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Relevant bibliographies by topics / Chaotic whale metaheuristic optimization

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Academic literature on the topic 'Chaotic whale metaheuristic optimization'

Author: Grafiati

Published: 4 June 2025

Last updated: 15 July 2025

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Journal articles on the topic "Chaotic whale metaheuristic optimization"

1

Hidayat, Nur Wahyu, Purwanto, and Fikri Budiman. "Whale Optimization Algorithm Bat Chaotic Map Multi Frekuensi for Finding Optimum Value." Journal of Applied Intelligent System 5, no. 2 (2021): 80–90. http://dx.doi.org/10.33633/jais.v5i2.4432.

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Optimization is one of the most interesting things in life. Metaheuristic is a method of optimization that tries to balance randomization and local search. Whale Optimization Algorithm (WOA) is a metaheuristic method that is inspired by the hunting behavior of humpback whales. WOA is very competitive compared to other metaheuristic algorithms, but WOA is easily trapped in a local optimum due to the use of encircling mechanism in its search space resulting in low performance. In this research, the WOA algorithm is combined with the BAT chaotic map multi-frequency (BCM) algorithm. This method is done by inserting the BCM algorithm in the WOA search phase. The experiment was carried out with 23 benchmarks test functions which were run 30 times continuously with the help of Matlab R2012a. The experimental results show that the WOABCM algorithm is able to outperform the WOA and WOABAT algorithms in most of the benchmark test functions. The increase of performance in the average of optimum value of WOABCM when compared to WOA is 2.27x10 ^ 3.

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Moqbel, Mohammed Ali Mohammed, Talal Ahmed Ali Ali, Zhu Xiao, and Amani Ali Ahmed Ali. "Design of efficient generalized digital fractional order differentiators using an improved whale optimization algorithm." PeerJ Computer Science 11 (July 1, 2025): e2971. https://doi.org/10.7717/peerj-cs.2971.

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This article proposes a new design and realization method for generalized digital fractional-order differentiator (GFOD) based on a composite structure of infinite impulse response (IIR) subfilters. The proposed method utilizes an improved whale optimization algorithm (IWOA) to compute the optimal coefficients of IIR subfilters of the realization structure. IWOA is developed by incorporating a piecewise linear chaotic mapping (PWLCM) and an adaptive inertia weight based on the hyperbolic tangent function (AIWHT) into the framework of original whale optimization algorithm (WOA). Simulation experiments are conducted to compare the performance of our method with that of well-known techniques, real-coded genetic algorithm (RCGA), particle swarm optimization (PSO), and original WOA. The results show that the new metaheuristic is superior to the other metaheuristics in terms of attaining the most accurate GFOD approximation. Moreover, the proposed IIR-based GFOD is compared with state-of-the-art GFOD, and observed to save about 50% of implementation complexity. Therefore, our method can be utilized in real-world digital signal processing applications.

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Sridhar, R., and Guruprasad N. "Energy efficient chaotic whale optimization technique for data gathering in wireless sensor network." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 4 (2020): 4176–88. https://doi.org/10.11591/ijece.v10i4.pp4176-4188.

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A Wireless Sensor Network includes the distributed sensor nodes using limited energy, to monitor the physical environments and forward to the sink node. Energy is the major resource in WSN for increasing the network lifetime. Several works have been done in this field but the energy efficient data gathering is still not improved. In order to amend the data gathering with minimal energy consumption, an efficient technique called chaotic whale metaheuristic energy optimized data gathering (CWMEODG) is introduced. The mathematical model called Chaotic tent map is applied to the parameters used in the CWMEODG technique for finding the global optimum solution and fast convergence rate. Simulation of the proposed CWMEODG technique is performed with different parameters such as energy consumption, data packet delivery ratio, data packet loss ratio and delay with deference to dedicated quantity of sensor nodes and number of packets. The consequences discussion shows that the CWMEODG technique progresses the data gathering and network lifetime with minimum delay as well as packet loss than the state-of-the-art methods.

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Xiaoming Shi, Xiaoming Shi, Kun Li Xiaoming Shi, and Liwei Jia Kun Li. "Improved Whale Optimization Algorithm via the Inertia Weight Method Based on the Cosine Function." 網際網路技術學刊 23, no. 7 (2022): 1623–32. http://dx.doi.org/10.53106/160792642022122307016.

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<p>Whale Optimization Algorithm (WOA) is a new meta-heuristic algorithm proposed by Australian scholar Mirjalili Seyedali in 2016 based on the feeding behavior of whales in the ocean. In response to the disadvantages of this algorithm, such as low solution accuracy, slow convergence speed and easy to fall into local optimum, an improved Whale Optimization Algorithm (IWOA) is proposed in this paper. We introduce chaotic mapping in the initialization of the algorithm to keep the whale population with diversity; introduce adaptive inertia weights in the spiral position update of humpback whales to prevent the algorithm from falling into local optimum; and introduce Levy flight in the random search for food of humpback whales to improve the global search ability of the algorithm. In the simulation experiments, we compare the algorithm of this paper with other metaheuristic algorithms in seven classical benchmark test functions, and the numerical results of four indexes, minimum, maximum, mean and standard deviation, in different dimensions, illustrate that the algorithm of this paper has better performance results.</p> <p>&nbsp;</p>

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Ridho, Akhmad, and Alamsyah Alamsyah. "Chaotic Whale Optimization Algorithm in Hyperparameter Selection in Convolutional Neural Network Algorithm." Journal of Advances in Information Systems and Technology 4, no. 2 (2023): 156–69. http://dx.doi.org/10.15294/jaist.v4i2.60595.

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In several previous studies, metaheuristic methods were used to search for CNN hyperparameters. However, this research only focuses on searching for CNN hyperparameters in the type of network architecture, network structure, and initializing network weights. Therefore, in this article, we only focus on searching for CNN hyperparameters with network architecture type, and network structure with additional regularization. In this article, the CNN hyperparameter search with regularization uses CWOA on the MNIST and FashionMNIST datasets. Each dataset consists of 60,000 training data and 10,000 testing data. Then during the research, the training data was only taken 50% of the total data, then the data was divided again by 10% for data validation and the rest for training data. The results of the research on the MNIST CWOA dataset have an error value of 0.023 and an accuracy of 99.63. Then the FashionMNIST CWOA dataset has an error value of 0.23 and an accuracy of 91.36.

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6

R., Sridhar, and N. Guruprasad. "Energy efficient chaotic whale optimization technique for data gathering in wireless sensor network." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 4 (2020): 4176. http://dx.doi.org/10.11591/ijece.v10i4.pp4176-4188.

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A Wireless Sensor Network includes the distributed sensor nodes using limited energy, to monitor the physical environments and forward to the sink node. Energy is the major resource in WSN for increasing the network lifetime. Several works have been done in this field but the energy efficient data gathering is still not improved. In order to amend the data gathering with minimal energy consumption, an efficient technique called chaotic whale metaheuristic energy optimized data gathering (CWMEODG) is introduced. The mathematical model called Chaotic tent map is applied to the parameters used in the CWMEODG technique for finding the global optimum solution and fast convergence rate. Simulation of the proposed CWMEODG technique is performed with different parameters such as energy consumption, data packet delivery ratio, data packet loss ratio and delay with deference to dedicated quantity of sensor nodes and number of packets. The consequences discussion shows that the CWMEODG technique progresses the data gathering and network lifetime with minimum delay as well as packet loss than the state-of-the-art methods.

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AlRijeb, Mothena Fakhri Shaker, Mohammad Lutfi Othman, Aris Ishak, Mohd Khair Hassan, and Baraa Munqith Albaker. "Whale Optimization Algorithm based on Tent Chaotic Map for Feature Selection in Soft Sensors." Engineering, Technology & Applied Science Research 15, no. 3 (2025): 23537–45. https://doi.org/10.48084/etasr.10965.

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Irrelevant features in data collected from oil refineries affect system performance due to conflicts between normal data and detected faults. Selecting the relevant features from the data leads to better classification results. Optimization algorithms are successfully applied in the feature selection task in many systems. One of the powerful optimization algorithms that is used for feature selection is the Whale Optimization Algorithm (WOA), which is a nature-inspired metaheuristic optimization algorithm that mimics the social behavior of humpback whales. This study presents an improvement to WOA using a tent chaotic map to select the most relevant features and enhance performance. The Tent map mainly applies randomness to generate diversification into the search process and escape from local optima in WOA. The tent map is used for generating the initial population, producing values in control parameters, and updating the position of search agents. The proposed approach combines the tent map with WOA, called TWOA, to enrich population diversity, prevent premature convergence, and speed up convergence. TWOA is applied in a soft sensor with actual data collected from the Salahuddin oil refinery in Iraq. The soft sensor was designed using several stages, including data collection, preprocessing, clustering, feature selection, and classification. The proposed TWOA achieved a higher fault classification result of 99.98% compared to other algorithms.

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Yildirim, Suna, and Bilal Alatas. "Increasing the explainability and success in classification: many-objective classification rule mining based on chaos integrated SPEA2." PeerJ Computer Science 10 (September 6, 2024): e2307. http://dx.doi.org/10.7717/peerj-cs.2307.

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Classification rule mining represents a significant field of machine learning, facilitating informed decision-making through the extraction of meaningful rules from complex data. Many classification methods cannot simultaneously optimize both explainability and different performance metrics at the same time. Metaheuristic optimization-based solutions, inspired by natural phenomena, offer a potential paradigm shift in this field, enabling the development of interpretable and scalable classifiers. In contrast to classical methods, such rule extraction-based solutions are capable of classification by taking multiple purposes into consideration simultaneously. To the best of our knowledge, although there are limited studies on metaheuristic based classification, there is not any method that optimize more than three objectives while increasing the explainability and interpretability for classification task. In this study, data sets are treated as the search space and metaheuristics as the many-objective rule discovery strategy and study proposes a metaheuristic many-objective optimization-based rule extraction approach for the first time in the literature. Chaos theory is also integrated to the optimization method for performance increment and the proposed chaotic rule-based SPEA2 algorithm enables the simultaneous optimization of four different success metrics and automatic rule extraction. Another distinctive feature of the proposed algorithm is that, in contrast to classical random search methods, it can mitigate issues such as correlation and poor uniformity between candidate solutions through the use of a chaotic random search mechanism in the exploration and exploitation phases. The efficacy of the proposed method is evaluated using three distinct data sets, and its performance is demonstrated in comparison with other classical machine learning results.

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Almotairi, Sultan, Elsayed Badr, Mustafa Abdul Salam, and Alshimaa Dawood. "Three Chaotic Strategies for Enhancing the Self-Adaptive Harris Hawk Optimization Algorithm for Global Optimization." Mathematics 11, no. 19 (2023): 4181. http://dx.doi.org/10.3390/math11194181.

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Harris Hawk Optimization (HHO) is a well-known nature-inspired metaheuristic model inspired by the distinctive foraging strategy and cooperative behavior of Harris Hawks. As with numerous other algorithms, HHO is susceptible to getting stuck in local optima and has a sluggish convergence rate. Several techniques have been proposed in the literature to improve the performance of metaheuristic algorithms (MAs) and to tackle their limitations. Chaos optimization strategies have been proposed for many years to enhance MAs. There are four distinct categories of Chaos strategies, including chaotic mapped initialization, randomness, iterations, and controlled parameters. This paper introduces SHHOIRC, a novel hybrid algorithm designed to enhance the efficiency of HHO. Self-adaptive Harris Hawk Optimization using three chaotic optimization methods (SHHOIRC) is the proposed algorithm. On 16 well-known benchmark functions, the proposed hybrid algorithm, authentic HHO, and five HHO variants are evaluated. The computational results and statistical analysis demonstrate that SHHOIRC exhibits notable similarities to other previously published algorithms. The proposed algorithm outperformed the other algorithms by 81.25%, compared to 18.75% for the prior algorithms, by obtaining the best average solutions for 13 benchmark functions. Furthermore, the proposed algorithm is tested on a real-life problem, which is the maximum coverage problem of Wireless Sensor Networks (WSNs), and compared with pure HHO, and two well-known algorithms, Grey Wolf Optimization (GWO) and Whale Optimization Algorithm (WOA). For the maximum coverage experiments, the proposed algorithm demonstrated superior performance, surpassing other algorithms by obtaining the best coverage rates of 95.4375% and 97.125% for experiments 1 and 2, respectively.

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Ayumi, Vina, L. M. Rasdi Rere, Mohamad Ivan Fanany, and Aniati Murni Arymurthy. "Random adjustment - based Chaotic Metaheuristic algorithms for image contrast enhancement." Jurnal Ilmu Komputer dan Informasi 10, no. 2 (2017): 67. http://dx.doi.org/10.21609/jiki.v10i2.375.

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Metaheuristic algorithm is a powerful optimization method, in which it can solve problemsby exploring the ordinarily large solution search space of these instances, that are believed tobe hard in general. However, the performances of these algorithms signicantly depend onthe setting of their parameter, while is not easy to set them accurately as well as completelyrelying on the problem's characteristic. To ne-tune the parameters automatically, manymethods have been proposed to address this challenge, including fuzzy logic, chaos, randomadjustment and others. All of these methods for many years have been developed indepen-dently for automatic setting of metaheuristic parameters, and integration of two or more ofthese methods has not yet much conducted. Thus, a method that provides advantage fromcombining chaos and random adjustment is proposed. Some popular metaheuristic algo-rithms are used to test the performance of the proposed method, i.e. simulated annealing,particle swarm optimization, dierential evolution, and harmony search. As a case study ofthis research is contrast enhancement for images of Cameraman, Lena, Boat and Rice. Ingeneral, the simulation results show that the proposed methods are better than the originalmetaheuristic, chaotic metaheuristic, and metaheuristic by random adjustment.

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Book chapters on the topic "Chaotic whale metaheuristic optimization"

1

Okwu, Modestus O., and Lagouge K. Tartibu. "Whale Optimization Algorithm (WOA)." In Metaheuristic Optimization: Nature-Inspired Algorithms Swarm and Computational Intelligence, Theory and Applications. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-61111-8_6.

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Kaveh, A. "Sizing Optimization of Skeletal Structures Using the Enhanced Whale Optimization Algorithm." In Applications of Metaheuristic Optimization Algorithms in Civil Engineering. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-48012-1_4.

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Rana, Nadim, Shafie Abd Latiff, and Shafi’i Muhammad Abdulhamid. "A Metaheuristic Based Virtual Machine Allocation Technique Using Whale Optimization Algorithm in Cloud." In International Conference on Emerging Applications and Technologies for Industry 4.0 (EATI’2020). Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-80216-5_3.

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Pandey, Krishna Kumar, Chandan Banerjee, and Vineet Kumar Tiwari. "Improve PV System with MPPT Technique by Using Chaotic Whale Optimization Algorithm." In Lecture Notes in Electrical Engineering. Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-8554-5_34.

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Mukherjee, Suvabrata, and Provas Kumar Roy. "Load Flow Solution for Radial Distribution Networks Using Chaotic Opposition Based Whale Optimization Algorithm." In Communications in Computer and Information Science. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-48876-4_7.

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Mohanty, Figlu, Suvendu Rup, and Bodhisattva Dash. "An Improved CAD Framework for Digital Mammogram Classification Using Compound Local Binary Pattern and Chaotic Whale Optimization-Based Kernel Extreme Learning Machine." In Artificial Neural Networks and Machine Learning – ICANN 2018. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01421-6_2.

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Qasim, Mohammad, Mohammad Sajid, Ranjit Rajak, and Mohammad Shahid. "Task Scheduling Strategy Using Chaotic Whale Optimization Algorithm in Cloud Computing." In Advances in Computer and Electrical Engineering. IGI Global, 2024. https://doi.org/10.4018/979-8-3693-6834-3.ch002.

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Cloud computing is becoming popular because it can provide cloud consumers with IT services scaled up globally over the internet. These services include platforms, applications, and infrastructure. Moreover, cloud computing can be provided on demand and offered in different pricing packages. To schedule the task optimally in a cloud environment is considered an NP-hard problem, which has become complex with the introduction of variables such as resource dynamicity and on-demand consumer applications. The proposed research introduces a Whale Optimization Algorithm (WOA) incorporating a transfer function (TF) and a tent chaotic map to tackle scheduling challenges in cloud computing. The performance of the proposed chaotic-based whale optimization algorithm (CWOA) is compared to that of well-known metaheuristics methods. The results show that CWOA may significantly reduce the makespan of the task scheduling problem compared to standard Grey Wolf Optimizer (GWO) and BAT algorithms. Furthermore, it converges quickly as the search space grows more prominent, making it suitable for large-scale scheduling issues.

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Abualigah, Laith, Roa’a Abualigah, Abiodun M. Ikotun, et al. "Whale optimization algorithm: analysis and full survey." In Metaheuristic Optimization Algorithms. Elsevier, 2024. http://dx.doi.org/10.1016/b978-0-443-13925-3.00015-7.

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Zitouni, Farouq, and Saad Harous. "An enhanced whale optimization algorithm using the Nelder-Mead algorithm and logistic chaotic map." In Handbook of Whale Optimization Algorithm. Elsevier, 2024. http://dx.doi.org/10.1016/b978-0-32-395365-8.00015-4.

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Ozpinar, Alper, and Emel Seyma Kucukasci. "Use of Chaotic Randomness Numbers." In Intelligent Techniques for Data Analysis in Diverse Settings. IGI Global, 2016. http://dx.doi.org/10.4018/978-1-5225-0075-9.ch010.

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The timeless search for optimizing the demand and supply of any resource is one of the main issues for humanity nearly from the beginning of time. The relevant cost of adding an extra resource reacts by means of more energy requirement, more emissions, interaction with policies and market status makes is even more complicated. Optimization of demand and supply is the key to successfully solve the problem. There are various optimization algorithms in the literature and most of them uses various algorithms of iteration and some degree of randomness to find the optimum solution. Most of the metaheuristic and artificial intelligence algorithms require the randomness where to make a new decision to go forward. So this chapter is about the possible use of chaotic random numbers in the metaheuristic and artificial intelligence algorithms that requires random numbers. The authors only provide the necessary information about the algorithms instead of providing full detailed explanation of the subjects assuming the readers already have theoretical basic information.

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Conference papers on the topic "Chaotic whale metaheuristic optimization"

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Liu, Wei, Wenlv Yan, Tengteng Ren, Tong Li, and Chuang Zhang. "Whale Optimization Algorithm Based on Chaotic Mapping and Dynamic Parameters." In 2024 2nd International Conference on Signal Processing and Intelligent Computing (SPIC). IEEE, 2024. http://dx.doi.org/10.1109/spic62469.2024.10691489.

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Lara-Monta�o, Oscar D., Fernando I. G�mez-Castro, Claudia Guti�rrez-Antonio, and Elena N. Dragoi. "Optimization Of Heat Exchangers Through an Enhanced Metaheuristic Strategy: The Success-Based Optimization Algorithm." In The 35th European Symposium on Computer Aided Process Engineering. PSE Press, 2025. https://doi.org/10.69997/sct.167193.

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The optimization of shell-and-tube heat exchangers (STHEs) is critical for improving energy efficiency, reducing operational costs, and mitigating environmental impacts in industrial applications. This study evaluates the performance of the Success-Based Optimization Algorithm (SBOA), a novel metaheuristic strategy inspired by behavioral patterns in success perception, against seven established algorithms�Cuckoo Search, Differential Evolution (DE), Grey Wolf Optimization (GWO), Jaya Algorithm, Particle Swarm Optimization, Teaching-Learning Based Optimization, and Whale Optimization Algorithm�for minimizing the total annual cost (TAC) of STHE designs. Using the Bell-Delaware method, the optimization framework incorporates eleven decision variables, including geometric and operational parameters, subject to thermo-hydraulic constraints. A penalty function method enforces feasibility by dynamically adjusting constraint weights. Statistical analysis of 30 independent runs reveals that DE achieves the lowest mean TAC (6,090 USD/year) with minimal variability (SD = 22.57), while GWO attains the best median TAC (6,076 USD/year). Although SBOA identifies competitive solutions (minimum TAC = 6,074 USD/year), its high standard deviation (270.66) underscores inconsistency in convergence.

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Zhu, Boxu. "Application of Whale Optimization Algorithm Based on Sine Chaotic Mapping in Optimising Travel Experience." In 2024 IEEE 2nd International Conference on Electrical, Automation and Computer Engineering (ICEACE). IEEE, 2024. https://doi.org/10.1109/iceace63551.2024.10898837.

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Zhang, Bingbing, Jingqing Jiang, and Jiazhi Song. "An Improved Beluga Whale Optimization Algorithm Based on Chaotic Opposition-Based Learning and Adaptive Weighting Factor." In 2024 International Conference on New Trends in Computational Intelligence (NTCI). IEEE, 2024. https://doi.org/10.1109/ntci64025.2024.10776495.

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Rohr, Nicolas, Oliver Ruggli, Thomas Hanne, and Rolf Dornberger. "Extending the Whale optimization Algorithm with Chaotic Local Search." In 2020 7th International Conference on Soft Computing & Machine Intelligence (ISCMI). IEEE, 2020. http://dx.doi.org/10.1109/iscmi51676.2020.9311600.

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Bi, Jing, Wenduo Gu, and Haitao Yuan. "Chaotic Lévy Whale Optimization Algorithm with Simulated Annealing and Differential Evolution." In 2021 IEEE International Conference on Networking, Sensing and Control (ICNSC). IEEE, 2021. http://dx.doi.org/10.1109/icnsc52481.2021.9702200.

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Bi, Jing, Wenduo Gu, and Haitao Yuan. "Hybrid Whale Optimization Algorithm with Differential Evolution and Chaotic Map Operations." In 2021 IEEE International Conference on Networking, Sensing and Control (ICNSC). IEEE, 2021. http://dx.doi.org/10.1109/icnsc52481.2021.9702209.

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Shi, Xingyu, Zhendong Yin, Li Wang, Heqi Liang, and Zimo Wang. "Solar cell parameter identification based on opposition-based chaotic whale optimization algorithm." In 2022 IEEE 5th International Electrical and Energy Conference (CIEEC). IEEE, 2022. http://dx.doi.org/10.1109/cieec54735.2022.9846391.

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Srikanth, V., Ranjan Walia, P. John Augustine, Venkatesh R, Jerrin Simla, and B. Jegajothi. "Chaotic Whale Optimization based Node Localization Protocol for Wireless Sensor Networks Enabled Indoor Communication." In 2022 International Conference on Electronics and Renewable Systems (ICEARS). IEEE, 2022. http://dx.doi.org/10.1109/icears53579.2022.9751953.

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Nurhayati, Ai, Jack Febrian Rusdi, Saepudin, Harya Gusdevi, Nova Agustina, and Widi Linggih Jaelani. "Performance Comparison of Evolutionary Biogeography Based Whale Optimizations with Chaotic Arc Adaptive Grasshopper Optimization Algorithms." In 2021 3rd International Conference on Cybernetics and Intelligent System (ICORIS). IEEE, 2021. http://dx.doi.org/10.1109/icoris52787.2021.9649446.

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