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A Survey of Using swarm intelligence algorithms in IoT

SENSORS 2020年第5期20卷 1420-1420页

作者： Sun, Weifeng Tang, Min Zhang, Lijun Huo, Zhiqiang Shu, Lei Dalian Univ Technol Key Lab Intelligent Control & Optimizat Ind Equip Dalian 116024 Peoples R China Nanjing Agr Univ Coll Engn Nanjing 210031 Peoples R China Univ Lincoln Lincoln England

With the continuing advancements in technologies (such as machine to machine, wireless telecommunications, artificial intelligence, and big data analysis), the Internet of Things (IoT) aims to connect everything for information sharing and intelligent decision-making. swarm intelligence (SI) provides the possibility of SI behavior through collaboration in individuals that have limited or no intelligence. Its potential parallelism and distribution characteristics can be used to realize global optimization and solve nonlinear complex problems. This paper reviews representative SI algorithms and summarizes their applications in the IoT. The main focus consists in the analysis of SI-enabled applications to wireless sensor network (WSN) and discussion of related research problems in the WSN. Also, we concluded SI-based applications in other IoT fields, such as SI in UAV-aided wireless network. Finally, possible research prospects and future trends are drawn.

关键词： swarm intelligence algorithm ant colony optimization particle swarm optimization artificial bee colony algorithm wireless sensor network UAV

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Particle swarm Optimization with Gaussian Disturbance-based Elite Population for Single-objective Problem 11

Particle Swarm Optimization with Gaussian Disturbance-based ...

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11th Joint International Information Technology and Artificial intelligence Conference, ITAIC 2023

作者： Zhang, Zhiming Sui, Qingya Qi, Lingyu Song, Yaotong Gao, Shangce University of Toyama Faculty of Engineering Toyama-shi930-8555 Japan UM-SJTU Joint Institute Shanghai JiaoTong University Shanghai200240 China

ISBN: (纸本)9798350333664

Single-objective optimization, especially with con- straints, is the most common class of problems in biology, society, and energy. Among various optimization algorithms, swarm intelligence algorithms is undoubtedly an effective methods to solve this type of problem. In this study, we propose a novel swarm intelligence optimization method, namely GuLo, which adopts Gaussian random disturbance into elite population-based particle swarm optimization, which leads the improvement of local search. Comprehensive experimental results on a typical single-objective constrained optimization problem benchmark shows that GuLo has the outstanding performance than other state-of-the-art meta-heuristic optimization approaches. © 2023 IEEE.

关键词： Meta-heuristic Optimization problem swarm intelligence algorithm

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A Hybrid Path Planning and Formation Control Strategy of Multi-Robots in a Dynamic Environment

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JOURNAL OF ADVANCED COMPUTATIONAL intelligence AND INTELLIGENT INFORMATICS 2022年第3期26卷 342-354页

作者： Zhou, Meng Wang, Zihao Wang, Jing Dong, Zhe North China Univ Technol Sch Elect & Control Engn 5 Jinyuanzhuang Rd Beijing 100144 Peoples R China

This paper proposes a hybrid path planning and formation control strategy for multi-robots in a dynamic environment. Under a leader-follower formation structure, the followers can track the motion of one leader after the leader's path is determined. First, a hybrid path planning strategy that contains global path planning and local path planning of the leader is investigated, in which an improved hybrid grey wolf optimizer with whale optimizer algorithm (GWO-WOA) is designed for the global path planning in a given map, meanwhile, a dynamic window approach (DWA) is fused for the local path planning to avoid dynamic obstacles. Then, a leader-follower formation control algorithm is proposed for multiple mobile robots. The followers are controlled to track their corresponding virtual robots which are generated according to the leader's position and the formation. Finally, simulation experiments are given to demonstrate the feasibility and effectiveness of the proposed algorithm in different environments.

关键词： multi-robots formation control hybrid motion planning improved GWO-WOA algorithm swarm intelligence algorithm controller design

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FATA: An efficient optimization method based on geophysics

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NEUROCOMPUTING 2024年 607卷

作者： Qi, Ailiang Zhao, Dong Heidari, Ali Asghar Liu, Lei Chen, Yi Chen, Huiling Changchun Normal Univ Coll Comp Sci & Technol Changchun 130032 Jilin Peoples R China Univ Tehran Coll Engn Sch Surveying & Geospatial Engn Tehran Iran Sichuan Univ Coll Comp Sci Chengdu 610065 Sichuan Peoples R China Wenzhou Univ Key Lab Intelligent Informat Safety & Emergency Zh Wenzhou 325035 Peoples R China

An efficient swarm intelligence algorithm is proposed to solve continuous multi-type optimization problems, named the fata morgana algorithm (FATA). By mimicking the process of mirage formation, FATA designs the mirage light filtering principle (MLF) and the light propagation strategy (LPS), respectively. The MLF strategy, combined with the definite integration principle, drives the algorithmic population to enhance FATA's exploration capability. The LPS strategy, combined with the trigonometric principle, drives the algorithmic individual to improve the algorithm's convergence speed and exploitation capability. These two search strategies can better use FATA's population and individual search capabilities. The FATA is compared with a broad array of competitive optimizers on 23 benchmark functions and IEEE CEC 2014 to verify the optimization capability. This work is designed separately for qualitative analysis, exploration and exploitation competence analysis, the analysis of avoiding locally optimal solutions, and comprehensive comparison experiments. The experimental results demonstrate the comprehensiveness and competitiveness of FATA for solving multi-type functions. Meanwhile, FATA is applied to three practical engineering optimization problems to evaluate its performance. Then, the algorithm obtains better results than its counterparts in engineering problems. According to the results, FATA has excellent potential to be used as an efficient computer-aided tool for dealing with practical optimization tasks. Source codes and related files are available at https://***/*** and other websites.

关键词： Fata morgana algorithm The mirage light filtering principle Light propagation strategy swarm intelligence algorithm Engineering optimization

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Ancient Migration algorithm: A Behavioral Path Model Study of the Lowest-cost Goal from an Anthropological Perspective

Ancient Migration Algorithm: A Behavioral Path Model Study o...

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IEEE/WIC/ACM International Joint Conference on Web intelligence and Intelligent Agent Technology (WI-IAT)

作者： Weng, Ziyang Hu, Yidan Li, Hui Tang, Xiaoyu Weng, Zhimo Wuhan Univ Sch Informat Management Wuhan Peoples R China Altay City Culture Sports Radiot Televis & Touris Altay Peoples R China Wuhan Univ Sch Remote Sensing & Informat Engn Wuhan Peoples R China Zhongnan Univ Econ & Law China Korea Inst New Media Wuhan Peoples R China

ISBN: (纸本)9781665494021

Based on the construction of the geographic data elevation model in the GIS environment, combined with the long-term paleoclimate data, this work realizes mountain snow accumulation, water level cutting and restoration, vegetation ecological simulation, restores the paleo-ecological geological structure, and restricts the migration algorithm. It is extended to the behavioral deduction of ancient human migration, and the path models with the lowest cost of human migration in harsh natural environments are established taking into account the geological diversity, namely the Forest-Water Model and the Snow Line Model. Through the deduction, the effective support of the original Altay population for the route to the north was obtained, the northward migration of the ethnic branch was completed by crossing the snow-capped mountain area.

关键词： swarm intelligence algorithm routes of ancient human activity ancient geographical environment Altay Region

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Priority-Aware Task Offloading and Resource Allocation in Vehicular Edge Computing Networks 18

Priority-Aware Task Offloading and Resource Allocation in Ve...

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18th IEEE International Conference on Mobility, Sensing and Networking (MSN)

作者： Wang, Ye Liu, Yanheng Sun, Zemin Liu, Lingling Li, Jiahui Sun, Geng Jilin Univ Coll Comp Sci & Technol Changchun 130012 Peoples R China

ISBN: (纸本)9781665464574

In recent years, the dramatic increase in vehicles and the limited resources of VEC servers make it challenging for vehicles to execute intensive and sensitive tasks on the local own CPU. The mobile edge computing (MEC) is viewed as a promising paradigm by deploying the cloud resources on roadside road side units (RSU). However, compared to cloud server, MEC servers have limited resources. Moreover, the vehicular tasks with different priorities have different requirements on the edge resources. In this work, we propose a priority-aware collaborative task offloading and resource allocation approach for vehicular edge computing networks (VECN). Specifically, we propose a variant grey wolf optimizer (VGWO) algorithm for resource optimization and a dynamic task offloading strategy (DOS) algorithm for task offloading. Simulation results show that the proposed VGWO algorithm outperforms the basic swarm intelligence optimization algorithm, and the collaborative offloading method is able to effectively reduce the task processing latency and energy consumption.

关键词： mobile edge computing task offloading resource allocation swarm intelligence algorithm collaborative offloading method

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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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