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MKELM: Mixed Kernel Extreme Learning Machine using BMDA optimization for web services based heart disease prediction in smart healthcare

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COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING 2022年第10期25卷 1180-1194页

作者： Sheeba, Adlin Padmakala, S. Subasini, C. A. Karuppiah, S. P. St Josephs Inst Technol Dept Comp Sci & Engn Chennai Tamil Nadu India St Josephs Coll Engn Dept MBA Chennai Tamil Nadu India

In recent years, cardiovascular disease becomes a prominent source of death. The web services connect other medical equipments and the computers via internet for exchanging and combining the data in novel ways. The accurate prediction of heart disease is important to prevent cardiac patients prior to heart attack. The main drawback of heart disease is delay in identifying the disease in the early stage. This objective is obtained by using the machine learning method with rich healthcare information on heart diseases. In this paper, the smart healthcare method is proposed for the prediction of heart disease using Biogeography optimization algorithm and Mexican hat wavelet to enhance Dragonfly algorithm optimization with mixed kernel based extreme learning machine (BMDA-MKELM) approach. Here, data is gathered from the two devices such as sensor nodes as well as the electronic medical records. The android based design is utilized to gather the patient data and the reliable cloud-based scheme for the data storage. For further evaluation for the prediction of heart disease, data are gathered from cloud computing services. At last, BMDA-MKELM based prediction scheme is capable to classify cardiovascular diseases. In addition to this, the proposed prediction scheme is compared with another method with respect to measures such as accuracy, precision, specificity, and sensitivity. The experimental results depict that the proposed approach achieves better results for the prediction of heart disease when compared with other methods.

关键词： Machine learning healthcare M-health heart disease prediction BMDA swarm intelligence algorithm MKELM web services

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A novel elitist fruit fly optimization algorithm

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SOFT COMPUTING 2023年第8期27卷 4823-4851页

作者： He, Jieguang Peng, Zhiping Qiu, Jinbo Cui, Delong Li, Qirui Guangdong Univ Petrochem Technol Coll Comp Sci Maoming Peoples R China Zhejiang Univ State Key Lab Ind Control Technol Hangzhou Peoples R China Jiangmen Polytech Sch Informat Engn Jiangmen Peoples R China Guangdong Univ Petrochem Technol Guangdong Prov Key Lab Petrochem Equipment Fault D Maoming Peoples R China Guangdong Univ Petrochem Technol Coll Elect Informat Engn Maoming Peoples R China

Aiming at the poor population diversity and serious imbalance between global exploration and local exploitation in the original fruit fly optimization algorithm (FOA), a novel elitist fruit fly optimization algorithm (EFOA) with elite guidance and population diversity maintenance is proposed. EFOA consists of two search phases: an osphresis search with elite and random individual guiding and a vision search with elite and boundary guiding in an iteration. The former contains two sub-stages: exploration with random individual guiding and exploitation with elite individual guiding. Randomly selected individual and flight control parameter constructed by the Sigmoid-based function are first introduced into the algorithm to improve the exploration. The elite guiding strategy with two position-update approaches is designed to augment the local ability of the proposed algorithm. With these stages, EFOA can search some areas of the problem space as much as possible. Finally, elite and boundary information is introduced into EFOA to enhance population diversity. The proposed EFOA is compared with other algorithms, including the original FOA, three outstanding FOA variants, and five state-of-the-art meta-heuristic algorithms. The validation tests are conducted based on the classical benchmark functions and CEC2017 benchmark functions. The Wilcoxon signed rank test and Friedman test are utilized to verify the significance of the results from the perspective of non-parametric statistics. The results demonstrate that the elite guiding strategy and the alternating execution of the three search stages can effectively balance the exploration and exploitation capabilities of the EFOA and enhance its convergence speed.

关键词： swarm intelligence algorithm Fruit fly optimization algorithm Elite guidance Boundary information Population diversity

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A core firework updating information guided dynamic fireworks algorithm for global optimization

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SOFT COMPUTING 2020年第2期24卷 1185-1211页

作者： Zhao, Haitong Zhang, Changsheng Ning, Jiaxu Northeastern Univ Coll Comp Sci & Engn Shenyang 110819 Peoples R China

As a new variant of swarm intelligence algorithm, fireworks algorithm (FWA) exhibits promising performance on a wide set of optimization problems, for which the fireworks algorithm has been concentrated on and investigated by researchers recently. This paper aims to improve the performance of the FWA by exploiting updating information of the core firework to guide the algorithm's searching process. Based on this mentality, this paper ameliorated the explosion strategy of core firework of dynamic fireworks algorithm (dynFWA). The proposed algorithm, named dynPgFWA in this paper, improved FWA from two aspects: amplifying the explosion amplitude on the direction on which core firework is updated, and making more sparks which are generated by core firework distributed on this direction to enhance the algorithm's searching ability on updating direction. A numerical experiment on CEC2015 and CEC2017 test suite was implemented to verify the performance of the proposed algorithm. Results of the experiment indicated that dynPgFWA outperformed the compared evolutionary algorithms in the quality of solutions.

关键词： Fireworks algorithm Updating information Core firework swarm intelligence algorithm Evolutionary computing

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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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Research on Medical Image Encryption Method Based on Uniform Scrambling and Chaotic Mapping Based on swarm intelligence

Research on Medical Image Encryption Method Based on Uniform...

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作者： Kun Li Jing Liu Chunfeng Song Faculty of Network Science Haikou University of Economics

Medical images need to be encrypted in the process of storage and transmission because of the important issues such as privacy, in order to improve the quality of medical image encryption and ensure the speed of encryption. Based on swarm intelligence(swarm intelligence) algorithm, the medical image encryption methods of uniform scrambling and chaotic mapping are studied. A uniform scrambling method based on Arnold mapping is used, and a pseudo-random number is generated in combination with the logistic map to control the scrambling parameters, and the values of the pixel points are XORed to further encrypt the image. The experimental results show that the image encryption algorithm has a large key space, is sensitive to the initial conditions of the key, has a high degree of grayscale scrambling, and has fast encryption and decryption speed. It is suiTable for real-time encryption protection of digital image information on the Internet.

关键词： swarm intelligence algorithm uniform scrambling chaotic mapping medicine image encryption

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Enhancing firefly algorithm with sliding window for continuous optimization problems

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NEURAL COMPUTING & APPLICATIONS 2022年第16期34卷 13733-13756页

作者： Peng, Hu Qian, Jiayao Kong, Fanrong Fan, Debin Shao, Peng Wu, Zhijian Jiujiang Univ Sch Comp & Big Data Sci Jiujiang 332005 Peoples R China JiangXi Agr Univ Sch Comp & Informat Engn Nanchang 330045 Jiangxi Peoples R China Wuhan Univ Sch Comp Sci Wuhan 430072 Peoples R China

Firefly algorithm (FA) is a simple and effective swarm intelligence algorithm, which has received wide attention from scholars. In original FA, each firefly must be compared with other fireflies in brightness, but it may not move, which may result in waste of system resources. Therefore, an enhancing firefly algorithm with sliding window (SWFA) is proposed in this paper to address the above problem. SWFA introduces sliding window mechanism to improve the attraction model of the FA, which is a technology used to ensure the reliability of data transmission in computer networks. The sliding window mechanism is essentially an archive mechanism, where the window denotes a form of archive, and sliding is the way the window updates. The update of the population is guided through the method of information exchange among individuals inside and outside the window. SWFA also combines the sliding window mechanism with reverse learning to reduce the number of comparisons and ensure every comparison is effective. Moreover, a novel adaptive step adjustment strategy is designed, which balances exploration and exploitation of FA. In order to verify the effectiveness of SWFA, extensive experiments are conducted on the CEC 2015 and CEC2013 test suite. Additionally, experiments are conducted on parameters estimation of chaotic systems and three practical engineering optimization problems. The results of the experiments show that the proposed algorithm has better performance.

关键词： Firefly algorithm swarm intelligence algorithm Attraction model Sliding window

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Technical, economic, and environmental assessment of a stand-alone power system based on diesel engine with/without energy storage using an optimization algorithm: A case study in China

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ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 2023年第27期31卷 38585-38602页

作者： Chen, Yujie Zhang, Shuo Henan Univ Technol Coll Informat Sci & Engn Zhengzhou 450000 Henan Peoples R China

In stand-alone power systems, technical, economic, and environmental (TEE) assessment of hybrid energy systems under uncertainty is an important issue. This paper focuses on the TEE assessment of a stand-alone hybrid energy system composed of photovoltaic (PV) and diesel generator (DG) with/without battery energy storage (BS) in remote islands in China. So, determining the optimal sizes of PV and DG with/without BS for economic, reliable, and efficient operation of a hybrid power system in a microgrid is important. For this goal, a modified swarm intelligence algorithm is used to optimize, techno-economic feasibility and avoid potential CO2 emission. To demonstrate the effectiveness of the modified swarm intelligence algorithm, it is compared with the standard swarm intelligence method and simple simulated annealing method in terms of operational cost reduction and power loss reduction. The aim of the optimization is to minimize the cost of a stand-alone solar power system based on diesel engine with/without battery energy storage system by optimal determination of the load uncertainty and CO2 emission. The optimal results are developed further by performing sensitivity analysis, such as the effect of the fuel cost and the penalty cost of CO2 emission. Over the case study, simulation results show that the proposed algorithm obtains more promising results in terms of TEE aspects. The reliability, low carbon, and cost-effectiveness of stand-alone solar power systems based on diesel engine with battery energy storage system can be easily calculated using the correlations derived in this analysis. The resulting cost of energy is in the range of 0.2845 to 0.6492 $/kWh.

关键词： Stand-alone power system swarm intelligence algorithm Techno-economic and environmental feasibility Penalty cost of CO2 emission Fuel cost

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QUAntum Particle swarm Optimization: an auto-adaptive PSO for local and global optimization

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COMPUTATIONAL OPTIMIZATION AND APPLICATIONS 2022年第2期82卷 525-559页

作者： Flori, Arnaud Oulhadj, Hamouche Siarry, Patrick Univ Paris Est Creteil LISSI F-94400 Vitry Sur Seine France

Particle swarm Optimization (PSO) is a population-based metaheuristic belonging to the class of swarm intelligence (SI) algorithms. Nowadays, its effectiveness on many hard problems is no longer to be proven. Nevertheless, it is known to be strongly sensitive on the choice of its settings and weak for local search. In this paper, we propose a new algorithm, called QUAntum Particle swarm Optimization (QUAPSO) based on quantum superposition to set the velocity PSO parameters, simplifying the settings of the algorithm. Another improvement, inspired by Kangaroo algorithm (KA), was added to PSO in order to optimize its efficiency in local search. QUAPSO was compared with a set of six well-known algorithms from the literature (two parameter sets of classical PSO, KA, Differential Evolution, Simulated Annealing Particle swarm Optimization, Bat algorithm and Simulated Annealing Gaussian Bat algorithm). The experimental results show that QUAPSO outperforms the competing algorithms on a set of 30 test functions.

关键词： Particle swarm Optimization swarm intelligence algorithm Auto-adaptive algorithm Self-organization

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IPP: An Intelligent Privacy-Preserving Scheme for Detecting Interactions in Genome Association Studies

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023年第1期20卷 455-464页

作者： Wang, Huanhuan Wu, Xiang China Univ Min & Technol Sch Informat & Control Engn Xuzhou 221116 Jiangsu Peoples R China Xuzhou Med Univ Sch Med Informat & Engn Xuzhou 221000 Jiangsu Peoples R China

Analyzing K-order Single Nucleotide Polymorphism (SNP) interactions through the statistics of Genome-Wide Association Studies (GWAS) is crucial for discovering pathogenic causes of human complex diseases and controlling risk genetic variants of diverse disorders. We propose a method based on Ant Colony Optimization (ACO) algorithm to detect gene interactions for GWAS - an Intelligent Privacy-Preserving scheme (IPP). Initially, we design a multi-objective search algorithm to discover the candidate SNP sets related to disease phenotype, which utilizes Differential Privacy (DP) method by disturbing the multi-objective function to construct a rational epistatic privacy protection strategy. Furthermore, the global path selection strategy composed of two probabilistic methods is proposed to reduce the probability of falling into the local optimum. We use simulated models and a real dataset of Rheumatoid Arthritis (RA) to compare IPP with four popular methods to detect K-order SNPs, the experimental results show that IPP can guarantee the search accuracy effectively and enhance the detecting ability of various models. Further, the privacy budget experiments indicate that the range of privacy budget in IPP is reasonable and make the framework more stable.

关键词： Genome-wide association studies swarm intelligence algorithm privacy-preserving differential privacy

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Improved Hybrid Grey Wolf Optimization algorithm Based on Dimension Learning-Based Hunting Search Strategy

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IEEE ACCESS 2023年 11卷 13738-13753页

作者： Zhang, Chuanjing Liu, Huanlao Zhou, Qunlong Liu, Can Guangdong Ocean Univ Sch Mech Engn Zhanjiang 524088 Guangdong Peoples R China

An improved hybrid grey wolf optimization algorithm (IHGWO) is proposed to solve the problem of population diversity, imbalance of exploration and development capabilities, and premature convergence. The algorithm benefits from particle swarm optimization and a dimension learning-based hunting search strategy. In the particle swarm algorithm search strategy, linear variable social learning and self-learning are introduced to improve the population's ability to communicate information. The individual position, current iteration optimal position, and optimal population position of grey wolves are combined to update the individual position information, thus strengthening the communication between individuals and the population. In the dimension learning-based hunting search strategy, neighborhoods are built for each search member, and neighborhood members can share information, balance global and local searches, and maintain diversity. To validate the algorithm, 23 typical benchmark functions, CEC2022 benchmark functions, and engineering problem sinusoidal low-order-polynomial prediction of positioning error of numerical control machine tools are used to optimize the algorithm's parameters. Results are compared with those from four other algorithms and analyzed using Friedman's statistical test. Experimental and statistical tests reveal that the IHGWO algorithm has the best overall benchmark function rating, with an overall effectiveness of 87.23%. In the engineering parameter optimization problem, the mean square error, root mean square error, and goodness of fit of the prediction equation after IHGWO algorithm optimization are 95.3761, 9.7661, and 97.47%, respectively. These numerical values are superior to those of the compared algorithms, effectively demonstrating the comprehensive performance and applicability of the algorithm.

关键词： Convergence Statistics Sociology Search problems Prediction algorithms Particle swarm optimization Metaheuristics Grey wolf optimizer improved hybrid grey wolf optimization algorithm metaheuristic optimization swarm intelligence algorithm function optimization

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