network. The optimization significantly depends of the energy model of the MAC layer. This paper proposes a whale optimization algorithm for optimizing the transmitted power model, with a goal to minimize the energy consumption per bit. The results of the conducted experiments indicate that the proposed model obtains better performances than the simple model before application of the algorithm, therefore affecting and prolonging the overall network lifetime.

关键词： wireless sensor networks optimization energy usage swarm intelligence metaheuristics whale optimization algorithm

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