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Directionally-Enhanced Binary Multi-Objective Particle Swarm Optimisation for Load Balancing in Software Defined Networks

Mustafa Hasan Albowarab, Nurul Azma Zakaria, Zaheera Zainal Abidin
2021 Sensors  
Both tests proved that our DAMP and AMP models were far superior to the state of the art standard models, MP, crowding distance multi-objective particle swarm optimisation (DMP), and PSO.  ...  Therefore, the purposes of this study were (1) to propose a joint mathematical formulation to solve load balancing challenges in cloud computing and (2) to propose two multi-objective particle swarm optimisation  ...  The authors would also like to thank the UTeM Zamalah Scheme for sponsoring the scholarship. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/s21103356 pmid:34065920 fatcat:ccatlffqy5bqrl2ysu2274blma

Vulnerability Coverage as an Adequacy Testing Criterion [article]

Shuvalaxmi Dass, Akbar Siami Namin
2020 arXiv   pre-print
We report the performance of two evolutionary algorithms (i.e., Genetic Algorithms and Particle Swarm Optimization) in generating the vulnerability pattern vectors.  ...  This paper introduces the idea of "vulnerability coverage," a concept to adequately test a given application for a certain classes of vulnerabilities, as reported by the National Vulnerability Database  ...  ACKNOWLEDGMENT This research work is supported in part by a funding from National Science Foundation under grant numbers 1516636 and 1821560.  ... 
arXiv:2006.08606v1 fatcat:umw7tnsglfeepkdaeepn3ayxjq

A Multiple-Swarm Particle Swarm Optimisation Scheme for Tracing Packets Back to the Attack Sources of Botnet

Hsiao-Chung Lin, Ping Wang, Wen-Hui Lin, Yu-Hsiang Huang
2021 Applied Sciences  
Theoretically, the multimodal optimisation problem cannot be solved for all of its multiple solutions using the traditional particle swarm optimisation (PSO) algorithm.  ...  Compared with commonly available systems, the MSPSO algorithm performs better in multimodal optimisation problems, improves the accuracy of traceability analysis and reduces false responses for IPTBK problems  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/app11031139 fatcat:5h7f5q5juze4pfx4nqqurvhtna

Identifying the Attack Sources of Botnets for a Renewable Energy Management System by Using a Revised Locust Swarm Optimisation Scheme

Hsiao-Chung Lin, Ping Wang, Wen-Hui Lin, Kuo-Ming Chao, Zong-Yu Yang
2021 Symmetry  
In general, traditional route-searching algorithms, such as particle swarm optimisation (PSO), have a high convergence speed for IPTBK, but easily fall into the local optima.  ...  This paper proposes an IPTBK analysis scheme for multimodal optimisation problems by applying a revised locust swarm optimisation (LSO) algorithm to the reconstructed attack path in order to identify the  ...  t for particle i in k subswarm ∆τ k ij the movement of ∆t for a particle in k subswarm (CFRS strategy) Figure 1 . 1 Movement of locust swarms in the WOSP algorithm.  ... 
doi:10.3390/sym13071295 fatcat:e743qf5crvfd3hhlyori6ujhxq

Local Bearing Estimation for a Swarm of Low-Cost Miniature Robots

Zheyu Liu, Craig West, Barry Lennox, Farshad Arvin
2020 Sensors  
In addition, the model parameters were optimised using a genetic algorithm to provide a reliable and precise model that can be applied for all robots in a swarm.  ...  The precision of bearing was significantly improved by optimising in both software level and re-arrangement of the sensors' positions on the hardware layout.  ...  particle swarm optimisation (PSO) [55] .  ... 
doi:10.3390/s20113308 pmid:32532071 fatcat:bknstv22prdcdjeliei3wenkoe

Test data generation with a Kalman filter-based adaptive genetic algorithm

Aldeida Aleti, Lars Grunske
2015 Journal of Systems and Software  
In this work, we introduce a new approach for generating test data, based on adaptive optimisation.  ...  Despite their general applicability, genetic algorithms have to be parameterised in order to produce results of high quality.  ...  We wish to acknowledge Monash University for the use of their Nimrod software in this work.  ... 
doi:10.1016/j.jss.2014.11.035 fatcat:rqkma6q7mfabpfgkgh5hffztyy

Sensing and Data-Driven Control for Smart Building and Smart City Systems

Grigore Stamatescu, Ioana Făgărăşan, Anatoly Sachenko
2019 Journal of Sensors  
For unknown node positioning, an optimisation problem is defined based on the LOS measurements and solved by means of the particle swarm optimisation with a constriction factor (PSO-C) method.  ...  Results achieved over testing on 14 reference datasets show improvements over conventional feature selection methods such as genetic algorithms (GA) and multiobjective particle swarm optimization feature  ...  , and software interoperability.  ... 
doi:10.1155/2019/4528034 fatcat:epb4aswjbvd7dbibca5hvdhnwi

Probabilistic Inference on Noisy Time Series (PINTS)

Michael Clerx, Martin Robinson, Ben Lambert, Chon Lok Lei, Sanmitra Ghosh, Gary R. Mirams, David J. Gavaghan
2019 Journal of Open Research Software  
for Bayesian inference or maximum-likelihood estimation.  ...  It allows users to wrap a model and data in a transparent and straightforward interface, which can then be used with custom or pre-defined error measures for optimisation, or with likelihood functions  ...  Currently available optimisers include CMA-ES [6] , XNES [5] , SNES [21] , and Particle Swarm Optimisation (PSO) [12] .  ... 
doi:10.5334/jors.252 fatcat:iomwmxtbojc43am52kpielppzu

Search and tracking algorithms for swarms of robots: A survey

Madhubhashi Senanayake, Ilankaikone Senthooran, Jan Carlo Barca, Hoam Chung, Joarder Kamruzzaman, Manzur Murshed
2016 Robotics and Autonomous Systems  
We review the seminal works that addressed this problem in the area of swarm robotics, which is the application of swarm intelligence principles to the control of multi-robot systems.  ...  Robustness, scalability and flexibility, as well as distributed sensing, make swarm robotic systems well suited for the problem of target search and tracking in real-world applications.  ...  Particle swarm optimisation Particle Swarm Optimisation (PSO) is an SI algorithm which was developed by Kennedy and Eberhart [49, 50] in an attempt to graphically simulate the flocking behaviour of birds  ... 
doi:10.1016/j.robot.2015.08.010 fatcat:fmzgbrzcyjef5hgzhvf6ahzmua

QoS-Aware Dynamic RRH Allocation in a Self-Optimized Cloud Radio Access Network With RRH Proximity Constraint

Muhammad Khan, Raad S. Alhumaima, Hamed S. Al-Raweshidy
2017 IEEE Transactions on Network and Service Management  
A Genetic Algorithm (GA) and Discrete Particle Swarm Optimisation (DPSO) are proposed as evolutionary algorithms to solve the optimisation problem.  ...  QoS is formulated as an optimisation problem by defining it as a weighted combination of new key performance indicators (KPIs) for the number of blocked users and handovers in the network subject to RRH  ...  In this paper, a Discrete Particle Swarm Optimisation (DPSO) is used to solve the QoS maximisation problem defined in (14) .  ... 
doi:10.1109/tnsm.2017.2719399 fatcat:jjg2vz4cwjethffhwnnni3gjlu

Artificial Intelligence Search Strategies for Autonomous Underwater Vehicles Applied for Submarine Groundwater Discharge Site Investigation

Christoph Tholen, Tarek A. El-Mihoub, Lars Nolle, Oliver Zielinski
2021 Journal of Marine Science and Engineering  
This set includes pre-defined path planning (PPP), adapted random walk (RW), particle swarm optimisation (PSO), inertia Levy-flight (ILF), self-organising-migration-algorithm (SOMA), and bumblebee search  ...  In this study, a set of different search strategies for locating submarine groundwater discharge (SGD) are investigated.  ...  Acknowledgments: DFKI acknowledges financial support by the Ministry of Science and Culture through "Niedersachsen Vorab" (ZN3480).  ... 
doi:10.3390/jmse10010007 fatcat:hbkmaiqhmbgfdpzblffptgfave

An Evaluation of Optimisation Approaches in Cloud of Things Resource Trading

Ahmed Salim Al Rawahi, Kevin Lee, Jon Robinson, Ahmad Lotfi
2018 2018 IEEE 6th International Conference on Future Internet of Things and Cloud (FiCloud)  
Cloud Computing and Internet of Things (IoT) have evolved to meet the requirements of many real-world applications.  ...  CoT is expected to host heterogeneous resources and fulfil complex requirements of resource providers and consumers. This complexity poses a real challenge for resource allocation in CoT.  ...  Problem Statement Resource allocation in CoT is formulated as an optimisation problem where different optimisation algorithms are applied including Particle Swarm Optimisation (PSO) [27] , Differential  ... 
doi:10.1109/ficloud.2018.00038 dblp:conf/ficloud/RawahiLRL18 fatcat:wg7v72nyivc57nyumn565ohta4

DeepEvolution: A Search-Based Testing Approach for Deep Neural Networks [article]

Houssem Ben Braiek, Foutse khomh
2019 arXiv   pre-print
We assess the effectiveness of DeepEvolution in testing computer-vision DL models and found that it significantly increases the neuronal coverage of generated test cases.  ...  To overcome these limitations, we propose, DeepEvolution, a novel search-based approach for testing DL models that relies on metaheuristics to ensure a maximum diversity in generated test cases.  ...  [9] proposed DeepXplore, the first white-box testing framework specialized for DNN, which has two main components: (i) a new coverage metric specialized for DNNs (named neuron coverage) that estimates  ... 
arXiv:1909.02563v1 fatcat:afz7fvaz3zbera235glfvjueeq

Probabilistic Inference on Noisy Time Series (PINTS) [article]

Michael Clerx, Martin Robinson, Ben Lambert, Chon Lok Lei, Sanmitra Ghosh, Gary R. Mirams, David J. Gavaghan
2018 arXiv   pre-print
for Bayesian inference or maximum-likelihood estimation.  ...  It allows users to wrap a model and data in a transparent and straightforward interface, which can then be used with custom or pre-defined error measures for optimisation, or with likelihood functions  ...  Currently available optimisers include CMA-ES [4] , XNES [3] , SNES [18] , and Particle Swarm Optimisation (PSO) [10] .  ... 
arXiv:1812.07388v1 fatcat:ryxt6rtwbbdbtf5lq6xn5ihcgq

A model for software defect prediction using support vector machine based on CBA

Xiaotao Rong, Feixiang Li, Zhihua Cui
2016 International Journal of Intelligent Systems Technologies and Applications  
Software defection prediction is not only crucial for improving software quality, but also helpful for software test effort estimation.  ...  In this paper, a CBA-SVM software defect prediction model is proposed, which take advantage of the non-linear computing ability of SVM model and optimisation capacity of bat algorithm with centroid strategy  ...  At the same time, the effort of test can be estimated on the basis of the number of the predicted defections (Srivastava et al., 2012) .  ... 
doi:10.1504/ijista.2016.076102 fatcat:waiyvjxj2zeu7fmsxc2lqha4h4
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