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cat swarm algorithm Generated Based on Genetic Programming Framework Applied in Digital Watermarking

Computers, Materials & Continua 2025年第5期83卷 3135-3163页

作者： Shu-Chuan Chu Libin Fu Jeng-Shyang Pan Xingsi Xue Min Liu School of Artificial Intelligence Nanjing University of Information Science and TechnologyNanjing210000China College of Computer Science and Engineering Shandong University of Science and TechnologyQingdao266000China Department of Information Management Chaoyang University of TechnologyTaichung40601TaiwanChina School of Information Science and Engineering Fujian University of TechnologyFuzhou350000China Key Laboratory of Hydrometeorological Disaster Mechanism and Warning of Ministry of Water Resource Nanjing University of Information Science and TechnologyNanjing210000China

Evolutionary algorithms have been extensively utilized in practical ***,manually designed population updating formulas are inherently prone to the subjective influence of the *** programming(GP),characterized by its tree-based solution structure,is a widely adopted technique for optimizing the structure of mathematical models tailored to real-world *** paper introduces a GP-based framework(GPEAs)for the autonomous generation of update formulas,aiming to reduce human *** modifications to tree-based GP have been instigated,encompassing adjustments to its initialization process and fundamental update operations such as crossover and mutation within the *** designing suitable function sets and terminal sets tailored to the selected evolutionary algorithm,and ultimately derive an improved update *** cat swarm Optimization algorithm(CSO)is chosen as a case study,and the GP-EAs is employed to regenerate the speed update formulas of the *** validate the feasibility of the GP-EAs,the comprehensive performance of the enhanced algorithm(GP-CSO)was evaluated on the CEC2017 benchmark ***,GP-CSO is applied to deduce suitable embedding factors,thereby improving the robustness of the digital watermarking *** experimental results indicate that the update formulas generated through training with GP-EAs possess excellent performance scalability and practical application proficiency.

关键词： cat swarm algorithm genetic programming digital watermarking update mode mode generation framework

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Routing optimization strategy of IoT awareness layer based on improved cat swarm algorithm

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NEURAL COMPUTING & APPLIcatIONS 2022年第5期34卷 3311-3322页

作者： Xiao, Xiang Zhao, Ming Cent South Univ Sch Comp Sci & Engn Changsha 410000 Hunan Peoples R China

With the rapid development of Internet of Things technology, wireless sensor networks have been widely used in many places. This study mainly discusses the routing optimization strategy of the IoT perceptive layer based on the improved cat swarm algorithm. This study simulates a perceptive network with 100 nodes deployed randomly. As SDWSN for Internet of Things applications, in order to simulate the data transmission requirements of IoT communication and ensure the fairness of experimental comparison, this study uses the pseudo-random mechanism to generate the source address and destination address of data packets. A special SDN controller node is added to the network. The SDN controller node broadcasts information to each sensing node, and the common sensing node sends node information to the SDN controller. The SDN controller can survive the global time graph of the entire network according to the information of the common node. In order to avoid the problem of high energy consumption of cluster heads caused by long-distance data transmission, the cat algorithm protocol adopts multi-hop communication between cluster heads and BS and uses network overhead index to quantify link overhead as the basis for cluster heads to select the next hop node. When the inter-cluster multi-hop route is successfully established, the wireless sensor node begins to collect data and send it to BS node. Six monitoring nodes, two coordinators and one workstation were selected as the test objects. The data volume sent by each node was 2000, and the accuracy rate of test transmission information at different rates and transmission distances was determined. The group network coverage rate of cat swarm algorithm is always above 95%, and the average energy loss of nodes is the highest and less than 36%. The results show that the aggregate of energy consumption of cluster heads and the variance of energy consumption are the lowest in the improved cat cluster algorithm, which ensures the reli

关键词： IoT sensing layer cat swarm algorithm Routing optimization strategy IoT technology

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Soil Moisture Prediction And Irrigation Control In Agriculture 4.0 With The cat swarm algorithm Optimized Deep Convolutional Neural Network 2

Soil Moisture Prediction And Irrigation Control In Agricultu...

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2nd International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2024

作者： Mane, Smita Sandeep Narawade, Vaibhav E. Ramrao Adik Institute of Technology D Y Patil Deemed to be University Maharashtra Nerul Mumbai400706 India Ramrao Adik Institute of Technology D Y Patil Deemed to be University Maharashtra Mumbai400706 India

ISBN: (纸本)9798350368413

Agriculture remains one of the most significant industries across the world and the technological advancements in the traditional agricultural industry led to the development of the Agriculture 4.0. The soil moisture aids in determining the amount of water that needs to be fed into the field. Though there are several advantages, the existing methods possess disadvantages such as less accurate prediction, unsustainable data model, and so on. The cat swarm algorithm-optimized Deep Convolutional Neural Network (CSA-optimized DCNN) is proposed for soil moisture prediction and irrigation control to tackle the challenges of conventional research and to achieve better prediction accuracy. The DCNN optimized with the CSA adds an advantage as the algorithm optimizes the hyperparameters of the DCNN to enhance the efficacy of the model. The obtained data from the sensor nodes are stored in the server and fetched when need to be processed and fed as the input to the DCNN, which is optimized with CSA optimization to enhance the performance. The research model CSA optimized DCNN is assessed with measures such as Accuracy, Sensitivity, and Specificity and achieves 96.43%, 96.12%, and 98.8% respectively. © 2024 IEEE.

关键词： Agriculture 4.0 cat swarm algorithm Deep Convolutional Neural Network Irrigation control Soil Moisture Prediction

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Homotopy-Inspired cat swarm algorithm for Global Optimization

Homotopy-Inspired Cat Swarm Algorithm for Global Optimizatio...

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2nd International Conference on Materials and Products Manufacturing Technology (ICMPMT 2012)

作者： Yang Shi-da Yi Ya-lin Lu Yan-ping Wuhan Univ Sci & Technol Coll Comp Sci & Technol Wuhan 430072 Hubei Peoples R China

ISBN: (纸本)9783037855430

Based on the concepts of homotopy, a novel cat swarm algorithm, called a homotopy-inspired cat swarm algorithm (HCSA),is proposed to deal with the problem of global optimization. Proceeding from dependent variables of optimized function,it traces a path from the solution of an easy problem to the solution of the given one by use of a homotopy--vertical bar a continuous transformation from the easy problem to the given *** novel strategy enables the cat swarm algorithm (CSA) to improve the search efficiency. Theoretical analysis proves that HCSA converges to the global optimum. Experimenting with a wide range of benchmark functions, we show that the proposed new version of CSA, with the continuous transformation, performs better, or at least comparably, to classic CSA.

关键词： cat swarm algorithm Homotopy Meta-heuristic algorithms Global optimization

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Application of cat swarm Optimization algorithm for Optimal Reservoir Operation

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JOURNAL OF IRRIGATION AND DRAINAGE ENGINEERING 2018年第1期144卷

作者： Bahrami, Mahdi Bozorg-Haddad, Omid Chu, Xuefeng Univ Tehran Fac Agr Engn & Technol Dept Irrigat & Reclamat Engn Coll Agr & Nat Resources Tehran *** Iran North Dakota State Univ Dept Civil & Environm Engn Dept 2470 Fargo ND 58108 USA

The scarcity of water resources throughout the world has caused many complexities in meeting water demands, which in turn has created a tendency toward developing more efficient and effective methods for optimum operation of reservoirs. In this study, the cat swarm optimization (CSO) algorithm is applied to determine optimal operation of reservoir systems (a single-reservoir system and a hypothetical four-reservoir system). Comparison with the commonly used genetic algorithm (GA) demonstrates the superiority of this metaheuristic algorithm. For the single-reservoir system, the global optimum of 1.213 was computed using the nonlinear programming method, whereas the average objective-function values for 10 runs of the CSO algorithm and GA were 1.222 and 1.635, respectively. The CSO algorithm scored a convergence rate of 99.58% compared with 78.76% by GA, and a coefficient of variation that was 1/31 that of the GA in 10 runs. In the four-reservoir system, the convergence rate of the CSO algorithm was 99.97% compared with 98.47% by GA, with average objective-function values of 307.76 and 303.59, respectively. The results for the mathematical test functions and operations of the reservoir systems demonstrated the superior performance and high efficiency of the CSO algorithm in finding the global optimization solutions.

关键词： Optimization cat swarm algorithm Reservoir system Operation Karun4 Reservoir

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Chaotic cat swarm algorithms for global numerical optimization

Chaotic cat swarm algorithms for global numerical optimizati...

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2nd International Conference on Materials and Products Manufacturing Technology (ICMPMT 2012)

作者： Yang Shi-da Yi Ya-lin Shan Zhi-yong Wuhan Univ Sci & Technol Coll Comp Sci & Technol Wuhan 430072 Hubei Peoples R China

ISBN: (纸本)9783037855430

A novel Chaotic Improved cat swarm algorithm (CCSA) is presented for global optimization. The CSA is a new meta-heuristic optimization developed based on imitating the natural behavior of cats and composed of two sub-models: tracing mode and seeking mode, which model upon the behaviors of cats. Here different chaotic maps are utilized to improve the seeking mode step of the algorithm. Seven different chaotic maps are investigated and the Logistic and Sinusoidal maps are found as the best choices. Comparing the new algorithm with the CSA method demonstrates the superiority of the CCSA for the benchmark functions.

关键词： cat swarm algorithm Chaos Meta-heuristic algorithms Global optimization

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CC-LA: determining optimal switching angles in a cascaded H-bridge multilevel inverter with the aid of binary cat cubpool-based lion algorithm

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ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 2022年第9期29卷 12399-12413页

作者： Jain, Pragya Deshmukh, Suresh Pandurang Atharva Coll Engn Mumbai Maharashtra India Inst Chem Technol Mumbai Maharashtra India

A multilevel inverter (MLI) is a power electronic device that includes the capability to offer the preferred voltage level (alternating) in the output. Accordingly, selective harmonic elimination (SHE) or pulse width modulation (PWM) methodologies were deployed widely. However, these techniques are not appropriate in a certain situation which involves a huge number of switching angles if an excellent primary guess is not obtainable. Hence, this paper intends to determine the optimum switching angles of a cascaded H-bridge multilevel inverter (CH-MLI) using a hybrid lion optimization algorithm (LOA) and binary cat swarm algorithm (BCSO). The objective of optimizing the switching angle is to generate the needed fundamental voltage and minimize the harmonic content. This is done by resolving the transcendental equations characterizing the harmonic content. The switching angles, i.e., alpha(1), alpha(2).... alpha(m), should be tuned in such a way that it satisfies the condition 0 < alpha(1), alpha(2) ....< alpha(m) < pi/2 Here, the optimal tuning of switching angles is done by the proposed binary cat cubpool-based lion algorithm (BCC-LA). In addition, the analysis is done for the proposed method over the state-of-the-art models in terms of total harmonic distortion (THD), and the impact of varying loads is also examined for the proposed and traditional models, and thus, the superior performance of the proposed model is validated.

关键词： Multilevel inverter Cascaded H-bridge multilevel inverter Optimal switching angles Lion optimization algorithm cat swarm algorithm

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A new torque ripple suppression strategy based on the CSA for PMHM of EVs under new European driving cycles

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INTERNATIONAL JOURNAL OF VEHICLE DESIGN 2024年第1期96卷 42-60页

作者： Zhang, Yao Sun, Xiaodong Chongqing Univ Technol Key Lab Adv Manufacture Technol Automobile Parts Minist Educ Chongqing 4000054 Peoples R China Jiangsu Univ Automot Engn Res Inst Zhenjiang 212013 Peoples R China

This paper presents a new torque ripple suppression strategy for a permanent magnet hub motor (PMHM) of EVs drive. With the complex characteristics such as nonlinear time delay and multi-dimension presented by the PMHM, the traditional PID controller has been unable to meet the requirements of the control system. Thus, the cat swarm algorithm (CSA) is introduced to improve the accuracy of PID parameters thanks to its good global search ability. Moreover, it is found that the proposed CSA-PID in the outer loop can obtain better performance such as smaller torque ripple and faster dynamic response both in steady and dynamic state compared with the traditional PID controller. Finally, the strategy proposed in this paper was applied to the vehicle model through HIL test platform. The possibility of applying the strategy proposed to EVs was verified under the New European Driving Cycle (NEDC).

关键词： PMHM permanent magnet hub motor CSA cat swarm algorithm torque ripple suppression NEDC New European Driving Cycle

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Fuzzy Clusterization of Distorted by Missing Observations Data Sets Using Evolutionary Optimization 9

Fuzzy Clusterization of Distorted by Missing Observations Da...

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9th International Conference on Advanced Computer Information Technologies (ACIT)

作者： Shafronenko, Alina Bodyanskiy, Yevgeniy Pliss, Iryna Patlan, Kateryna Kharkiv Natl Univ Radio Elect Philosophy Kharkov Ukraine Kharkiv Natl Univ Radio Elect Dept Informat Kharkov Ukraine Kharkiv Natl Univ Radio Elect Sci Kharkov Ukraine Kharkiv Natl Univ Radio Elect Control Syst Res Lab Kharkov Ukraine NTU Kharkiv Polytech Inst Kharkov Ukraine

ISBN: (纸本)9781728104492

The problem of probabilistic fuzzy clustering of data arrays, distorted by omissions under overlapping classes is considered. In contrast to the traditional approach (FCM of J. Bezdek), based on the nonlinear programming procedure using the uncertain Lagrange multipliers, it was proposed to solve a multi extremal optimization problems without restrictions using the partial distances. To solve this problem, it is proposed to use a modified evolutionary procedure - accelerated cat swarm optimization algorithm. A special feature of the proposed algorithm is uniting seeking and tracing modes within a multi step optimization procedure, having the properties of a gradient search, the "heavy ball" method, global random search and fuzzy J-means clustering algorithm.

关键词： Optimization cat swarm algorithm Seeking mode Tracing mode

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The Fast Modification of Evolutionary Bioinspired cat swarm Optimization Method 8

The Fast Modification of Evolutionary Bioinspired Cat Swarm ...

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8th IEEE Int Conf on Advanced Optoelectron and Lasers (CAOL) / Sci Workshop on Data Sci in Modern Optoelectronics and Laser Engineering (DSMOLE) / 16th Sci Workshop on Measurement Uncertainty - Scientific, Normative, Appl and Methodical Aspects (UM)

作者： Shafronenko, A. Yu Bodyanskiy, Ye, V Pliss, I. P. Kharkiv Natl Univ Radio Elect Kharkiv Ukraine

ISBN: (纸本)9781728118147

This paper discusses the optimization problem based on cat swarm optimization by introducing elements of a random search in a stochastic modification of the basic procedure into the seeking and tracing modes, that improves the speed and accuracy of determining the direction of movement in the seeking mode and improves the global properties of the procedure in the tracing mode. The proposed optimization method, being a representative of evolutionary algorithms, is intended for using hybrid systems of computational intelligence and, above all, in learning tasks of artificial neural networks, neuro-fuzzy systems, as well as in clustering and classification problems.

关键词： Optimization cat swarm algorithm Seeking mode Tracing mode

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