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Hybridized dragonfly and Jaya algorithm for optimal sensor node location identification in mobile wireless sensor networks

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JOURNAL OF SUPERCOMPUTING 2023年第15期79卷 16940-16962页

作者： Khedr, Ahmed M. Rani, S. Sheeja Saad, Mohamed Univ Sharjah Dept Comp Sci Sharjah 27272 U Arab Emirates Univ Sharjah Dept Comp Engn Sharjah 27272 U Arab Emirates Zagazig Univ Math Dept Zagazig Egypt

A wireless sensor network (WSN) consists of an extensive number of low-power sensor nodes to gather information from their environment and monitor physical activities. This makes node localization a crucial aspect in most WSN applications since measurement data is worthless unless the location from where the data is acquired is known precisely. The majority of localization solutions rely on anchor nodes for estimating the node locations with different localization accuracy, complexity, and hence different applicability. But, the cost and complexity in the localization of large-scale WSNs are not significantly reduced. In this paper, a novel Hybridized dragonfly and Jaya Optimization technique (HyDAJ) is introduced for improving localization accuracy and performance of mobile WSNs. The proposed hybrid technique combines the advantages of dragonfly algorithm and Jaya algorithm to localize the sensor nodes in a more efficient way and overcomes the limitations of the original algorithm. The hybrid algorithm verifies that all target nodes are precisely localized with higher accuracy. Simulation results reveal that HyDAJ outperforms existing methods under multiple metrics including localization efficiency, mean localization error, computation time, and energy consumption.

关键词： Mobile WSN Sensor node location identification Time of flight (ToF) Received signal strength indicator (RSSI) dragonfly algorithm Jaya optimization

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Training of the feed forward artificial neural networks using dragonfly algorithm

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APPLIED SOFT COMPUTING 2022年 124卷

作者： Gulcu, Saban Necmettin Erbakan Univ Comp Engn Dept Konya Turkey

One of the most important parts of an artificial neural network (ANN) which affects performance is training algorithms. Training algorithms optimize the weights and biases of the ANN according to the inputs-outputs pattern. Two types of training algorithms are widely used: Gradient methods and meta-heuristic methods. Gradient methods are effective in training the ANN. But they have some disadvantages. The main disadvantage of gradient methods is premature convergence. Secondly, the performance of gradient methods highly depends on the initial parameters and positions. Thirdly, they can easily get stuck in local optima. To overcome these disadvantages, this article presents a new hybrid algorithm (DA-MLP) to train the feed-forward multilayer neural networks (MLP) using the dragonfly algorithm. The dragonfly algorithm optimizes the weights and biases of the MLP. In the experiments, one real-world problem in the civil engineering area and eight classification datasets were used. To verify the success of the DA-MLP algorithm, the results of the DA-MLP algorithm were compared with the results of four algorithms (the BAT-MLP based on the bat optimization algorithm, the SMS-MLP based on the states of matter search optimization algorithm, the PSO-MLP based on the particle swarm optimization algorithm, and the backpropagation (BP) algorithm). The experimental study showed that the DA-MLP algorithm is more efficient than the other algorithms. (c) 2022 Elsevier B.V. All rights reserved.

关键词： Artificial neural networks Training of artificial neural networks dragonfly algorithm Optimization Multilayer perceptron

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A Hybrid dragonfly algorithm for Efficiency Optimization of Induction Motors

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SENSORS 2022年第7期22卷 2594页

作者： Shukla, Niraj Kumar Srivastava, Rajeev Mirjalili, Seyedali Shambhunath Inst Engn & Technol Dept Elect Engn Prayagraj 211015 India Univ Allahabad JK Inst Appl Phys & Technol Dept Elect & Commun Prayagraj 211002 India Torrens Univ Australia Ctr Artificial Intelligence Res & Optimizat Brisbane Qld 4006 Australia Yonsei Univ Yonsei Frontier Lab Seoul 03722 South Korea

Induction motors tend to have better efficiency on rated conditions, but at partial load conditions, when these motors operate on rated flux, they exhibit lower efficiency. In such conditions, when these motors operate for a long duration, a lot of electricity gets consumed by the motors, due to which the computational cost as well as the total running cost of industrial plant increases. Squirrel-cage induction motors are widely used in industries due to their low cost, robustness, easy maintenance, and good power/mass relation all through their life cycle. A significant amount of electrical energy is consumed due to the large count of operational units worldwide;hence, even an enhancement in minute efficiency can direct considerable contributions within revenue saving, global electricity consumption, and other environmental facts. In order to improve the efficiency of induction motors, this research paper presents a novel contribution to maximizing the efficiency of induction motors. As such, a model of induction motor drive is taken, in which the proportional integral (PI) controller is tuned. The optimal tuning of gains of a PI controller such as proportional gain and integral gain is conducted. The tuning procedure in the controller is performed in such a condition that the efficiency of the induction motor should be maximum. Moreover, the optimization concept relies on the development of a new hybrid algorithm, the so-called Scrounger Strikes Levy-based dragonfly algorithm (SL-DA), that hybridizes the concept of dragonfly algorithm (DA) and group search optimization (GSO). The proposed algorithm is compared with particle swarm optimization (PSO) for verification. The analysis of efficiency, speed, torque, energy savings, and output power is validated, which confirms the superior performance of the suggested method over the comparative algorithms employed.

关键词： induction motor PI controller speed control dragonfly algorithm group search optimizer particle swarm optimization algorithm meta-heuristic

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Dynamic opposite learning enhanced dragonfly algorithm for solving large-scale flexible job shop scheduling problem

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KNOWLEDGE-BASED SYSTEMS 2022年第0期238卷 107815-107815页

作者： Yang, Dongsheng Wu, Mingliang Li, Di Xu, Yunlang Zhou, Xianyu Yang, Zhile Northeastern Univ Intelligent Elect Sci & Technol Res Inst Shenyang Peoples R China South China Univ Technol Sch Mech & Automot Engn Guangzhou Peoples R China Fudan Univ Sch Microelect State Key Lab ASIC & Syst Shanghai Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen Peoples R China

Flexible job shop scheduling problem (FJSP) has attracted many research interests, in particular for meta-heuristic algorithm (MA) developers due to the superior optimization performance. dragonfly algorithm (DA) is one of recent and popular MA approaches. However, it is inevitable for DA to be trapped into local optima, especially when dealing with the complex large-scale flexible job shop scheduling problem (LSFJSP). In this paper, an improved DA, adopting a dynamic opposite learning (DOL) strategy, is proposed (namely DOLDA) to solve the LSFJSP. DOL strategy is embedded into the population initialization stage and the generation jumping stage to raise the search ability of DA. This paper uses a T-test to testify whether there are differences between the proposed algorithm and other comparison algorithms, comparison results indicate that DOLDA shows noticeable differences with other algorithms that explain the effectiveness and innovation of the proposed algorithm. The jump rate is an important parameter that determines the probability of algorithms escaping from the algorithms' local optimal solution. This paper also considers the jump rate analysis experiments to maximize the power of the DOLDA. 28 test functions from CEC 2013 and CEC 2014 are applied to verify the performance of DOLDA, test results reveal that DOLDA owns strong search ability in coping with almost all test functions. The DOLDA also is applied to solve 15 LSFJSP instances generated by Brandimate rule, the results obtained show that DOLDA can efficiently achieve a better solution on the LSFJSP compare to compared algorithms.& nbsp;(c) 2021 Elsevier B.V. All rights reserved.

关键词： Large-scale flexible job shop scheduling & nbsp problem dragonfly algorithm Dynamic opposite learning Global optimization Makespan

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Mathematical modeling and dragonfly algorithm for optimizing sustainable agritourism supply chains

Journal of Engineering Research (Kuwait)

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Journal of Engineering Research (Kuwait) 2024年

作者： Zhang, Gongwang Chen, Chun-Nan Shokouhifar, Mohammad Goli, Alireza Hangzhou Administrative College Zhejiang Hangzhou 310024 China College of Management Chang Jung Christian University Tainan 711301 Taiwan Department of Electrical and Computer Engineering Shahid Beheshti University Tehran *** Iran Department of Industrial Engineering and Future Studies Faculty of Engineering University of Isfahan Iran

The management of agritourism supply chains plays a pivotal role in promoting sustainable rural development and cultivating economic growth within agricultural communities. The study devises a comprehensive methodology aimed at designing and optimizing agritourism supply chain networks. To achieve this purpose, a two-objective mixed-integer linear programming mathematical model is introduced, tailored to accommodate diverse water consumption priorities across different gardens within the chain. Recognizing the NP-hardness of this intricate problem, a metaheuristic-driven solution approach based on MIN-MAX goal programming and dragonfly algorithm is presented to solve the designed model, aiming to simultaneously maximize the total profit and minimize overall water consumption within the supply chain. To assess the effectiveness of the proposed methodology, we adopted a real-world case study of an agritourism supply chain in China. The obtained results indicate the adoption of the proposed method can efficiently lead to the development and mechanization of agriculture in villages. The numerical analysis highlights that recruitment and production costs significantly influence the total profit of the agritourism supply chain. Additionally, the research underscores the pivotal role of sustainable rural development in creating job opportunities within the agritourism supply chain, thereby stimulating economic growth and fostering more profitable enterprises. © 2024 The Authors

关键词： dragonfly algorithm Mathematical modeling Optimization Supply chain network design Sustainability Water consumption

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BMDA: applying biogeography-based optimization algorithm and Mexican hat wavelet to improve dragonfly algorithm

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SOFT COMPUTING 2020年第21期24卷 15979-16004页

作者： Shirani, Mohammad Reza Safi-Esfahani, Faramarz Islamic Azad Univ Fac Comp Engn Najafabad Branch Najafabad Iran Islamic Azad Univ Big Data Res Ctr Najafabad Branch Najafabad Iran

One of the methods for solving optimization problems is applying metaheuristic algorithms that find near to optimal solutions. dragonfly algorithm is one of the metaheuristic algorithms which search problem space by the inspiration of hunting and emigration behavior of dragonflies in nature. However, it suffers from the premature convergence of the population to an undesirable point in the detection ability (global search). In this research, an improved dragonfly algorithm called BMDA (applying Biogeography-based algorithm, Mexican hat wavelet, and dragonfly algorithm) is presented to resolve the premature convergence in high workloads by creating a mutation phase based on the combination of the biogeography-based optimization (BBO) migration process and the Mexican hat wavelet transform in dragonfly algorithm (DA). The algorithm was evaluated for the mean error in comparison with standard dragonfly algorithm (DA), Memory-based Hybrid dragonfly algorithm (MHDA), chaotic dragonfly algorithm version 9 (CDA9), Adaptive_DA algorithm, bat algorithm (BAT), particle swarm optimization algorithm (PSO), raven roosting optimization (RRO) and whale optimization algorithm (WOA) using the CEC2017 benchmark functions. The implementation results of the proposed BMDA algorithm applying different benchmark functions outweighed the DA-based algorithm, MHDA algorithm, CDA9 algorithm, Adaptive_DA algorithm, BAT algorithm, PSO algorithm, RRO, and WOA algorithms in terms of mean error.

关键词： dragonfly algorithm Biogeography-based optimization algorithm Mexican hat wavelet

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IBDA: Improved Binary dragonfly algorithm With Evolutionary Population Dynamics and Adaptive Crossover for Feature Selection

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IEEE ACCESS 2020年 8卷 108032-108051页

作者： Li, Jiahui Kang, Hui Sun, Geng Feng, Tie Li, Wenqi Zhang, Wei Ji, Bai Jilin Univ Coll Comp Sci & Technol Changchun 130012 Peoples R China Jilin Univ Key Lab Symbol Computat & Knowledge Engn Minist Educ Changchun 130012 Peoples R China Jilin Univ Hosp 1 Dept Hepatobiliary & Pancreat Surg Changchun 130021 Peoples R China

Feature selection is an effective method to eliminate irrelevant, redundant and noisy features, which improves the performance of classification and reduces the computational burden in machine learning. In this paper, an improved binary dragonfly algorithm (IBDA) which extends from the conventional dragonfly algorithm (DA) is proposed as a search strategy to design a wrapper-based feature selection method. First, a novel evolutionary population dynamics (EPD) strategy is introduced in IBDA to enhance the exploitation ability while ensuring population diversity of the algorithm. Second, IBDA proposes a novel crossover operator which establishes connections between the crossover rates and iterations so that making the algorithm can adjust the crossover rates of solutions dynamically, thereby balancing the exploitation and exploration of the algorithm. Finally, a binary mechanism is proposed to make the algorithm suitable for the binary feature selection problems. Simulations are conducted on 27 classical datasets from the UC Irvine Machine Learning Repository, and the results demonstrate that the proposed IBDA has better performance than some other comparison algorithms. Moreover, the effectiveness and performance of the proposed improved factors are evaluated by tests.

关键词： Feature extraction Heuristic algorithms Optimization Machine learning algorithms Machine learning Particle swarm optimization Sociology Feature selection classification bio-inspired computing dragonfly algorithm

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The importance of reconfiguration of the distribution network to achieve minimization of energy losses using the dragonfly algorithm

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e-Prime - Advances in Electrical Engineering, Electronics and Energy 2023年 5卷

作者： Rahmati, Katayun Taherinasab, Sina Department of Electrical and Computer Engineering Islamic Azad University Gorgan Iran Department of Electrical and Computer Engineering Institute of Higher Education Fakhruddin As'ad Gorgani Gorgan Iran

In this article, a method for reconfiguring the distribution network with the presence of production sources to minimize energy losses is presented. Also, besides the reconfiguration issue, the Super distribution tap transformers and the power factor of resources in the network will have their optimal values based on the amount of energy consumption as factors affecting the number of losses. According to the stated parameters, the optimization problem in this article is a non-linear problem with a large search space, for which the dragonfly optimization algorithm was used. To make the results realistic, all types of loads, including commercial, residential and office, have been considered in 24 h and two seasons of the year, cold and hot. The program was prepared with the help of MATLAB software on a sample network of 16 buses, and its applications and results have been evaluated. The obtained results show that determining the optimal reconfiguration is very effective in reducing network losses because, with this method, the amount of losses and unsupplied energy of network subscribers has been reduced significantly. Also, by coordinating the reconfiguration with tap transformers and the power factor of distributed generation resources, the loss reduction rate can be made more effective. The results obtained are compared with the genetic algorithm and the results show the superiority of the dragonfly optimization method. © 2023 The Author(s)

关键词： Distribution network reconfiguration dragonfly algorithm Genetic algorithm Network loss reduction Power transformer

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An improved opposition based learning firefly algorithm with dragonfly algorithm for solving continuous optimization problems

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INTELLIGENT DATA ANALYSIS 2020年第2期24卷 309-338页

作者： Abedi, Mehdi Gharehchopogh, Farhad Soleimanian Islamic Azad Univ Dept Comp Engn Urmia Branch Orumiyeh Iran

Nowadays, the existence of continuous optimization problems has led researchers to come up with a variety of methods to solve continues optimization problems. The metaheuristic algorithms are one of the most popular and common ways to solve continuous optimization problems. Firefly algorithm (FA) is a successful metaheuristic algorithm for solving continuous optimization problems;however, although this algorithm performs very well in local search, it has weaknesses and disadvantages in finding solution in global search. This problem has caused this algorithm to be trapped locally and the balance between exploration and exploitation cannot be well maintained. In this paper, three different approaches based on the dragonfly algorithm (DA) processes and the OBL method are proposed to improve exploration, performance, efficiency and information-sharing of the FA and to avoid the FA getting stuck in local trap. In the first proposed method (FADA), the robust processes of DA are used to improve the exploration, performance and efficiency of the FA;and the second proposed method (OFA) uses an Opposition-Based Learning (OBL) algorithm to accelerate the convergence and exploration of the FA. Finally, in the third approach, which is referred to as OFADA in this paper, a hybridization of the hybrid FADA and the OBL method is used to improve the convergence and accuracy of the FA. The three proposed methods were implemented on functions with 2, 4, 10, and 30 dimensions. The results of the implementation of these three proposed methods showed that OFADA approach outperformed the other two proposed methods and other compared metaheuristic algorithms in different dimensions. In addition, all the three proposed methods provided better results compared with other metaheuristic algorithms on smalldimensional functions. However, performance of many metaheuristic algorithms decreased with increasing the dimensions of the functions. While the three proposed methods, in particular the OF

关键词： Opposition based learning dragonfly algorithm firefly algorithm continuous optimization problem optimization

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A Hybrid Improved dragonfly algorithm for Feature Selection

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IEEE ACCESS 2020年 8卷 155619-155629页

作者： Cui, Xueting Li, Ying Fan, Jiahao Wang, Tan Zheng, Yuefeng Jilin Univ Coll Comp Sci & Technol Changchun 130012 Peoples R China Jilin Univ Key Lab Symbol Computat & Knowledge Engn Minist Educ Changchun 130012 Peoples R China Space Technol Jilin Co Ltd Changchun 130000 Peoples R China Jilin Normal Univ Comp Coll Siping 136000 Peoples R China

Feature selection, which eliminates irrelevant and redundant features, is one of the most efficient classification methods. However, searching for an optimal subset from the original set is still a challenging problem. This paper proposes a novel feature selection algorithm named hybrid improved dragonfly algorithm (HIDA) which combines the advantages of both mRMR and improved dragonfly algorithm (IDA) in order to generate promising candidate subset and achieve higher classification accuracy rate. Firstly, to generate promising subset, features with small weight have chance to be selected into candidate subset with a small probability in mRMR. Secondly, to balance the exploitation and exploration capabilities of IDA, dynamic swarming factors are proposed to balance global and local capability. Lastly, to enhance the exploitation capability of IDA, quantum local optimum and global optimum are introduced in the position updating mechanism. The performance of HIDA is investigated on ten gene expression datasets and eight UCI data sets from the UCI Machine Learning Data Repository. Results show that the performance of HIDA is superior to BBA, BDA, CDA, LBPSO, MPMDWOA and MSMCCS.

关键词： Feature selection hybrid optimization dragonfly algorithm mRMR classification

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