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A novel Discrete Artificial Bee Colony algorithm combined with adaptive filtering to extract Fetal Electrocardiogram signals

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EXPERT SYSTEMS WITH APPLICATIONS 2024年 247卷

作者： Chai, Qing-Wei Kong, Lingping Pan, Jeng-Shyang Zheng, Wei-Min Shandong Univ Sci & Technol Shandong 266590 Peoples R China VSB TU Ostrava Fac Elect Engn & Comp Sci Ostrava Czech Republic

The Fetal Electrocardiogram (FECG) signal plays a crucial role in monitoring the health of the fetus, but there are numerous challenges in eliminating the maternal thorax signal and reducing noise interference. This paper proposes a novel objective function that combines a Least Mean Squares (LMS) adaptive filter with a heuristic algorithms to enhance the quality of the extracted FECG signal. To achieve better results, we introduce the Discrete Artificial Bee Colony (DABC) algorithm with a new initialization strategy, a random wavelet basic function strategy, and Gaussian distribution. These improvements enhance global search capabilities and ensure a faster convergence rate. The application of heuristic algorithms can reduce noise signals and provides clearer and more accurate results compared to the traditional LMS filter. Furthermore, the effectiveness of this innovative algorithm is compared with other widely used heuristic algorithms. The experiment results demonstrate that the novel algorithm significantly enhances performance by up to 8% compared to other conventional extraction methods in some indicators.

关键词： Heuristic algorithms Artificial Bee Colony swarm intelligence algorithm Signal extraction

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An evolutionary crow search algorithm equipped with interactive memory mechanism to optimize artificial neural network for disease diagnosis

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2024年 90卷

作者： Zamani, Hoda Nadimi-Shahraki, Mohammad H. Islamic Azad Univ Fac Comp Engn Najafabad Branch Najafabad *** Iran Islamic Azad Univ Big Data Res Ctr Najafabad Branch Najafabad *** Iran

Artificial neural network (ANN) is an information processing paradigm that loosely models the thinking patterns of the human brain with specifications such as real-time learning, self-adaption, and self -organization. The learning process of ANNs is complex and tackles shortcomings such as a slow convergence rate, learning timeconsuming, and local minimum trapping, especially when using gradient -based optimization techniques. Although many metaheuristics have been proposed to arm ANN and solve these weaknesses, ANN learning still needs solution quality. Therefore, this study proposes an evolutionary crow search algorithm (ECSA) to optimize the hyperparameters of ANNs for diagnosing chronic diseases. ECSA introduces an evolutionary search strategy, self -adaptive adaptive flight length, and an interactive memory mechanism to alleviate the canonical crow search algorithm's shortcomings. The evolutionary search strategy and self -adaptive flight length provide a meaningful search strategy in which crows effectively explore and exploit problem spaces by maintaining population diversity. During the search process, the interactive memory mechanism records the best solution obtained during optimization. The performance of ECSA was evaluated and compared with well-known metaheuristic algorithms in terms of local and global search abilities, local optima avoidance, and convergence speed towards the promising area. Then, the results were statistically analyzed. Ultimately, the effectiveness of an adaptation of ECSA named ECSA-MLP for optimizing hyperparameters of the multilayer perceptron network for diagnosing diseases including coronavirus, breast cancer, diabetes, cardiovascular, cervical cancer, Parkinson's, mammography, and acute inflammation was compared with state-of-the-art competitor algorithms. The experimental results indicated the superiority of ECSA over competitor algorithms in optimizing the network.

关键词： Artificial Neural Networks Multilayer perceptron network Hyperparameters Metaheuristic algorithm swarm intelligence algorithm Evolutionary optimization algorithms

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Optimal Control and Performance Enhancement of DC-DC Bidirectional SEPIC Converter 13

Optimal Control and Performance Enhancement of DC-DC Bidirec...

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IEEE 13th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON)

作者： Mahmud, Al Jaber Mithun, Mehedi Hasan Khan, Md. Ashik Faisal, Fahim Nishat, Mirza Muntasir Hoque, Md Ashraful Islamic Univ Technol Dept Elect & Elect Engn Dhaka Bangladesh

ISBN: (数字)9781665492997

ISBN: (纸本)9781665492997

This paper represents an inspective work on the closed-loop stability of the Bidirectional SEPIC converter by implementing the Firefly algorithm (FA) and Particle swarm Optimization (PSO) for scheming an optimized PID controller to improve the Percentage of Overshoot (%OS), Rise Time (Tr), Settling Time (Ts), and Peak Amplitude (PA). At first, the transfer function of the converter's open-loop system was determined after which the converter's closed-loop arrangement was introduced with the controller's help. The algorithms were evaluated in the system through different fitness functions: IAE, ITAE, ISE, and ITSE. MATLAB is used to carry out all the simulations. After analyzing the performances for the case of the Bidirectional SEPIC converter, PSO-PID (ITSE) could be chosen as a more optimized controller than FA-based PID controllers as to performance parameters. In this case, values of overshoot (0.2674%) and PA (1.0027) were lower than FA-PID for error function. For rise time, the value of FA-PID (ITAE) was better (0.0670 sec), and for settling time, the value of FA-PID (ITSE) was better (0.1217 sec). Hence, swarm intelligence algorithms-based optimized PID controller provides more optimized results and performs far better than conventional PID controller for Bidirectional SEPIC converter.

关键词： swarm intelligence algorithm Bidirectional SEPIC Optimal Control PID Controller

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Improved Initialization Method for Metaheuristic algorithms: A Novel Search Space View

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IEEE ACCESS 2021年 9卷 121366-121384页

作者： Li, Qian Bai, Yiguang Gao, Weifeng Xidian Univ Sch Math & Stat Xian 710071 Peoples R China

As an essential step of metaheuristic optimizers, initialization seriously affects the convergence speed and solution accuracy. The main motivation of the state-of-the-art initialization method is to generate a small initial population to cover the search space as much as possible uniformly. However, these approaches have suffered from the curse of dimensionality, high computational cost, and sensitivity to parameters, which ultimately reduce the algorithm's convergence speed. In this paper, a new initialization technique named diagonal linear uniform initialization (DLU) is proposed, which follows a novel search view, i.e., adopting the diagonal subspace sampling instead of the whole space. By considering the algorithm's update mechanism, the improved sampling method dramatically improves the convergence speed and solution accuracy of metaheuristic algorithms. Compared with the other eight widely used initialization strategies, the differential evolution (DE) algorithm with DLU obtains the best performance in search accuracy and convergence speed. In the extension experiments, results show that the DLU is still effective for three swarm-based algorithms: particle swarm optimization (PSO), cuckoo search (CS), and artificial bee colony (ABC). Especially for the multi-objective problem, the DLU still demonstrates its powerful performance compared with other strategies.

关键词： Statistics Sociology Optimization Convergence Hypercubes Search problems Licenses Optimization metaheuristic algorithm initialization evolutionary algorithm swarm intelligence algorithm multi-objective optimization

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An evacuation simulation method based on an improved artificial bee colony algorithm and a social force model

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APPLIED intelligence 2021年第1期51卷 100-123页

作者： Zhao, Yuan Liu, Hong Gao, Kaizhou Shandong Normal Univ Sch Informat Sci & Engn Jinan 250014 Peoples R China Shandong Prov Key Lab Distributed Comp Software N Jinan 250014 Peoples R China Macau Univ Sci & Technol Macau Inst Syst Engn Macau 999078 Peoples R China

Simulation modeling is an important tool for simulating crowd behavior and studying the law of crowd evacuation. It is of great significance for exploring evacuation management methods in emergency situations. The real-time change of evacuation is the main challenge of simulation modeling. In the evacuation simulation, it is difficult for people to choose a suitable route according to the change of evacuation dynamics. This paper proposes a new evacuation simulation method which combines an improved artificial bee colony algorithm for dynamic path planning and SFM (Social Force Model) for simulating the movement of pedestrians, to providing pedestrians with timely route selection. In the path planning layer, we developed a MABCM (Multiple-subpopulations Artificial Bee Colony with Memory) algorithm and proposed a new exit evaluation strategy. These methods can plan a route with the shortest evacuation time for pedestrians according to the dynamic changes of evacuation and improve evacuation efficiency. In the simulated motion layer, we use the SFM to avoid collisions and achieve the reproduction of the evacuation scene. We verified the performance of the proposed MABCM on the CEC 2014 benchmark suite, and the results show that it is superior to the four existing artificial bee colony algorithms in most cases. The proposed crowd evacuation method is verified on an existing SFM platform. The experimental results indicate that the proposed method can efficiently evacuate a dense crowd in multiple scenes and can effectively shorten evacuation time.

关键词： Artificial bee colony algorithm Crowd evacuation Computer simulation swarm intelligence algorithm

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Enhancing firefly algorithm with courtship learning

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INFORMATION SCIENCES 2021年 543卷 18-42页

作者： Peng, Hu Zhu, Wenhua Deng, Changshou Wu, Zhijian Jiujiang Univ Sch Informat Sci & Technol Jiujiang 332005 Peoples R China Wuhan Univ Sch Comp Sci Wuhan 430072 Peoples R China

The firefly algorithm (FA) is a nature-inspired heuristic optimization algorithm based on the luminescence and attraction behavior of fireflies. Although the FA can effectively solve complex optimization problems, it suffers from premature convergence because of its simple full attraction model. However, in nature, fireflies exhibit luminous behavior to attract mates. The attractiveness between fireflies of opposite sexes depends not only on the light intensity they emit, but also on their individual size, location, and other factors. In the original FA, all fireflies are assumed to be the same, i.e., they have no gender-based difference, which is not true biologically. Therefore, in this paper, we proposed a novel courtship learning (CL) framework to enhance the performance of the FA. In the proposed framework, the population is divided into female and male subpopulations. The female archiving mechanism is adopted to select excellent fireflies, which are assumed to be female fireflies. When the selected male firefly emits light that is less bright than that of the current firefly, a female individual will be selected from the female archive to guide the movement of the selected male firefly. Comprehensive experiments are conducted on the CEC 2013 benchmark set and the proposed CL framework is integrated with other advanced FA variants to verify its effects. Experimental results confirm that the proposed framework significantly enhances the performance of the original FA and advanced FA variants. (C) 2020 Elsevier Inc. All rights reserved.

关键词： Firefly algorithm swarm intelligence algorithm Global optimization Courtship learning

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A cluster-based intelligence ensemble learning method for classification problems

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INFORMATION SCIENCES 2021年 560卷 386-409页

作者： Cui, Shaoze Wang, Yanzhang Yin, Yunqiang Cheng, T. C. E. Wang, Dujuan Zhai, Mingyu Dalian Univ Technol Sch Econ & Management Dalian 116023 Peoples R China Univ Elect Sci & Technol China Sch Econ & Management Chengdu 610064 Peoples R China Hong Kong Polytech Univ Dept Logist & Maritime Studies Hung Hom Kowloon Hong Kong Peoples R China Sichuan Univ Business Sch Chengdu 610064 Peoples R China

Classification is a vital task in machine learning. By learning patterns of samples of known categories, the model can develop the ability to distinguish the categories of samples of unknown categories. Noticing the advantages of the clustering method in cluster structure analysis, we combine the clustering and classification methods to develop the novel cluster-based intelligence ensemble learning (CIEL) method. We use the clustering method to analyze the inherent distribution of the data and divide all the samples into clusters according to the characteristics of the dataset. Then, for each specific cluster, we use differ-ent classification algorithms to establish the corresponding classification model. Finally, we integrate the prediction results of each base classifier to form the final prediction result. In view of the problem of parameter sensitivity, we use a swarm intelligence algorithm to optimize the key parameters involved in the clustering, classification, and ensemble stages in order to boost the classification performance. To assess the effectiveness of CIEL, we per -form tenfold cross-validation experiments on the 24 benchmark datasets provided by UCI and KEEL. Designed to improve the performance of the classifiers, CIEL outperforms other popular machine learning methods such as naive Bayes, k-nearest neighbors, random for -est, and support vector machine. (c) 2021 Elsevier Inc. All rights reserved.

关键词： Ensemble learning Classification algorithm Combination strategy Clustering algorithm swarm intelligence algorithm

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Improved butterfly optimisation algorithm based on guiding weight and population restart

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JOURNAL OF EXPERIMENTAL & THEORETICAL ARTIFICIAL intelligence 2021年第1期33卷 127-145页

作者： Guo, Yanju Liu, Xianjie Chen, Lei Hebei Univ Technol Sch Elect Informat Engn Tianjin Peoples R China Tianjin Univ Commerce Sch Informat Engn Tianjin Peoples R China

Butterfly Optimisation algorithm (BOA) is a kind of meta-heuristic swarm intelligence algorithm based on butterfly foraging strategy, but it still needs to be improved in the aspects of convergence speed and accuracy when solving with high-dimensional optimisation problems. In this paper, an improved butterfly optimisation algorithm is proposed, in which guiding weight and population restart strategy are applied to the original algorithm. By adding guiding weight to the global search equation, the convergence speed and accuracy of the algorithm are improved, and the possibility of jumping out of the local optimal solution is increased by the population restart strategy. In order to verify the performance of the proposed algorithm, 24 benchmark functions commonly used for optimisation algorithm experiments are applied in this paper, including 12 unimodal functions and 12 multimodal functions. Experimental results show that the proposed algorithm improves the convergence speed, accuracy and the ability to jump out of the local optimal solution.

关键词： Butterfly optimisation algorithm meta-heuristic swarm intelligence algorithm guiding weight population restart

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An improved firefly algorithm based on personalized step strategy

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COMPUTING 2021年第4期103卷 735-748页

作者： Yu, Shuhao Zuo, Xukun Fan, Xianglin Liu, Zhengyu Pei, Mingjing West Anhui Univ Sch Elect & Informat Engn Luan 237012 Peoples R China

Firefly algorithm has shown good performance for solving various optimization problems. Most of the nature-inspired algorithms have the same problem which can easily trap in local optimal. To overcome this defect, a novel personalized step strategy for firefly algorithm (PSSFA) is presented. It uses large step for the optimal firefly and linearly decreasing step for the other fireflies to improve the ability of exploration. Experiments on 20 test functions show that the proposed algorithm can promote accuracy of the original method. Finally, we integrate PSSFA with k-means clustering for five datasets. The results show that PSSFA is an effective optimization algorithm.

关键词： Personalized step Firefly algorithm Local optimum swarm intelligence algorithm Optimization

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Improved Particle swarm Optimization algorithm with Fireworks Search 41

Improved Particle Swarm Optimization Algorithm with Firework...

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第41届中国控制会议

作者： Xiang Jinwei Jiang Chengpeng Cheng Zhizhao Xiao Wendong School of Automation and Electrical Engineering University of Science and Technology Beijing Shunde Graduate School University of Science and Technology Beijing

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

Particle swarm Optimization(PSO) is an iterative swarm intelligence algorithm,whose main idea is introducing random factors into the evolutionary equation to simulate the foraging behavior of ***,this random search model gives rise to the weakened local search ability of PSO,the premature convergence problem and the inability to find the optimal *** improve this issue,an Improved Particle swarm Optimization with Fireworks Search(FS-PSO) is proposed in this *** FS-PSO,explosive sparks and gaussian mutation sparks are used to search at the end of the iteration of PSO based on the search mechanism of the Fireworks algorithm(FWA),which is aiming to increase the population diversity of the particles and enhance the local search ability of *** simulation results show that the improvement of FS-PSO is effective to the premature convergence problem of PSO.

关键词： Particle swarm Optimization Premature Convergence Problem Fireworks Search swarm intelligence algorithm

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