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Quasi Oppositional dragonfly algorithm for Load Balancing in Cloud Computing Environment

WIRELESS PERSONAL COMMUNICATIONS 2022年第3期122卷 2639-2656页

作者： Latchoumi, T. P. Parthiban, Latha SRM Inst Sci & Technol Dept Comp Sci & Engn Chennai Tamil Nadu India Pondicherry Univ Community Coll Dept Comp Sci Pondicherry India

In cloud computing (CC), load balancing tasks remain a critical problem in distributing resources from a data center. Ensure that every virtual machine (VM) has a balanced load to maximize capacity utilization. In the CC world, load balancing is a Non-Polynomial (NP) problem resolved with metaheuristic algorithms. A new Quasi-Oppositional dragonfly algorithm for Load Balancing (QODA-LB) has been developed to obtain optimum resource scheduling in a CC configuration. The proposed QODA-LB algorithm uses three variables to calculate an objective function: execution time, execution cost, and charge. The QODA-LB algorithm assigns tasks to VM according to its potential and the resulting objective function. Also, the QODA-LB algorithm employs the Quasi-Oppositional Based Learning principle to increase the standard convergence rate of the dragonfly (DA) algorithm. A complete series of experiments were conducted, and the results were analyzed in various ways to ensure the increased efficiency of the QODA-LB algorithm. Simulation results demonstrated an optimal load balancing efficiency and outperformed key approaches.

关键词： Cloud computing Load scheduling dragonfly algorithm Oppositional based learning

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Wrapper scan chain design algorithm for testing of embedded cores based on chaotic dragonfly algorithm

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EVOLUTIONARY INTELLIGENCE 2022年第1期15卷 369-379页

作者： Zhou, Tian Hu, Cong Zhu, Aijun Xu, Chuanpei Wan, Chunting Guilin Univ Aerosp Technol Sch Elect Informat & Automat Guilin 541004 Peoples R China Guilin Univ Elect Technol Sch Elect Engn & Automat Guilin 541004 Peoples R China

As embedded pre-designed and pre-validated cores in system-on-chip (SoC) designs have increased usage, the wrapper scan chain design (WSCD) for the embedded cores is one of the fundamental ways to reduce the SoC test time. In this paper, a chaotic dragonfly algorithm (CDA) for WSCD is proposed to minimize the test time of embedded cores by balancing the packaged scan chains (WSCs).Since the WSCD problem is non-continuous, we improve the dragonfly algorithm (DA) with integer coding to make it suitable for the WSCD problem. In order to improve population diversity and prevent falling into local optimum state, we introduce chaotic strategy into DA. Furthermore, a repaired operator that considers the specific knowledge is added to the DA. Since the CDA is a swarm intelligence method, it is expected to effectively solve the NP-hard problem. The experimental results on ITC'02 SoC benchmark show that the proposed algorithm can improve the balanced results and shorten the test time compared with the existing algorithms.

关键词： SoC testing Wrapper scan chain design Chaotic map dragonfly algorithm

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Researches on crane scheduling based on improved dragonfly algorithm considering customer satisfaction 4

Researches on crane scheduling based on improved dragonfly a...

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4th International Conference on Informatics Engineering and Information Science (ICIEIS)

作者： Yang, Wen Q. Su, Jian X. Nie, Fu Q. Nie, Yu X. Kong, Xiao H. Li, Yong F. Yang, Zhi L. Du, Liu F. Xu, Jun P. Henan Inst Sci & Technol Sch Mech & Elect Engn Xinxiang 453003 Henan Peoples R China Zhengzhou Univ Int Coll Zhengzhou 451000 Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen 518055 Peoples R China

ISBN: (数字)9781510651999

ISBN: (纸本)9781510651999;9781510651982

Aimed at increasing the customer satisfaction of yard warehousing, a model of crane scheduling that is used to minimize the total level of disappointment of all customers is established, and resolved by an improved dragonfly algorithm (IDA) proposed, which overcomes the premature convergence and slow convergent speed of standard dragonfly algorithm by introducing Gaussian neighborhood radius and multiple mating operator. They can balance the global exploration and the local exploitation. Compared with other popular algorithms, simulation results demonstrate that IDA achieves high search efficiency and accuracy.

关键词： Crane scheduling dragonfly algorithm customer satisfaction gaussian neighborhood radius multiple mating operator

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Wind driven dragonfly algorithm for global optimization

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2021年第6期33卷 e6054-e6054页

作者： Zhong, Lianlian Zhou, Yongquan Luo, Qifang Zhong, Keyu Guangxi Univ Nationalities Coll Artificial Intelligence Nanning 530006 Peoples R China Guangxi High Sch Key Lab Complex Syst & Computat Nanning Peoples R China Guangxi Key Labs Hybrid Computat & IC Design Anal Nanning Peoples R China

dragonfly algorithm (DA) is a new swarm intelligence optimization algorithm based on the static and dynamic swarm behavior of dragonflies. The algorithm has the characteristics of simple structure, strong search ability, easy implementation, and strong robustness. However, the DA algorithm itself also has insufficient solution accuracy and slow convergence speed. The Wind Driven Optimization algorithm (WDO) has the characteristics of fast convergence speed and strong global search capability. So as to improve the optimization performance of the DA algorithm and avoid premature convergence, the speed of the WDO is introduced into the later calculation of the algorithm iteration, which speeds up the convergence speed of the global optimal solution. This paper proposes a dragonfly algorithm based on wind driven (WDDA), that is to reduce the blindness of the dragonfly algorithm search, improve the solution accuracy and convergence speed, to improve the overall optimization performance of the algorithm. The 23 benchmark test functions and one engineering example for optimization and comparison experiments. The experimental results show that WDDA algorithm has better performance in function optimization.

关键词： dragonfly algorithm engineering optimization function optimization metaheuristic algorithm wind driven dragonfly algorithm

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Dynamic scheduling of tasks in cloud computing applying dragonfly algorithm, biogeography-based optimization algorithm and Mexican hat wavelet

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JOURNAL OF SUPERCOMPUTING 2021年第2期77卷 1214-1272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

Cloud computing maps tasks to resources in a scalable fashion. The scheduling is an NP-hard problem;thus, the scheduler chooses one solution from among many. This is the reason why finding the best optimal solution, especially at a high scale of the system, is not possible. Applying metaheuristic algorithms to find a near-to-optimal solution, not the best one, could be the right approach. dragonfly metaheuristic algorithm explores and exploits a solution space by the inspiration of hunting and emigration behavior of dragonflies in nature. But it suffers from the premature convergence of the algorithm to an undesirable when explores the solution space. In this research, an improved dragonfly algorithm (applying biogeography-based algorithm, Mexican hat wavelet and dragonfly algorithm-BMDA) is presented to resolve the premature convergence by applying a mutation phase that is the combination of the biogeography-based optimization (BBO) migration process and Mexican hat wavelet transform in dragonfly algorithm. Then, it is applied for dynamically scheduling tasks under the BMDDSF framework (BBO-Mexican hat wavelet-dragonfly dynamic scheduling framework) in the cloud computing environment. The purpose is customizing a metaheuristic algorithm to be applied in the resource manager of cloud computing to improve its performance. The BMDA algorithm was firstly evaluated for the mean error in comparison with the baseline algorithms using the CEC2017 benchmark functions. Then, the performance of the BMDDSF framework in cloud computing was tested using NASA parallel workload compared with baseline methods in terms of execution time, response time and reduction of service-level agreement violation. The experiments showed that the presented method outweighed the baseline approaches.

关键词： Task scheduling Cloud computing dragonfly algorithm Biogeography-based optimization algorithm Mexican hat wavelet

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dragonfly algorithm: a comprehensive review and applications

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NEURAL COMPUTING & APPLICATIONS 2020年第21期32卷 16625-16646页

作者： Meraihi, Yassine Ramdane-Cherif, Amar Acheli, Dalila Mahseur, Mohammed Univ MHamed Bougara Boumerdes LIST Lab Ave Independence Boumerdes 35000 Algeria Univ Versailles St Quentin En Yvelines LISV Lab 10-12 Ave Europe F-78140 Velizy Villacoublay France Univ MHamed Bougara Boumerdes LAA Lab Ave Independence Boumerdes 35000 Algeria Univ Sci & Technol Houari Boumediene Fac Elect & Informat Algiers 16025 Algeria

dragonfly algorithm (DA) is a novel swarm intelligence meta-heuristic optimization algorithm inspired by the dynamic and static swarming behaviors of artificial dragonflies in nature. It has proved its effectiveness and superiority compared to several well-known meta-heuristics available in the literature. This paper presents a comprehensive review of DA and its new variants classified into modified and hybrid versions. It also describes the main diverse applications of DA in several fields and areas such as machine learning, neural network, image processing, robotics, and engineering. Finally, the paper suggests some possible interesting research on the applications and hybridizations of DA for future works.

关键词： dragonfly algorithm Nature-inspired algorithm Swarm intelligence Meta-heuristics

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An adaptive resistance and stamina strategy-based dragonfly algorithm for solving engineering optimization problems

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ENGINEERING COMPUTATIONS 2021年第5期38卷 2228-2251页

作者： Yuan, Yongliang Wang, Shuo Lv, Liye Song, Xueguan Henan Polytech Univ Sch Mech & Power Engn Jiaozuo Henan Peoples R China Dalian Univ Technol Sch Mech Engn Dalian Peoples R China Zhejiang Sci Tech Univ Fac Mech Engn & Automat Hangzhou Peoples R China

Purpose Highly non-linear optimization problems exist in many practical engineering applications. To deal with these problems, this study aims to propose an improved optimization algorithm, named, adaptive resistance and stamina strategy-based dragonfly algorithm (ARSSDA). Design/methodology/approach To speed up the convergence, ARSSDA applies an adaptive resistance and stamina strategy (ARSS) to conventional dragonfly algorithm so that the search step can be adjusted appropriately in each iteration. In ARSS, it includes the air resistance and physical stamina of dragonfly during a flight. These parameters can be updated in real time as the flight status of the dragonflies. Findings The performance of ARSSDA is verified by 30 benchmark functions of Congress on Evolutionary Computation 2014's special session and 3 well-known constrained engineering problems. Results reveal that ARSSDA is a competitive algorithm for solving the optimization problems. Further, ARSSDA is used to search the optimal parameters for a bucket wheel reclaimer (BWR). The aim of the numerical experiment is to achieve the global optimal structure of the BWR by minimizing the energy consumption. Results indicate that ARSSDA generates an optimal structure of BWR and decreases the energy consumption by 22.428% compared with the initial design. Originality/value A novel search strategy is proposed to enhance the global exploratory capability and convergence speed. This paper provides an effective optimization algorithm for solving constrained optimization problems.

关键词： Adaptive resistance and stamina strategy Bucket wheel reclaimer dragonfly algorithm Optimization algorithm

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A hybrid memory-based dragonfly algorithm with differential evolution for engineering application

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ENGINEERING WITH COMPUTERS 2021年第4期37卷 2775-2802页

作者： Debnath, Sanjoy Baishya, Srimanta Sen, Debarati Arif, Wasim Natl Inst Technol Dept Elect & Commun Engn Silchar Assam India IIT Kharagpur GS Sanyal Sch Tele Commun Kharagpur W Bengal India

The dragonfly algorithm (DA) is a swarm-based stochastic algorithm which possesses static and dynamic behavior of swarm and is gaining meaningful popularity due to its low computational cost and fast convergence in solving complex optimization problems. However, it lacks internal memory and is thereby not able to keep track of its best solutions in previous generations. Furthermore, the solution also lacks in diversity and thereby has a propensity of getting trapped in the local optimal solution. In this paper, an iterative-level hybridization of dragonfly algorithm (DA) with differential evolution (DE) is proposed and named as hybrid memory-based dragonfly algorithm with differential evolution (DADE). The reason behind selecting DE is for its computational ability, fast convergence and capability in exploring the solution space through the use of crossover and mutation techniques. Unlike DA, in DADE the best solution in a particular iteration is stored in memory and proceeded with DE which enhances population diversity with improved mutation and accordingly increases the probability of reaching global optima efficiently. The efficiency of the proposed algorithm is measured based on its response to standard set of 74 benchmark functions including 23 standard mathematical benchmark functions, 6 composite benchmark function of CEC2005, 15 benchmark functions of CEC2015 and 30 benchmark function of CEC2017. The DADE algorithm is applied to engineering design problems such as welded beam deign, pressure vessel design, and tension/compression spring design. The algorithm is also applied to the emerging problem of secondary user throughput maximization in an energy-harvesting cognitive radio network. A comparative performance analysis between DADE and other most popular state-of-the-art optimization algorithms is carried out and significance of the results is deliberated. The result demonstrates significant improvement and prominent advantages of DADE compared to conventi

关键词： Optimization Evolutionary algorithms Differential evolution dragonfly algorithm Hybridization

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Clothing Image Classification with a dragonfly algorithm Optimised Online Sequential Extreme Learning Machine

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FIBRES & TEXTILES IN EASTERN EUROPE 2021年第3期29卷 90-95页

作者： Li, Jianqiang Shi, Weimin Yang, Donghe Zhejiang Sci Tech Univ Key Lab Modern Text Machinery & Technol Zhejiang Hangzhou 310018 Zhejiang Peoples R China Zhejiang Sci Tech Univ Sch Informat Sci & Technol Hangzhou 310018 Peoples R China

This study proposes a solution for the issue of the low classification accuracy of clothing images. Using Fashion-MNIST as the clothing image dataset, we propose a clothing image classification technology based on an online sequential extreme learning machine (OSELM) optimised by the dragonfly algorithm (DA). First, we transform the Fashion-MNIST dataset into a data set that we extract from the corresponding grey image. Then, considering that the input weight and hidden layer bias of an OSELM are generated randomly, a DA is proposed to optimise the input weight and hidden layer bias of the OSELM to reduce the influence of random generation on the classification results. Finally, the optimised OSELM is applied to the clothing image classification. Compared to the other seven types of classification algorithms, the proposed clothing image classification model with the DA-optimised OSELM reached 93.98% accuracy when it contained 350 hidden nodes. Its performance was superior to other algorithms that were configured with the same number of hidden nodes. From a stability analysis of the box-plot, it was found that there were no outliers exhibited by the DA-OSELM model, whereas some other models had outliers or had lower stability compared to the model proposed, thereby validating the efficacy of the solution proposed.

关键词： dragonfly algorithm Online Sequential Extreme Learning Machine clothing image classification optimised parameter

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Optimization of Construction Material Cost through Logistics Planning Model of dragonfly algorithm - Particle Swarm Optimization

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KSCE JOURNAL OF CIVIL ENGINEERING 2021年第7期25卷 2350-2359页

作者： Pham Vu Hong Son Nguyen Huynh Chi Duy Pham Ton Dat Ho Chi Minh City Univ Technol Vietnam Natl Univ Bach Khoa Univ Construct Engn & Management Dep Ho Chi Minh City 700000 Vietnam

Managing a construction project is challenging because of cost, time, safety, and quality considerations. In the most projects, the cost of construction is one of the most critical aspect because material cost alone accounts for significant ratio of the total project. Therefore, the cost of construction materials should be controlled. In this study, we proposed the use of material requirements planning (MRP) to control the cost of construction materials. After determining the demand for the materials required for construction, we estimated both the quantity of materials required and time taken to deliver the materials to the construction site. Although economic order quantity models have been applied to analyze construction material costs, they do not accurately reflect concerns related to material cost. Therefore, we used the material supply chain model (construction logistics planning) to analyze material costs. To optimize MRP according to the current progress of a project, a novel approach combining the dragonfly algorithm (DA) and particle swarm optimization algorithm (PSO) was proposed. To verify the advanced searchability of the DA-PSO algorithm, the algorithm was compared with the gray wolf and the genetic algorithms.

关键词： Construction logistics planning Optimizing material Inventory control dragonfly algorithm PSO algorithm Optimize delivery schedules Time to import construction materials

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