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Locality sensitive hashing-aware fruit fly optimization algorithm and its application in edge server placement

JOURNAL OF CLOUD COMPUTING-ADVANCES SYSTEMS AND APPLICATIONS 2022年第1期11卷 1-15页

作者： Cao, Qian Liu, Bo Jin, Ying Chaohu Univ Coll Informat Engn Hefei Peoples R China Macau Univ Sci & Technol Fac Innovat Engn Macau Peoples R China Hefei Univ Sch Artif Intelligence & Big Data Hefei Peoples R China

As is well known that the global optimization ability of the Fruit fly Optimization algorithm (FOA)is weak because it is easy to fall into local optimum. In this paper, a Fruit Fly Optimization algorithm based on Locality Sensitive Hashing-aware (LSHFOA)was proposed. The locality sensitive hashing mechanism to optimize the generation mechanism for swarm population individuals was used, which can improve the individual diversity of the population. Meanwhile, when the fruit fly population falls into the local optimum, the locality sensitive hashing mechanism was adopted to change the population location, which is used for jumping out of local optimal limits. To verify the performance of LSHFOA, it was compared with FOA and its improvement algorithms CFOA, and IFFO with 8 representative benchmark functions. A large number of experimental results showed that LSHFOA has a faster convergence speed and higher precision of optimization for function optimization, especially in high-dimensional multi-peak functions. In addition to the theoretical evaluation, we also evaluate its performance in a real-world scenario. Generally, an edge computing environment, as an extension of cloud computing, can allow the users to access the network in a low-latency manner. In this way, to capture the high-speed convergence advantage, this paper makes the first attempt to tackle a classic research problem in the edge computing environment, i.e., the edge server placement problem. The experimental results show that the new algorithm has an excellent application effect.

关键词： Fruit fly optimization algorithm Locality sensitive hashing swarm intelligence algorithm Global optimization capacity Edge server placement problem Edge computing

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Sintered silicon carbide grinding surface roughness prediction based on deep learning and neural network

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JOURNAL OF THE BRAZILIAN SOCIETY OF MECHANICAL SCIENCES AND ENGINEERING 2022年第7期44卷 1-13页

作者： Yang, Jie Zhang, Liqiang Liu, Gang Gao, Qiuge Qian, Long Shanghai Univ Engn Sci Sch Mech & Automot Engn Shanghai 201620 Peoples R China

Surface roughness is vital to high-precision parts, and it directly affects performance and service time of parts. So, in the grinding process, roughness control and prediction of grinding surface is particularly important. However, it is difficult to accurately predict surface roughness due to interlaced influence of large factors in grinding process. This problem can be solved due to the unique working principle of neural network;thus, in this paper, for the task of ground surface roughness, an integration sparrow search algorithm-based deep belief net model (SSA-DBN) was established, based on data obtained from simulation and experiment. SSA refers to a type of swarm intelligence algorithm, and DBN refers to deep belief net and belongs to deep learning, respectively. The purpose of swarm intelligence algorithm is to optimize network weight, compared to DBN model and SSA-BP network model, that is, DBN without optimization and back-propagation neural network (BPNN) with optimization, for analyzing their performance separately. Ground surface roughness prediction based on SSA-DBN model effectiveness was proven through training. At the same time, compared to SSA-BP network model and DBN model, SSA-DBN model performance was the best.

关键词： Roughness Neural network swarm intelligence algorithm Deep learning

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Whale Optimization algorithm Strategies for Higher Interaction Strength T-Way Testing

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Computers, Materials & Continua 2022年第10期73卷 2057-2077页

作者： Ali Abdullah Hassan Salwani Abdullah Kamal Z.Zamli Rozilawati Razali Faculty of Information Science and Technology Universiti Kebangsaan MalaysiaBangi43600SelangorMalaysia Faculty of Computing College of Computing and Applied SciencesUniversiti Malaysia PahangPekan26600PahangMalaysia

Much of our daily tasks have been computerized by machines and sensors communicating with each other in *** is a reasonable risk that something could go wrong because there are a lot of sensors producing a lot of *** testing(CT)can be used in this case to reduce risks and ensure conformance to *** existing metaheuristic-based solutions aim to assist the test suite generation for combinatorial testing,also known as t-way testing(where t indicates the interaction strength),viewed as an optimization *** previous research,while helpful,only investigated a small number of interaction strengths up to t=*** lightweight applications,research has demonstrated good fault-finding ***,the number of interaction strengths considered must be higher in the case of interactions that generate large amounts of *** to resource restrictions and the combinatorial explosion challenge,little work has been done to produce high-order interaction *** this context,the Whale Optimization algorithm(WOA)is proposed to generate high-order interaction *** ensure that WOA conquers premature convergence and avoids local optima for large search spaces(owing to high-order interaction),three variants of WOA have been developed,namely Structurally Modified Whale Optimization algorithm(SWOA),Tolerance Whale Optimization algorithm(TWOA),and Tolerance Structurally Modified Whale Optimization algorithm(TSWOA).Our experiments show that the third strategy gives the best performance and is comparable to existing state-of-thearts based strategies.

关键词： Software testing optimization problem swarm intelligence algorithm combinatorial optimization IoT

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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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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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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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Improved African Vulture Optimization algorithm Based on Quasi-Oppositional Differential Evolution Operator

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IEEE ACCESS 2022年 10卷 95197-95218页

作者： Liu, Renju Wang, Tianlei Zhou, Jing Hao, Xiaoxi Xu, Ying Qiu, Jiongzhi Wuyi Univ Dept Intelligent Mfg Jiangmen 529020 Peoples R China

In this study, an improved African vulture optimization algorithm (IAVOA) that combines the African vulture optimization algorithm (AVOA) with both quasi-oppositional learning and differential evolution is proposed to address specific drawbacks of the AVOA, including low population diversity, bad development capability, and unbalanced exploration and development capabilities. The improved algorithm has three parts. First, quasi-oppositional learning is introduced in the population initialization and exploration stages to improve population diversity. Second, a differential evolution operator is introduced in the local search position update of each population to improve exploration capability. Third, adaptive parameters are introduced to the differential evolution operator, thus balancing the algorithm exploration and development. A numerical simulation experiment based on 36 different types of benchmark functions showed that while the IAVOA can enhance the convergence speed and solution accuracy of the basic AVOA and two variants of AVOA, IAVOA outperforms the other 7 swarm intelligence algorithms in the mean and best values of 33 benchmark functions.

关键词： Statistics Sociology Mathematical models Particle swarm optimization Metaheuristics Convergence Benchmark testing African vulture optimization algorithm benchmark function differential evolution quasi-oppositional learning swarm intelligence algorithm

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Cooperative Networking Strategy of UAV Cluster for Large-Scale WSNs

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IEEE SENSORS JOURNAL 2022年第22期22卷 22276-22290页

作者： Chen, Yidi Liu, Hao Guo, Jiapeng Wang, Yujie Liu, Fei Ding, Shanshan Chen, Renwen Nanjing Univ Aeronaut & Astronaut State Key Lab Mech & Control Mech Struct Nanjing 210016 Peoples R China

To solve the shortcomings of wireless sensor networks (WSNs) in data collection in a wide range, long distance, and other complex environments, such as high networking cost, short network life cycle, and difficult data collection, a data collection strategy based on the unmanned aerial vehicle (UAV) cluster collaborative networking is proposed. First, the node structure and deployment application of WSNs are analyzed, and the architecture of UAV collaborative data collection is designed. Second, the related models of communication energy consumption between WSNs' network and UAV cluster, UAV cluster task allocation, and path planning are established successively. Then, a simple but efficient task planning strategy considering UAV forward and return distances is proposed. Finally, for the problem of model solving, the solution strategy of the heuristic swarm intelligence algorithm is proposed. The simulation results verify that the strategy reduces the overall network energy consumption by 42.16% and the total range of UAV by 9.7% and realizes the relative minimization of WSNs energy consumption and UAV flight cost on the premise of ensuring the full coverage of WSNs' nodes.

关键词： Wireless sensor networks Sensors Optimization Data collection Autonomous aerial vehicles Energy consumption Heuristic algorithms Collaborative perception data collection swarm intelligence algorithm unmanned aerial vehicle (UAV) wireless sensor networks (WSNs)

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Coordinated control strategy for braking and shifting for electric vehicle with two-speed automatic transmission

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ETRANSPORTATION 2022年 13卷

作者： Liu, Yonggang Gao, Dekun Zhai, Kening Huang, Qi Chen, Zheng Zhang, Yi Chongqing Univ State Key Lab Mech Transmiss Chongqing 400044 Peoples R China Chongqing Univ Coll Mech & Vehicle Engn Chongqing 400044 Peoples R China Dongfeng Liuzhou Automobile Co Ltd Passenger Car Technol Ctr Liuzhou 545005 Guangxi Peoples R China Chongqing Univ Sci & Technol Sch Mech & Power Engn Chongqing 401331 Peoples R China Kunming Univ Sci & Technol Fac Transportat Engn Kunming 650500 Yunnan Peoples R China Univ Michigan Dearborn Dept Mech Engn Dearborn MI 48128 USA

This paper presents a coordinated control strategy for braking and shifting of electric vehicles that are equipped with a two-speed automatic transmission. The coordinated control strategy mainly includes braking force distribution and synthetic shifting regulation for braking operations. Firstly, the vehicle model is established for investigation of the shift process during braking. Then, the braking force distribution considering energy efficiency is presented, and the regenerative braking force is calculated. With the target of maximizing energy recovery and ensuring driving comfort, an optimal shift schedule during regenerative braking is advanced based on the multi-objective cuckoo search algorithm. Due to the interaction of regenerative braking force in torque releasing and restoring process, an adaptive braking mode switching control is proposed to improve the shifting smoothness. The detailed simulations and hardware-in-the-loop validations are conducted to verify the anticipated performance of the proposed strategy. (C) 2022 Elsevier B.V. All rights reserved.

关键词： Electric vehicle Regenerative braking Multi-objective cuckoo search swarm intelligence algorithm

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A modified hybrid rice optimization algorithm for solving 0-1 knapsack problem

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APPLIED intelligence 2022年第5期52卷 5751-5769页

作者： Shu, Zhe Ye, Zhiwei Zong, Xinlu Liu, Shiqin Zhang, Daode Wang, Chunzhi Wang, Mingwei Hubei Univ Technol Sch Comp Sci Wuhan Peoples R China Hubei Univ Technol Sch Mech Engn Wuhan Peoples R China

The 0-1 knapsack problem (KP) is a classic NP-hard problem and could be handled by swarm intelligence algorithms. However, most of these algorithms might be trapped in the local optima as the scale increases. Hybrid rice optimization (HRO) is a novel swarm intelligence algorithm inspired by the breeding process of Chinese three-line hybrid rice, its population is classified into three types such as the maintainer, restorer and sterile line and several stages including hybridization, selfing and renewal are implemented. In this paper, a modified HRO algorithm is proposed for the complicated large-scale 0-1 KP. A dynamic step is introduced in the renewal stage to balance the exploration and exploitation phases. Moreover, HRO is combined with binary ant colony optimization (BACO) algorithm to compose the parallel model and serial model for enhancing the convergence speed and search efficiency. In the parallel model, HRO and BACO are independently implemented on two subpopulations and communicate during each iteration. In the serial model, BACO is embedded in HRO as an operator to update the maintainer line. The experimental results on 0-1 KPs of different scales and correlations demonstrate the outperformance of the parallel model and serial model.

关键词： 0-1 knapsack problem swarm intelligence algorithm Hybrid rice optimization algorithm Binary ant colony optimization algorithm

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