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An efficient multilevel thresholding segmentation method for thermography breast cancer imaging based on improved chimp optimization algorithm

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EXPERT SYSTEMS WITH APPLICATIONS 2021年 185卷 115651-115651页

作者： Houssein, Essam H. Emam, Marwa M. Ali, Abdelmgeid A. Minia Univ Fac Comp & Informat Al Minya Egypt

Thermography images are a helpful screening tool that can detect breast cancer by showing the body parts that indicate an abnormal change in temperature. Various segmentation methods are proposed to extract regions of interest from breast cancer images to enhance the classification. Many issues were solved using thresholding. In this paper, a new efficient version of the recent chimp optimization algorithm (ChOA), namely opposition based Levy Flight chimp optimizer (IChOA), was proposed. The original ChOA algorithm can stagnate in local optima and needs varied exploration with an adequate blending of exploitation. Therefore, the convergence is accelerated by improving the initial diversity and good exploitation capability at a later stage of generations. Opposition-based learning (OBL) is applied at the initialization phase of ChOA to boost its population diversity in the search space, and the Levy Flight is used to enhance its exploitation. Moreover, the IChOA is applied to tackle the image segmentation problem using multilevel thresholding. The proposed method tested using Otsu and Kapur methods over a dataset from Mastology Research with Infrared Image (DMR-IR) database during the optimization process. Furthermore, compared against seven other meta-heuristic algorithms, namely Gray wolf optimization (GWO), Moth flame optimization (MFO), Whale optimization algorithm (WOA), Sine-cosine algorithm (SCA), Slap swarm algorithm (SSA), Equilibrium optimization (EO), and original chimp optimization algorithm (ChOA). Results based on the fitness values of obtained best solutions revealed that the IChOA achieved valuable and accurate results in terms of quality, consistency, accuracy, and the evaluation matrices such as PSNR, SSIM, and FSIM. Eventually, IChOA obtained robustness for the segmentation of various positive and negative cases compared to the methods of its counterparts.

关键词： Breast cancer chimp optimization algorithm Image segmentation Kapur's and Otsu's method Multi-level thresholding Thermography images Meta-heuristics

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Robust approach based chimp optimization algorithm for minimizing power loss of electrical distribution networks via allocating distributed generators

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SUSTAINABLE ENERGY TECHNOLOGIES AND ASSESSMENTS 2021年 47卷 101359-101359页

作者： Fathy, Ahmed Yousri, Dalia Abdelaziz, Almoataz Y. Ramadan, Haitham S. Jouf Univ Fac Engn Elect Engn Dept Sakakah Saudi Arabia Zagazig Univ Fac Engn Elect Power & Machine Dept Zagazig Egypt Fayoum Univ Fac Engn Elect Engn Dept Al Fayyum Egypt Future Univ Egypt Fac Engn & Technol Cairo Egypt Inst Int Stockage Hydrogene ISTHY F-90400 Meroux Moval Belfort France

Integrating distributed generators (DGs) in radial distribution networks plays a vital role in improving the system performance via enhancing the bus voltage and minimizing the system losses. Nonetheless, uncoordinated DGs integration may cause technical issues if they are not efficiently planned, controlled, and operated. Therefore, this paper proposes a new methodology based on the recent metaheuristic chimp optimizer approach (CO) to identify DGs' optimal allocations and rated powers. This work's objective function is minimizing the total active power loss of the network;the considered constraints are load flow, buses' voltages, and transmission lines. The proposed CO is characterized by ease of implementation, high convergence rate, and avoiding stuck in local optima. CO is adapted such that the first design variables are integer numbers representing the locations of DGs while the others are assigned to be the DGs' powers. The proposed CO is applied on three radial networks, 33bus, 69-bus, and 119-bus, moreover two modes of DGs, unity power factor (DGs generate only active power) and non-unity power factor (DGs generate active and reactive powers), are studied. The results obtained via the proposed CO are compared to other reported approaches of exhaustive load flow (ELF), genetic algorithm (GA), and different programmed approaches of particle swarm optimizer (PSO) and Archimedes optimization algorithm (AOA). The obtained results confirmed the superiority and reliability of the proposed CO methodology in achieving a minor power loss via installing the DGs in the correct sites.

关键词： Distributed generators chimp optimization algorithm Distribution network Optimal allocation

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Simultaneous Feature Selection and Support Vector Machine optimization Using an Enhanced chimp optimization algorithm

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algorithmS 2021年第10期14卷 282页

作者： Wu, Di Zhang, Wanying Jia, Heming Leng, Xin Sanming Univ Sch Educ & Mus Sanming 365004 Peoples R China Northeast Forestry Univ Coll Mech & Elect Engn Harbin 150040 Peoples R China Sanming Univ Sch Informat Engn Sanming 365004 Peoples R China

chimp optimization algorithm (ChOA), a novel meta-heuristic algorithm, has been proposed in recent years. It divides the population into four different levels for the purpose of hunting. However, there are still some defects that lead to the algorithm falling into the local optimum. To overcome these defects, an Enhanced chimp optimization algorithm (EChOA) is developed in this paper. Highly Disruptive Polynomial Mutation (HDPM) is introduced to further explore the population space and increase the population diversity. Then, the Spearman's rank correlation coefficient between the chimps with the highest fitness and the lowest fitness is calculated. In order to avoid the local optimization, the chimps with low fitness values are introduced with Beetle Antenna Search algorithm (BAS) to obtain visual ability. Through the introduction of the above three strategies, the ability of population exploration and exploitation is enhanced. On this basis, this paper proposes an EChOA-SVM model, which can optimize parameters while selecting the features. Thus, the maximum classification accuracy can be achieved with as few features as possible. To verify the effectiveness of the proposed method, the proposed method is compared with seven common methods, including the original algorithm. Seventeen benchmark datasets from the UCI machine learning library are used to evaluate the accuracy, number of features, and fitness of these methods. Experimental results show that the classification accuracy of the proposed method is better than the other methods on most data sets, and the number of features required by the proposed method is also less than the other algorithms.

关键词： chimp optimization algorithm highly disruptive polynomial mutation Spearman's rank correlation coefficient Beetle Antennae Search algorithm support vector machine

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Enhanced chimp hierarchy optimization algorithm with adaptive lens imaging for feature selection in data classification

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SCIENTIFIC REPORTS 2024年第1期14卷 1-32页

作者： Zhang, Li Chen, Xiaobo Jiangsu Univ Technol Coll Comp Engn Changzhou 213001 Peoples R China Jilin Univ Key Lab Symbol Computat & Knowledge Engn Minist Educ Changchun 130012 Peoples R China Peoples Bank China Changzhou City Ctr Branch Changzhou 213001 Jiangsu Peoples R China

Feature selection is a critical component of machine learning and data mining to remove redundant and irrelevant features from a dataset. The chimp optimization algorithm (CHoA) is widely applicable to various optimization problems due to its low number of parameters and fast convergence rate. However, CHoA has a weak exploration capability and tends to fall into local optimal solutions in solving the feature selection process, leading to ineffective removal of irrelevant and redundant features. To solve this problem, this paper proposes the Enhanced chimp Hierarchy optimization algorithm for adaptive lens imaging (ALI-CHoASH) for searching the optimal classification problems for the optimal subset of features. Specifically, to enhance the exploration and exploitation capability of CHoA, we designed a chimp social hierarchy. We employed a novel social class factor to label the class situation of each chimp, enabling effective modelling and optimization of the relationships among chimp individuals. Then, to parse chimps' social and collaborative behaviours with different social classes, we introduce other attacking prey and autonomous search strategies to help chimp individuals approach the optimal solution faster. In addition, considering the poor diversity of chimp groups in the late iteration, we propose an adaptive lens imaging back-learning strategy to avoid the algorithm falling into a local optimum. Finally, we validate the improvement of ALI-CHoASH in exploration and exploitation capabilities using several high-dimensional datasets. We also compare ALI-CHoASH with eight state-of-the-art methods in classification accuracy, feature subset size, and computation time to demonstrate its superiority.

关键词： Feature selection chimp optimization algorithm Hierarchy Social class factor Local optimal

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The optimization of permanent magnet synchronous motor using chimp algorithm

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COMPEL-THE INTERNATIONAL JOURNAL FOR COMPUTATION AND MATHEMATICS IN ELECTRICAL AND ELECTRONIC ENGINEERING 2025年第3期44卷 290-302页

作者： Knypinski, Lukasz Poznan Univ Tech Inst Elect Engn & Elect Poznan Poland

PurposeThe purpose of this paper is to develop an algorithm and computer script for the optimization of permanent magnet synchronous machines with self-starting ***/methodology/approachThe optimization script consists of two independent modules: (a) optimization procedure and (b) a finite element models describing the investigation of line-start permanent magnet synchronous motors (LSPMSM). The proposed modification of the chimp optimization algorithm improves the convergence and reliability of the optimization procedure. The multiobjective compromise function consists of three components describing the functional parameters of the optimized machine. The field-circuit mathematical model of the dynamics operation of the LSPMSM was elaborated in Ansys and consists of: transient electromagnetic field equations, equations describing external electric circuits and mechanical motion *** proposed optimization procedure containing the chimp algorithm can be applied to the optimal selection of the selected parameters of the various permanent magnet motors. The optimization algorithm was modified to obtain better convergence. The proposed modification involves the use of different types of changes in the parameter characteristic of the group of best-adapted individuals in the chimp troop. The developed optimization procedure was adapted to optimization ***/valueThe proposed optimization algorithm and optimizing procedure can be successfully applied to solve the design problems of the LSPMSM.

关键词： Optimal design optimization chimp optimization algorithm Line-start permanent magnet synchronous motor Finite element method

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A chimp-inspired remora optimization algorithm for multilevel thresholding image segmentation using cross entropy

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ARTIFICIAL INTELLIGENCE REVIEW 2023年第SUPPL 1期56卷 159-216页

作者： Liu, Qingxin Li, Ni Jia, Heming Qi, Qi Abualigah, Laith Hainan Univ Sch Comp Sci & Technol Haikou 570228 Peoples R China Hainan Normal Univ Sch Math & Stat Haikou 571158 Peoples R China Hainan Normal Univ Key Lab Data Sci & Intelligence Educ Minist Educ Haikou 571158 Peoples R China Sanming Univ Sch Informat Engn Sanming 365004 Peoples R China Al Ahliyya Amman Univ Hourani Ctr Appl Sci Res Amman 19328 Jordan Middle East Univ Fac Informat Technol Amman 11831 Jordan

Multilevel thresholding is one of the most commonly used methods in image segmentation. However, the exhaustive search methods are costly in determining optimal thresholds and the conventional remora optimization algorithm (ROA) is prone to the premature convergence. This paper presents a chimp-inspired remora optimization algorithm (HCROA) to search optimal threshold levels, and the cross-entropy is employed as the objective function. In HCROA, the particles' position are adjusted by the chimp optimization algorithm (ChOA) because of its good exploitation ability and sufficient diversity. With this change, HCROA achieves both the intra-group diversity intelligence and a suitable balance between exploration and exploitation. To validate its performance, a series of experiments are performed. First, we test the HCROA's segmentation accuracy by a set of natural gray-scale images with different thresholds. Second, HCROA is implemented for noisy image segmentation to evaluate its robustness. Several reference-based measurements including Peak Signal-to-Noise Ratio (PSNR), Structural Similarity (SSIM), Feature Similarity (FSIM), Quality Index based on Local Variance (QILV), Haar wavelet-based Perceptual Similarity Index (HPSI), Wilcoxon test, and CPU time have been considered for evaluating the proposed method. Additionally, eight well-known predecessors are injected for parallel comparison. The comparison results prove that the suggested method outperforms the existing approaches in terms of accuracy, convergence speed, noise robustness, and efficiency.

关键词： Remora optimization algorithm chimp optimization algorithm Multi-level thresholding image segmentation Cross-entropy

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Performance optimization of photovoltaic and solar cells via a hybrid and efficient chimp algorithm

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SOLAR ENERGY 2023年第1期253卷 343-359页

作者： Yang, Chao Su, Chang Hu, Haiting Habibi, Mostafa Safarpour, Hamed Khadimallah, Mohamed Amine Guangdong Univ Technol Sch Adv Mfg Jieyang 522000 Guangdong Peoples R China Duy Tan Univ Inst Res & Dev Da Nang 550000 Vietnam Duy Tan Univ Fac Elect Elect Engn Da Nang 550000 Vietnam Sharif Univ Technol Ctr Excellence Design Robot & Automat Dept Mech Engn Azadi AvePOB 113659567 Tehran Iran Imam Khomeini Int Univ Fac Engn Dept Mech Qazvin Iran Prince Sattam Bin Abdulaziz Univ Coll Engn Civil Engn Dept Al Kharj 16273 Saudi Arabia Univ Carthage Polytech Sch Tunisia Lab Syst & Appl Mech Tunis Tunisia

The global shift toward solar energy has resulted in the advancement of research into the manufacture of high-performance solar cells. It is critical to accurately model and identify the parameters of solar cells. Numerous models of solar cells have been presented thus far, including the single-diode, the double-diode, and the three-diode models. Every model contains a number of unidentified parameters, and numerous approaches for determining their optimal values have been published in the literature. The purpose of this article is to propose an efficient optimization technique, dubbed the chimp optimization algorithm (ChOA), for estimating the model parameters of solar networks. The proposed ChOA outperforms state-of-the-art algorithms in terms of conver-gence rate, global search capacity, and durability. To demonstrate the proposed ChOA algorithm's efficiency, it is used to determine the parameters of several photovoltaic modules and solar cells. The result of ChOA is evaluated and compared with ten well-known optimization algorithms in the literature. Additionally, the performance of the ChOA algorithm has been evaluated in a practical application for parameter evaluation of three widely-utilized commercial modules, i.e., multi-crystalline (KC200GT), polycrystalline (SW255), and monocrystalline (SM55), under a variety of temperature and irradiance circumstances that result in alterations in the photovoltaic model's parameters. The results confirm the proposed algorithm's robustness and high performance.

关键词： optimization Solar cell ChOA Photo Voltaic Modules chimp optimization algorithm

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ECH3OA: An Enhanced chimp-Harris Hawks optimization algorithm for copyright protection in Color Images using watermarking techniques

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KNOWLEDGE-BASED SYSTEMS 2023年第1期269卷

作者： Fahmy, Hager El-Gendy, Eman M. Mohamed, M. A. Saafan, Mahmoud M. Mansoura Univ Fac Engn Elect & Commun Engn Dept Mansoura Egypt Mansoura Univ Fac Engn Comp & Control Syst Engn Dept Mansoura Egypt

This paper proposes a hybrid Enhanced chimp-Harris Hawks optimization algorithm (ECH3OA) that combines both enhanced chimp optimization algorithm (ChOA) and Harris Hawks optimization algorithm (HHO). HHO is a robust algorithm in solving optimization issues. However, it suffers from a balancing problem between exploitation and exploration. On the other hand, ChOA algorithm is another powerful algorithm with the capability of balancing between exploitation and exploration. However, ChOA suffers from low convergence and falling in the local minimum. The novelty of ECH3OA as a proposed optimization algorithm is concentrated in three contributions. The first is an enhanced ChOA that uses Levy Flight to improve its exploration phase. The second contribution of the algorithm is proposing an updated formula to calculate escaping energy of the prey as a selector between exploitation and exploration. Finally, the four cases of exploitation using HHO are used in the proposed algorithm. The effectiveness of the proposed algorithm is proved by comparing its performance with other eleven state-of-the-art algorithms on 23 standard benchmark function, 30 benchmark function of different modalities from CEC2017, 10 complex functions from CEC2019, and six standard real world engineering problems. Friedman average rank and Wilcoxon rank-sum (p-value) tests are applied as non-parametric statistical tests. Among the different benchmark suits, ECH3OA is ranked first among the other algorithms. We also applied ECH3OA for copyright protection in Color Images using CryptoWatermarking Techniques. The results show better durability and transparency level of the watermark as compared to recent studies.& COPY;2023 Elsevier B.V. All rights reserved.

关键词： chimp optimization algorithm Harris Hawks optimization Benchmark functions Levy Flight Watermarking

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A novel chaotic chimp sine cosine algorithm Part-I: For solving optimization problem

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CHAOS SOLITONS & FRACTALS 2023年第1期173卷

作者： Vigya Raj, Saurav Shiva, Chandan Kumar Vedik, Basetti Mahapatra, Sheila Mukherjee, V. Indian Inst Technol Indian Sch Mines Dept Elect Engn Dhanbad Jharkhand India Inst Chem Technol Mumbai Marathwada Campus Jalna India SR Univ Warangal Dept Elect & Elect Engn Warangal 506371 Telangana India Alliance Univ Bangalore 562106 India

In the realm of solving complex optimization problems, it becomes crucial to fulfill multiple objectives to achieve optimal system performance while adhering to various conditions and limitations. Traditional optimization methods often face challenges such as sluggish convergence speed and confinement to local optima, making it difficult to find satisfactory solution. To tackle this challenge, the development of multi-objective programming is directed towards satisfying physical constraints. In line of the above problems and limitations, this work aims to contribute by proposing a hybrid optimization technique that can effectively address such optimization issues. The proposed algorithm, called the chaotic chimp sine cosine (C-CHOA-SC) algorithm, combines the chimp optimization algorithm (CHOA) and the sine cosine algorithm (SCA) to overcome problems of slow convergence and getting stuck in local optima. It does this by using the SCA after the CHOA and incorporating chaos to explore and exploit the search space effectively. This hybrid approach aims to improve the algorithm's performance (offers enhanced exploration capabilities, efficient exploitation of the search space, and improved convergence speed) and find better solutions to optimization problems. These features make the C-CHOA-SC algorithm wellsuited for solving complex optimization problems with multiple objectives, providing a contemporary and effective solution. To evaluate the performance of the proposed C-CHOA-SC algorithm, three phases of analysis are conducted. In the first phase, the exploration, exploitation, and avoidance of local optima are tested using the CEC 2019 benchmark problems. The second phase involves tracking and validating the performance of C-CHOASC using multiple performance metrics on two-dimensional test functions. Finally, non-parametric alternative test i.e., the Wilcoxon rank sum test and Friedman test are performed. The results demonstrate that the proposed technique exhibits

关键词： CEC 2019 Chaotic chimp optimization algorithm Sine cosine algorithm Friedman test Wilcoxon rank-sum test

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Dwarf Mongoose chimp optimization Enabled RMDL for Sentiment Categorization Using Cell Phone Data

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INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY & DECISION MAKING 2025年第1期24卷 197-222页

作者： Abraham, Minu P. Reddy, K. R. Udaya Kumar Nitte Deemed Univ NMAM Inst Technol NMAMIT Dept Comp Sci & Engn Karkala 574110 Karnataka India Dayananda Sagar Univ Bengaluru Karnataka India

Sentiment analysis is the process of looking through digital text to determine if the emotional tone of a text is positive, negative, or neutral. It helps companies improve their product, but a serious problem arises in classifying the polarity of certain texts with information, sentences or features to forecast their opinion. Therefore, sentiment classification should be done using new technology that classifies reviews as positive or negative so that users can make effective decisions. This research paper develops an effective model to classify sentiment using cell phone data. Initially, the Amazon phone document is passed to the BERT tokenization stage to split the acquired reviews. Then, the Aspect Term Extraction (ATE) is applied and the Term Frequency-Inverse Document Frequency (TF-IDF) is extracted as the first output. Afterward, Wordnet ontology features are extricated as the second output. Moreover, features like statistical, sarcasm linguistic, and N-gram features are extracted from BERT tokenization and considered as the third output. Finally, the sentiment is classified by subjecting the obtained three outputs to Random Multimodal Deep Learning (RMDL), which is tuned by Dwarf Mongoose chimp optimization (DMCO). DMCO is created by the combination of the Dwarf Mongoose optimization (DMO) and the chimp optimization algorithm (ChOA). The developed DMCO-RMDL approach attained high accuracy, True Positive Rate (TPR), True Negative Rate (TNR), precision, recall, and F1-score values of 93%, 92.8%, 92.2%, 91.5%, 94.1%, and 94.8%, respectively.

关键词： Aspect term extraction cell phone review data sentiment classification random multimodal deep learning dwarf mongoose chimp optimization dwarf mongoose optimization chimp optimization algorithm

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