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An application of meta-heuristic and nature-inspired algorithms for designing reliable networks based on the Internet of things: A systematic literature review

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INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS 2023年第5期36卷

作者： Gong, Jianhu Guangdong Peizheng Coll Sch Data & Comp Sci Guangzhou Peoples R China Guangdong Peizheng Coll Sch Data & Comp Sci Guangzhou 510830 Peoples R China

The widespread use of Internet of Things (IoT) in various wireless sensor networks applications has increased their importance in recent years. IoT is a smart technology that connects anything anywhere at any time. These smart objects, which connect the physical world with the world of computing infrastructure, are expected to permeate all aspects of our daily lives and revolutionize a number of application domains such as healthcare, energy conservation, and transportation. As wireless networking expands, the disadvantage of wireless communication is clearly obvious. People's apprehension over the IoT's dependability has therefore skyrocketed. IoT networks' key requirements are dependability, channel security, fault tolerance, and reliability. Monitoring the IoT networks depends on the availability and correct functioning of all the network nodes. Recent research has proposed promising solutions to address these challenges. This article systematically examines recent articles that use meta-heuristic and nature-inspired algorithms to establish reliable IoT networks. Eighteen articles were analyzed in four groups. Results showed that reliable enhancement mechanisms in IoT networks increase fault node detection, network efficiency, and lifetime and attain energy optimization results in the IoT concept. Additionally, it was discovered in the literature that the current studies focus on how to effectively use edge network capabilities for IoT application executions and support, along with the related needs.

关键词： dependability energy consumption fault tolerant Internet of things meta-heuristic algorithms nature-inspired algorithm reliability wireless sensor networks

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Generalized Oppositional Moth Flame Optimization with Crossover Strategy:An Approach for Medical Diagnosis

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Journal of Bionic Engineering 2021年第4期18卷 991-1010页

作者： Jianfu Xia Hongliang Zhang Rizeng Li Huiling Chen Hamza Turabieh Majdi Mafarja Zhifang Pan Department of General Surgery The Second Affiliated Hospital of Shanghai UniversityWenzhou 325000China Department of Computer Science and Artificial Intelligence Wenzhou UniversityWenzhou 325035China Department of Information Technology College of Computers and Information TechnologyP.O.Box11099Taif 21944Taif UniversityTaifSaudi Arabia Department of Computer Science Birzeit UniversityRamallah 72439Palestine The First Affiliated Hospital of Wenzhou Medical University Wenzhou 325000China

In the original Moth-Flame Optimization(MFO),the search behavior of the moth depends on the corresponding flame and the interaction between the moth and its corresponding flame,so it will get stuck in the local optimum easily when facing the multi-dimensional and high-dimensional optimization ***,in this work,a generalized oppositional MFO with crossover strategy,named GCMFO,is presented to overcome the mentioned *** the proposed GCMFO,GOBL is employed to increase the population diversity and expand the search range in the initialization and iteration jump phase based on the jump rate;crisscross search(CC)is adopted to promote the exploitation and/or exploration ability of *** proposed algorithm’s performance is estimated by organizing a series of experiments;firstly,the CEC2017 benchmark set is adopted to evaluate the performance of GCMFO in tackling high-dimensional and multimodal ***,GCMFO is applied to handle multilevel thresholding image segmentation *** last,GCMFO is integrated into kernel extreme learning machine classifier to deal with three medical diagnosis cases,including the appendicitis diagnosis,overweight statuses diagnosis,and thyroid cancer *** results and discussions show that the proposed approach outperforms the original MFO and other state-of-the-art algorithms on both convergence speed and *** also indicates that the presented GCMFO has a promising potential for application.

关键词： nature-inspired algorithm moth-flame optimization generalized opposition-based learning crisscross search medical diagnosis

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A Method Based on Plants Light Absorption Spectrum and Its Use for Data Clustering

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Journal of Bionic Engineering 2024年第6期21卷 3004-3040页

作者： Behnam Farnad Kambiz Majidzadeh Mohammad Masdari Amin Babazadeh Sangar Department of Computer Engineering Urmia BranchIslamic Azad UniversityUrmia57169-63896Iran

nature-inspired optimization algorithms refer to techniques that simulate the behavior and ecosystem of living organisms or natural *** such technique is the“Photosynthesis Spectrum algorithm,”which was developed by mimicking the process by which photons behave as a population in *** optimization technique has three stages that mimic the structure of leaves and the fluorescence *** stage updates the fitness of the solution by using a mathematical equation to direct the photon to the reaction *** stages of testing have been conducted to test the efficacy of this *** the first stage,functions from the CEC 2019 and CEC 2021 competitions are used to evaluate the performance and convergence of the proposed *** statistical results from non-parametric Friedman and Kendall’s W tests show that the proposed method is superior to other methods in terms of obtaining the best average of solutions and achieving stability in finding *** other sections,the experiment is designed for data *** proposed method is compared with recent data clustering and classification metaheuristic algorithms,indicating that this method can achieve significant performance for clustering in less than 10 s of CPU time and with an accuracy of over 90%.

关键词： Data clustering Photosynthesis spectrum algorithm nature-inspired algorithm Metaheuristic

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CQFFA:A Chaotic Quasi-oppositional Farmland Fertility algorithm for Solving Engineering Optimization Problems

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Journal of Bionic Engineering 2023年第1期20卷 158-183页

作者： Farhad Soleimanian Gharehchopogh Mohammad H.Nadimi-Shahraki Saeid Barshandeh Benyamin Abdollahzadeh Hoda Zamani Department of Computer Engineering Urmia BranchIslamic Azad UniversityUrmia969Iran Faculty of Computer Engineering Najafabad BranchIslamic Azad UniversityNajafabad517Iran Big Data Research Center Najafabad BranchIslamic Azad UniversityNajafabad517Iran Afagh Higher Education Institute Urmia969Iran

Farmland Fertility algorithm(FFA)is a recent nature-inspired metaheuristic algorithm for solving optimization ***,FFA has some drawbacks:slow convergence and imbalance of diversification(exploration)and intensification(exploitation).An adaptive mechanism in every algorithm can achieve a proper balance between exploration and *** literature shows that chaotic maps are incorporated into metaheuristic algorithms to eliminate these ***,in this paper,twelve chaotic maps have been embedded into FFA to find the best numbers of prospectors to increase the exploitation of the best promising ***,the Quasi-Oppositional-Based Learning(QOBL)mechanism enhances the exploration speed and convergence rate;we name a CQFFA *** improvements have been made in line with the weaknesses of the FFA algorithm because the FFA algorithm has fallen into the optimal local trap in solving some complex problems or does not have sufficient ability in the intensification *** results obtained show that the proposed CQFFA model has been significantly *** is applied to twenty-three widely-used test functions and compared with similar state-of-the-art algorithms statistically and ***,the CQFFA algorithm has evaluated six real-world engineering *** experimental results showed that the CQFFA algorithm outperforms other competitor algorithms.

关键词： nature-inspired algorithm Farmland fertility algorithm Chaotic maps Quasi Engineering optimization problems

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A survey on firefly algorithms

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NEUROCOMPUTING 2022年 500卷 662-678页

作者： Li, Jun Wei, Xiaoyu Li, Bo Zeng, Zhigao Wuhan Univ Sci & Technol Coll Comp Sci & Technol Wuhan 430065 Hubei Peoples R China Hubei Prov Key Lab Intelligent Informat Proc & Rea Wuhan 430065 Hubei Peoples R China Hunan Univ Technol Coll Comp Sci Zhuzhou 412000 Hunan Peoples R China

Firefly algorithm (FA) is one of the popular algorithms of Swarm Intelligence domain. The literature has expanded significantly in the past few years. This paper provides a timely review of the FA and its new variants. The different variants of FA are classified and analyzed. The applications and case studies in the rapidly evolving domain are also reviewed and summarized in detail here. The problems to be further solved and future research directions of firefly algorithms will be given at the end of this paper. (c) 2022 Elsevier B.V. All rights reserved.

关键词： Firefly algorithm Swarm Intelligence nature-inspired algorithm Optimization Applications

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Coral Reef Optimization with substrate layers for medical Image Registration

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SWARM AND EVOLUTIONARY COMPUTATION 2018年 42卷 138-159页

作者： Bermejo, Enrique Chica, Manuel Damas, Sergio Salcedo-Sanz, Sancho Cordon, Oscar Univ Granada Dept Comp Sci & Artificial Intelligence E-18071 Granada Spain Univ Newcastle Sch Elect Engn & Comp Callaghan NSW 2308 Australia Univ Granada Dept Software Engn E-18071 Granada Spain Univ Alcala Dept Signal Theory & Commun Alcala De Henares 28805 Spain Univ Granada Res Ctr Informat & Commun Technol E-18071 Granada Spain

In medical imaging there is a special interest in relating information from different images frequently used for diagnosis or treatment. Image registration (IR) involves the transformation of different sets of image data having a shared content into a common coordinate system. The estimation of the optimal transformation is modelled either as a combinatorial or a numerical optimization problem. Since traditional IR methods are constrained by several limitations, other optimization methods have been recently proposed to overcome such shortcomings. In this contribution, we consider the use of a recently proposed and high performance bio-inspired meta-heuristic: the Coral Reef Optimization algorithm with Substrate Layers (CRO-SL). We adapt the algorithm to the real coding IR problem variant following both feature-based and intensity-based designs, and perform two thorough experimental studies. Such studies focus on both mono-modal and inter-modal scenarios where the images suffer different types of 3D affine transformations to validate our proposal. The new proposal is benchmarked with state-of-the-art evolutionary and non-evolutionary IR methods. The results show that CRO-SL is a very competitive approach in terms of its robustness, accuracy, and efficiency.

关键词： Image Registration Medical imaging Coral Reef Optimization nature-inspired algorithm Swarm intelligence

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The Challenge for the nature-inspired Global Optimization algorithms: Non-Symmetric Benchmark Functions

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IEEE ACCESS 2021年 9卷 106317-106339页

作者： Gao, Zheng-Ming Zhao, Juan Hu, Yu-Rong Chen, Hua-Feng Jingchu Univ Technol Sch Comp Engn Jingmen 448000 Peoples R China Jingchu Univ Technol Sch Elect & Informat Engn Jingmen 448000 Peoples R China Jingchu Univ Technol Dept Sci & Technol Jingmen 448000 Peoples R China

Along with the increasing number of nature-inspired algorithms, more and more benchmark functions were also involved in the initial verification experiments. The benchmark functions were introduced to verify the capability of algorithms in optimization, but not all of them could be optimized, because they were different from each other in dimensionality, separability, scalability, and modality ***.. In this paper, we introduced another property called symmetry or non-symmetry, which should be another embedded characteristic of functions affecting the capability of algorithms in optimization. 67 non-symmetric benchmark functions were collected and 9 popular capability-verified algorithms were introduced in four types of simulation experiments. Experimental results show that most of the non-symmetric algorithms could not be optimized. And none of the algorithms involved could optimize them all. Efforts remain in need of new methods and improvements of nature-inspired algorithms.

关键词： Benchmark testing Optimization Classification algorithms Genetic algorithms Scalability Rabbits Mathematical model nature-inspired algorithm optimization algorithms benchmark functions non-symmetry

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Packing algorithm inspired by gravitational and electromagnetic effects

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WIRELESS NETWORKS 2020年第8期26卷 5631-5644页

作者： Martinez-Rios, Felix Murillo-Suarez, Alfonso Univ Panamer Mexico City DF Mexico

This paper introduces a faster and more efficient algorithm for solving a two-dimension packing problem. This common optimization problem takes a set of geometrical objects and tries to find the best form of packing them in a space with specific characteristics, called container. The visualization of nanoscale electromagnetic fields was the inspiration for this new algorithm, using the electromagnetic field between the previously placed objects, this paper explains how to determine the best positions for to place the remaining ones. Two gravitational phenomena are also simulated to achieve better results: shaken and gravity. They help to compact the objects to reduce the occupied space. This paper shows the executions of the packing algorithm for four types of containers: rectangles, squares, triangles, and circles.

关键词： Optimization Packing problem Electromagnetic fields nature-inspired algorithm Gravitational algorithms

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GGSA: A Grouping Gravitational Search algorithm for data clustering

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2014年 36卷 114-121页

作者： Dowlatshahi, Mohammad Bagher Nezamabadi-pour, Hossein Lorestan Univ Dept Comp Engn Khorramabad Iran Shahid Bahonar Univ Kerman Dept Elect Engn Kerman Iran

Gravitational Search algorithm (GSA) is a stochastic population-based metaheuristic designed for solving continuous optimization problems. It has a flexible and well-balanced mechanism for enhancing exploration and exploitation abilities. In this paper, we adapt the structure of GSA for solving the data clustering problem, the problem of grouping data into clusters such that the data in each cluster share a high degree of similarity while being very dissimilar to data from other clusters. The proposed algorithm, which is called Grouping GSA (GGSA), differs from the standard GSA in two important aspects. First, a special encoding scheme, called grouping encoding, is used in order to make the relevant structures of clustering problems become parts of solutions. Second, given the encoding, special GSA updating equations suitable for the solutions with grouping encoding are used. The performance of the proposed algorithm is evaluated through several benchmark datasets from the well-known UCI Machine Learning Repository. Its performance is compared with the standard GSA, the Artificial Bee Colony (ABC), the Particle Swarm Optimization (PSO), the Firefly algorithm (FA), and nine other well-known classical classification techniques from the literature. The simulation results indicate that GGSA can effectively be used for multivariate data clustering. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Gravitational Search algorithm Grouping encoding Data clustering nature-inspired algorithm Swarm Intelligence

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CL-IoT: cross-layer Internet of Things protocol for intelligent manufacturing of smart farming

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JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2021年第7期12卷 7777-7791页

作者： Mahajan, Hemant B. Badarla, Anil Junnarkar, Aparna A. Univ Technol Jaipur Rajasthan India Univ Technol Comp Sci & Engn Dept Jaipur Rajasthan India PES Modern Coll Engn Pune Maharashtra India

Internet of Things (IoT) for Intelligent Manufacturing of Smart Farming gained significant attention from researchers to automate various farming applications called Smart Farming (SF). The sensors and actuators deployed across the farm using which farmers receive periodic farm information related to temperature, soil moisture, light intensity, and water used, etc. The clustering-based methods are proven energy-efficient solutions for Wireless Sensor Networks (WSNs). However, by considering long-distance communications and scalable networks of IoT enabled SF;the present clustering solutions cannot be feasible and having higher delay and latency for various SF applications. To focus on requirements SF applications, an efficient and scalable protocol for remote monitoring and decision making of farms in rural regions called CL-IoT protocol proposed. A cross-layer-based clustering and routing algorithms have designed to reduce network communication delay, latency, and energy consumption. The cross-layer-based optimal Cluster Head (CH) selection solution proposed to overcome the energy asymmetry problem in WSN. The parameters of different layers like a physical, medium access control (MAC), and network layer of each sensor used to evaluate and select optimal CH and efficient data transmission. The nature-inspired algorithm proposed with a novel probabilistic decision rule functions as a fitness function to discover the optimal route for data transmission. The performance of the CL-IoT protocol analyzed using NS2 by considering the energy-efficiency, computational-efficiency, and QoS-efficiency factors. Compared to state-of-art IoT-based farming methods, the CL-IoT reduces energy consumption, communication overhead, and end-to-end delay up to a certain extent and maximizes the network throughput.

关键词： Cross-layer Clustering Intelligent manufacturing nature-inspired algorithm Smart farming Internet of Things

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