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Heterogeneous cognitive learning chameleon swarm algorithm for high-dimensional feature selection

JOURNAL OF SUPERCOMPUTING 2025年第5期81卷

作者： Braik, Malik Awadallah, Mohammed A. Alzoubi, Hussein Al-Hiary, Heba Al Balqa Appl Univ Comp Sci Dept Salt Jordan Al Aqsa Univ Dept Comp Sci Gaza Palestine Ajman Univ Artificial Intelligence Res Ctr AIRC Ajman U Arab Emirates Yarmouk Univ Comp Engn Dept Irbid Jordan

High-dimensional feature selection problems (HFSPs) are becoming more prevalent but complex. Currently used feature selection (FS) techniques for problems like biological and medical data often struggle due to the complexity of the problems they face. To adeptly address this kind of problem, a new binary form of the chameleon swarm algorithm (BCSA) called binary heterogeneous cognitive learning CSA (BHCLCSA) is proposed. In BHCLCSA, chameleons are specifically divided during optimization into topmost chameleons (TC\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\text {TC}$$\end{document}) and undermost chameleons (UC\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\text {UC}$$\end{document}) as per their fitness. These two types of chameleons are addressed variously by creating an elite cognitive learning (CL) mechanism to evolve the undermost chameleons and a predominant CL mechanism to mature the topmost ones. With the collaboration between these two learning strategies, BHCLCSA is expected to evolve chameleons capable of efficiently exploring the search space and exploiting the discovered optimal regions to obtain the best solutions for HFSPs. Further, this work develops adaptive and dynamic swarm partition mechanisms to adaptively segregate chameleons into two categories. HCLCSA is used in FS domain to confront premature convergence, locate the best subset of features between classes, and improve the CSA's global and local search capabilities. The BHCLCSA-based FS method was evaluated, using the k-nearest neighbor (k-NN) classifier, on 20 HFSPs collected from the UCI repository. The results showed that BHC

关键词： Feature selection chameleon swarm algorithm Transfer function Optimization

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chameleon swarm algorithm Assisted Optimization of U-Slot Patch Antenna for Quad-Band Applications

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IEEE ACCESS 2022年 10卷 74152-74163页

作者： Uluslu, Ahmet Istanbul Univ Cerrahpasa Dept Elect & Automat TR-34500 Istanbul Turkey

Existing optimization algorithms are insufficient in the face of problems with difficult measurement functions as well as a large number of design parameters. Therefore, to achieve such challenging applications, the chameleon swarm algorithm and its variants, which belong to the family of meta-heuristic algorithms that have not been used in any antenna optimization study before, are used together with 3 different objective functions fitted from mathematical models. Then, a U-slot antenna application with 12 different variable design parameters with resonance frequencies of 3.5 GHz, 3.7 GHz, 5.2 GHz and 5.8 GHz is considered as a multidimensional and single-objective optimization problem. In this study, first of all, the success of the algorithm is reinforced by comparing the performance with other commonly used single and multi-objective optimization algorithms. In addition, the results obtained with different population parameters, weight coefficients, objective functions and variant models were compared. All these processes are compared within themselves, and the antenna results of the most successful result are displayed as 3D electromagnetic simulation. The results show that the optimization processes proposed for an antenna designer are a safe, practical and efficient solution for multidimensional and single-objective antenna optimization applications. In addition, any optimization problem with a large number of variable design parameters can undoubtedly be adapted to the chameleon swarm algorithm.

关键词： Optimization Antennas Microstrip antennas Heuristic algorithms Mathematical models Substrates Patch antennas Quad-band eCSA patch antenna multidimensional optimization optimization techniques chameleon swarm algorithm evolutionary algorithms swarm intelligence algorithms

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chameleon swarm algorithm Enhanced by Differential Evolution 12

Chameleon Swarm Algorithm Enhanced by Differential Evolution

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Joint 12th International Conference on Soft Computing and Intelligent Systems / 23rd International Symposium on Advanced Intelligent Systems (SCIS and ISIS)

作者： Cai, Pengxing Zhang, Yu Tang, Zheng Gao, Shangce Univ Toyama Fac Engn Toyama 9308555 Japan

ISBN: (纸本)9781665499248

The chameleon swarm algorithm (CSA) is a novel meta-heuristic algorithm for solving unconstrained optimization problems. CSA simulates the dynamic behavior of chameleon foraging method to design optimization mechanism, which performs excellent in function optimization. Differential evolution (DE) is an effective and classical stochastic direct search method based on evolutionary strategies, its operators have good convergence. In this paper, the excellent exploration ability of CSA and outstanding convergence speed of DE are taken into consideration. We ingeniously incorporate the differential evolution operator into CSA, and propose hybrid CSADE to solve 30 benchmark optimization functions. To testify the effectiveness of proposed CSADE, we utilize 29 benchmark functions on CEC 2017 to conduct experiments. Through the analysis of statistical results for many experiments, CSADE outperforms selected state-of-the-art algorithms.

关键词： Meta-heuristic algorithm chameleon swarm algorithm differential evolution function optimization

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An enhanced chameleon swarm algorithm for global optimization and multi-level thresholding medical image segmentation

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NEURAL COMPUTING & APPLICATIONS 2024年第15期36卷 8775-8823页

作者： Mostafa, Reham R. Houssein, Essam H. Hussien, Abdelazim G. Singh, Birmohan Emam, Marwa M. Univ Sharjah Res Inst Sci & Engn RISE Sharjah 27272 U Arab Emirates Mansoura Univ Fac Comp & Informat Sci Dept Informat Syst Mansoura 35516 Egypt Minia Univ Fac Comp & Informat Al Minya Egypt Linkoping Univ Dept Comp & Informat Sci Linkoping Sweden Fayoum Univ Fac Sci Faiyum Egypt Middle East Univ MEU Res Unit Amman 11831 Jordan Appl Sci Private Univ Appl Sci Res Ctr Amman 11831 Jordan St Longowal Inst Engn & Technol Dept Comp Sci & Engn Longowal Punjab India

Medical image segmentation is crucial in using digital images for disease diagnosis, particularly in post-processing tasks such as analysis and disease identification. Segmentation of magnetic resonance imaging (MRI) and computed tomography images pose distinctive challenges attributed to factors such as inadequate illumination during the image acquisition process. Multilevel thresholding is a widely adopted method for image segmentation due to its effectiveness and ease of implementation. However, the primary challenge lies in selecting the optimal set of thresholds to achieve accurate segmentation. While Otsu's between-class variance and Kapur's entropy assist in identifying optimal thresholds, their application to cases requiring more than two thresholds can be computationally intensive. Meta-heuristic algorithms are commonly employed in literature to calculate the threshold values;however, they have limitations such as a lack of precise convergence and a tendency to become stuck in local optimum solutions. In this paper, we introduce an improved chameleon swarm algorithm (ICSA) to address these limitations. ICSA is designed for image segmentation and global optimization tasks, aiming to improve the precision and efficiency of threshold selection in medical image segmentation. ICSA introduces the concept of the "best random mutation strategy" to enhance the search capabilities of the standard chameleon swarm algorithm (CSA). This strategy leverages three distribution functions-Levy, Gaussian, and Cauchy-for mutating search individuals. These diverse distributions contribute to improved solution quality and help prevent premature convergence. We conduct comprehensive experiments using the IEEE CEC'20 complex optimization benchmark test suite to evaluate ICSA's performance. Additionally, we employ ICSA in image segmentation, utilizing Otsu's approach and Kapur's entropy as fitness functions to determine optimal threshold values for a set of MRI images. Comparative

关键词： chameleon swarm algorithm Metaheuristics Global optimization Multilevel thresholding image segmentation Magnetic resonance imaging Computed tomography

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Collaborative Optimization algorithm Based on chameleon swarm algorithm Designed for the Problem of Beer Production Scheduling

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APPLIED SCIENCES-BASEL 2024年第15期14卷 6562页

作者： Zheng, Song Dai, Chen Hangzhou Dianzi Univ Sch Automat Hangzhou 310018 Peoples R China

Featured Application The algorithm could be used in the beer production *** Aiming at finding a better way to solve the problem of beer production scheduling, a new collaborative optimization based on the Manhattan Distance and chameleon swarm algorithm is proposed. Firstly, a dynamic relaxation factor is introduced to the constraints at the system level, which combines the changing trend of the inconsistency information and the optimal solution of the discipline level. Additionally, the Manhattan Distance is used to replace the square of the Euclidean Distance at the system level. Thirdly, the chameleon swarm algorithm is used to improve the update rule during the process of iteration. As these improvements are applied to the collaborative optimization, the steps of this new algorithm are given. Through the test case of a multivariate function, it can be found that the algorithm has been improved compared to the original algorithm. Then, a model for beer production scheduling is proposed, and the results of the optimization show that the improved collaborative optimization has better optima effectiveness and fewer iterations and is not sensitive to initial points, which proves that the improved collaborative optimization has a better ability to solve the problem of beer production scheduling than normal collaborative optimization and collaborative optimization with fixed relaxation factors.

关键词： beer production scheduling collaborative optimization Manhattan distance chameleon swarm algorithm

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A non-convex economic load dispatch problem using chameleon swarm algorithm with roulette wheel and Levy flight methods

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APPLIED INTELLIGENCE 2023年第14期53卷 17508-17547页

作者： Braik, Malik Sh. Awadallah, Mohammed A. Al-Betar, Mohammed Azmi Hammouri, Abdelaziz I. Abu Zitar, Raed Al Balqa Appl Univ Dept Comp Sci Salt Jordan Al Aqsa Univ Dept Comp Sci POB 4051 Gaza Palestine Ajman Univ Artificial Intelligence Res Ctr AIRC Ajman U Arab Emirates Al Zaytoonah Univ Jordan Fac Sci & Informat Technol Amman Jordan Ajman Univ Coll Engn & Informat Technol Artificial Intelligence Res Ctr AIRC Ajman U Arab Emirates Al Balqa Appl Univ Al Huson Univ Coll Dept Informat Technol Salt Jordan Sorbonne Univ Sorbonne Ctr Artificial Intelligence Abu Dhabi U Arab Emirates

An Enhanced chameleon swarm algorithm (ECSA) by integrating roulette wheel selection and Le vy flight methods is presented to solve non-convex Economic Load Dispatch (ELD) problems. CSA has diverse strategies to move towards the optimal solution. Even so, this algorithm's performance faces some hurdles, such as early convergence and slumping into local optimum. In this paper, several enhancements were made to this algorithm. First, it's position updating process was slightly tweaked and took advantage of the chameleons' randomization as well as adopting several time-varying functions. Second, the Levy flight operator is integrated with roulette wheel selection method and both are combined with ECSA to augment the exploration behavior and lessen its bias towards exploitation. Finally, an add-on position updating strategy is proposed to develop a further balance between exploration and exploitation conducts. The optimization performance of ECSA is shown by testing it on five various real ELD cases with a generator having 3, 13, 40, 80 and 140 units, each with different constraints. The results of the ELD systems' analysis depict that ECSA is better than the parent CSA and other state-of-the art methods. Further, the efficacy of ECSA was experimented on several benchmark test functions, and its performance was compared to other well-known optimization methods. Experimental results show that ECSA surpasses other methods on complex benchmark functions with modest computational burdens. The superiority and practicality of ECSA is demonstrated by getting new best solutions for large-scale ELD cases such as 40-unit and 140-unit test systems.

关键词： Economic load dispatch chameleon swarm algorithm Optimization Levy flight Roulette wheel selection

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Model parameters extraction of solid oxide fuel cells based on semi-empirical and memory-based chameleon swarm algorithm

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INTERNATIONAL JOURNAL OF ENERGY RESEARCH 2021年第15期45卷 21435-21450页

作者： Rizk-Allah, Rizk M. El-Hameed, Mohamed A. El-Fergany, Attia A. Menoufia Univ Fac Engn Basic Engn Sci Dept Shibin Al Kawm 32511 Egypt Zagazig Univ Elect Power & Machines Dept Fac Engn Zagazig Egypt

Reliable and precise parameters' identification of the solid oxide fuel cell (SOFC) models is vital for the simulation and analysis of its steady-state and dynamic behaviors. However, the SOFC model is characterized by multimodal, high nonlinearity, and strong nonconvexity natures. Therefore, this paper develops a novel variant of the chameleon swarm algorithm (CSA), named memory-based chameleon swarm algorithm (MCSA), to extract highly accurate and precise parameters of SOFC model. In the MCSA, the search mechanism of the chameleon is guided using an internal memory to keep track of the best solutions in previous generations using neighborhood searching strategy, experience-based crossover scheme, and greedy selection strategy. These strategies can provide an appropriate balancing among the global exploration and local exploitation phases. Besides, the stored solutions in the memory can enhance the population diversity and accordingly increase the propensity of reaching the global optimal efficiently. The proposed MCSA is examined on various scenarios of the SOFC model under the steady-state and dynamic state manners. The simulation results of the proposed algorithm are confirmed and validated via comprehensive comparisons using the statistical measures and Friedman nonparametric test with other recent counterparts. The conducted comparisons and analyses have affirmed the efficacy and superiority of the proposed MCSA through achieving accurately identified parameters, where minimum deviation among the estimated and measured stack current-voltage and stack current-power curves is exhibited.

关键词： chameleon swarm algorithm optimization parameters estimation solid oxide fuel cell

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An evolutionary chameleon swarm algorithm based network for 3D medical image segmentation

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EXPERT SYSTEMS WITH APPLICATIONS 2024年 239卷

作者： Rajesh, Chilukamari Sadam, Ravichandra Kumar, Sushil Natl Inst Technol Warangal Dept Comp Sci & Engn Warangal 506004 Telangana India Natl Inst Technol Kurukshetra Dept Comp Engn Kurukshetra 136119 Haryana India

Developing 3D Convolutional Neural Networks (CNNs) for medical image segmentation is challenging due to the limited number of available labeled medical images and computational resources. Effective design of a 3D CNN requires a better structure and appropriate hyperparameters. Manually designing a neural network and choosing appropriate hyperparameters requires extensive knowledge, a time-consuming and challenging process, resulting in complex and over-parameterized networks. Neuroevolution is an automatic and effective approach to finding an optimal network structure and hyperparameters. This paper proposes an evolutionary based method using chameleon swarm algorithm (CSA) to develop 3D CNN networks for volume segmentation. We also design a search space to build optimal 3D block-based encoder-decoder structure networks for medical image segmentation. We evaluated the proposed method on two different datasets (CT Spleen and MRI Heart) and compared the results of the obtained model with state-of-the-art models. Furthermore, the comparative results show that our proposed method yields 3D CNN networks with superior performance and fewer parameters than other state-of-the-art models utilized in 3D medical image segmentation.

关键词： Neural architecture search 3D medical image segmentation chameleon swarm algorithm

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Optimal sizing design and integrated cost-benefit assessment of stand-alone microgrid system with different energy storage employing chameleon swarm algorithm: A rural case in Northeast China

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RENEWABLE ENERGY 2023年 202卷 1110-1137页

作者： Zhou, Jianguo Xu, Zhongtian North China Elect Power Univ Dept Econ & Management Baoding 071000 Hebei Peoples R China

This paper proposes an optimal sizing design and cost-benefit evaluation framework for stand-alone renewable microgrid system to serve rural community load usage in Northeast China. The microgrid system combines Photovoltaic arrays (PV), Wind turbines (WT), Tidal turbines (Tid), Battery (Bat) storage and hydrogen storage, respectively. The optimal component sizing is determined based on the actual local hourly meteorological data and load demand during a year using chameleon swarm algorithm (CSA), with minimum Total net present cost (TNPC) of the microgrid system as the optimization objective and the reliability constraint is considered as Loss of power supply probability (LPSP). Meanwhile, a cost-benefit index is further introduced for conveniently evaluating the overall effectiveness of the microgrid systems. The results of the simulation experiments under the benchmark scenario reveal that the PV/WT/Tid/Bat system is the most viable approach for local load supply among different versions of the microgrid. Additionally, a thorough sensitivity investigation of the optimal sizing of two exemplary microgrid systems based on different energy storage is carried out under different technoeconomic scenarios. This study can provide a more efficient and clean electrification scheme and insightful reference for investment in the power-starved Northeast China.

关键词： Power shortage Stand-alone renewable microgrid system Optimal sizing design Energy storage chameleon swarm algorithm Integrated cost -benefit assessment

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Hybrid chameleon swarm algorithm with multi-strategy: A case study of degree reduction for disk Wang-Ball curves

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MATHEMATICS AND COMPUTERS IN SIMULATION 2023年 206卷 709-769页

作者： Hu, Gang Yang, Rui Wei, Guo Xian Univ Technol Dept Appl Math Xian 710054 Peoples R China Xian Univ Technol Sch Comp Sci & Engn Xian 710048 Peoples R China Univ N Carolina Dept Math & Comp Sci Pembroke NC 28372 USA Xian Univ Technol 5 South Jinhua Rd Xian 710048 Peoples R China

In this paper, an enhanced hybrid chameleon swarm algorithm (CSA) is proposed and applied to the degree reduction problem of disk Wang-Ball (DWB) curve. CSA is a novel population-based algorithm inspired by the hunting behavior of chameleons, its simplicity and easy implementation make it applied to different fields. However, it suffers from premature convergence and easy to fall into local optimum, especially in the face of complex optimization problems. Therefore, this paper proposes an enhanced hybrid CSA (CCECSA, for short). Compared with the classic CSA, the proposed CCECSA mainly introduces three improvements: (1) The crisscross optimization algorithm is mixed to avoid premature convergence, in which the horizontal and vertical crossover can generate moderation solutions to increase the diversity of the population. (2) Elite guidance mechanism is introduced to speed up the convergence. (3) Competitive substitution mechanism is added to replace the worst individual, and an interference strategy is set to prevent the algorithm from falling into a local optimum. The efficiency and robustness of the proposed CCECSA are demonstrated by the comparison results with some advanced meta-heuristic algorithms on CEC2014, CEC2017, and 4 engineering design examples. In addition, for the degree reduction problem of DWB curves, the multi-degree reduction optimization models of its center curve and radius function are established respectively. At the same time, the optimal center curve and radius function of the approximating DWB curves of lower degree are obtained by the proposed CCECSA. The experimental results show that the proposed CCECSA achieves the optimal solution with better convergence and robustness. The source code of CCECSA is publicly available in the supplementary material related to this article.(c) 2022 International Association for Mathematics and Computers in Simulation (IMACS). Published by Elsevier B.V. All rights reserved.

关键词： chameleon swarm algorithm Crisscross optimization algorithm Elite guidance mechanism Competitive substitution mechanism Disk Wang-Ball curve Multi-degree reduction

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