Abstract
Nature-inspired optimization is one of the most prevalent research domains with a confounding history that fascinates the research communities. Particle Swarm Optimization is one of the well-known optimizers that belongs to the family of nature-inspired algorithms. It often suffers from premature convergence leading to a local optimum. To address this, several methods were presented using different network topologies of the particles, but either lacked accuracy or were slow. To solve these problems, an improved version of the Directed Weighted Complex Network Particle Swarm Optimization using the Genetic Algorithm (GDWCN-PSO) is presented. This method uses the concept of the Genetic Algorithm after each update to enhance convergence and diversity. Since most of the real-world applications and complex optimization problems involve more than one objective function so to suit this problem, a multiobjective version of GDWCN-PSO is also proposed and validated on standard benchmarks. To demonstrate its applicability in real-world applications, GDWCN-PSO is applied to solve the optimal key-based medical image encryption. It is one of the most challenging problems in health IoTs for protecting sensitive and confidential patient data as well as addressing the major concern of integrity and security of data in today’s advanced digital world.
- Roy Pramono Adhie, Yonatan Hutama, A. Saleh Ahmar, and M. I. Setiawan. 2018. Implementation cryptography data encryption standard (DES) and triple data encryption standard (3DES) method in communication system based near field communication (NFC). In Journal of Physics: Conference Series, Vol. 954. IOP Publishing, Makassar, Indonesia, 012009.Google Scholar
- M. M. Annie Alphonsa and N. MohanaSundaram. 2019. A reformed grasshopper optimization with genetic principle for securing medical data. Journal of Information Security and Applications 47 (2019), 410–420.Google ScholarCross Ref
- Corey M. Angst, Emily S. Block, John D’arcy, and Ken Kelley. 2017. When do IT security investments matter? Accounting for the influence of institutional factors in the context of healthcare data breaches. MIS Quarterly 41, 3 (2017), 893–916. Google ScholarDigital Library
- Carmelo J. A. Bastos-Filho, Marcel P. Caraciolo, Péricles B. C. Miranda, and Danilo F. Carvalho. 2008. Multi-ring particle swarm optimization. In Proceedings of the 10th Brazilian Symposium on Neural Networks (SBRN’08). IEEE, Salvador, Bahia, Brazil, 111–116. Google ScholarDigital Library
- Vandana Bharti, Bhaskar Biswas, and Kaushal Kumar Shukla. 2020. Recent trends in nature inspired computation with applications to deep learning. In Proceedings of the 2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence). IEEE, Noida, India, 294–299.Google ScholarCross Ref
- Riaan Brits, Andries P. Engelbrecht, and F, Van den Bergh. 2002. A niching particle swarm optimizer. In Proceedings of the 4th Asia-Pacific Conference on Simulated Evolution and Learning, Vol. 2. Singapore, Orchid Country Club, 692–696.Google Scholar
- Wilfried Daniels, Danny Hughes, Mahmoud Ammar, Bruno Crispo, Nelson Matthys, and Wouter Joosen. 2017. S v-the security microvisor: A virtualisation-based security middleware for the internet of things. In Proceedings of the 18th ACM/IFIP/USENIX Middleware Conference: Industrial Track. ACM, Las Vegas, Nevada, 36–42. Google ScholarDigital Library
- Ashraf Darwish, Aboul Ella Hassanien, Mohamed Elhoseny, Arun Kumar Sangaiah, and Khan Muhammad. 2019. The impact of the hybrid platform of Internet of Things and cloud computing on healthcare systems: Opportunities, challenges, and open problems. Journal of Ambient Intelligence and Humanized Computing 10, 10 (2019), 4151–4166.Google ScholarCross Ref
- Kalyanmoy Deb. 2001. Multi-objective Optimization Using Evolutionary Algorithms. Vol. 16. John Wiley & Sons, Chichester, UK. Google ScholarDigital Library
- Joaquín Derrac, Salvador García, Daniel Molina, and Francisco Herrera. 2011. A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm and Evolutionary Computation 1, 1 (2011), 3–18.Google ScholarCross Ref
- Ferdinando Di Martino and Salvatore Sessa. 2020. PSO image thresholding on images compressed via fuzzy transforms. Information Sciences 506 (2020), 308–324.Google ScholarDigital Library
- Wilfred J. Dixon. 1950. Analysis of extreme values. The Annals of Mathematical Statistics 21, 4 (1950), 488–506.Google ScholarCross Ref
- Mohamed Elhoseny, Ahmed Abdelaziz, Ahmed S. Salama, Alaa Mohamed Riad, Khan Muhammad, and Arun Kumar Sangaiah. 2018. A hybrid model of internet of things and cloud computing to manage big data in health services applications. Future Generation Computer Systems 86 (2018), 1383–1394.Google ScholarDigital Library
- Mohamed Elhoseny, K. Shankar, S. K. Lakshmanaprabu, Andino Maseleno, and N. Arunkumar. 2018. Hybrid optimization with cryptography encryption for medical image security in Internet of Things. Neural Computing and Applications - (2018), 1–15. DOI:https://doi.org/10.1007/s00521-018-3801-xGoogle Scholar
- Sahil Garg, Kuljeet Kaur, Shalini Batra, Georges Kaddoum, Neeraj Kumar, and Azzedine Boukerche. 2020. A multi-stage anomaly detection scheme for augmenting the security in IoT-enabled applications. Future Generation Computer Systems 104 (2020), 105–118.Google ScholarDigital Library
- David E. Goldberg and John Henry Holland. 1988. Genetic algorithms and machine learning. Machine Learning 3 (1988), 95–99. Google ScholarDigital Library
- Shivam Gupta, Arpan Kumar Kar, Abdullah Baabdullah, and Wassan A. A. Al-Khowaiter. 2018. Big data with cognitive computing: A review for the future. International Journal of Information Management 42 (2018), 78–89.Google ScholarCross Ref
- Ali Asghar Heidari, Seyedali Mirjalili, Hossam Faris, Ibrahim Aljarah, Majdi Mafarja, and Huiling Chen. 2019. Harris hawks optimization: Algorithm and applications. Future Generation Computer Systems 97 (2019), 849–872.Google ScholarCross Ref
- Yanxia Jin, Rong Zhu, Xin Qi, Jinrui Zhang, Qifu Cheng, Bo Ma, and Yao Jia. 2019. An image watermark insertion and extraction method based on EDA-PSO. In Proceedings of the 2019 2nd International Conference on Sustainable Energy, Environment and Information Engineering (SEEIE 2019), Vol. 184. Atlantis Press, Beijing, China, 251–256. DOI:https://doi.org/10.2991/seeie-19.2019.58Google ScholarCross Ref
- Zhao Jing. 2014. Self-adaptive particle swarm optimization algorithm based on directed-weighted complex networks. Journal of Networks 9, 8 (2014), 2232–2238.Google Scholar
- Jae-Woo Kang, Hyeon-Jeong Park, Jong-Suk Ro, and Hyun-Kyo Jung. 2018. A strategy-selecting hybrid optimization algorithm to overcome the problems of the no free lunch theorem. IEEE Transactions on Magnetics 54, 3 (2018), 1–4.Google Scholar
- Javaid A. Kaw, Nazir A. Loan, Shabir A. Parah, Khan Muhammad, Javaid A. Sheikh, and Ghulam Mohiuddin Bhat. 2019. A reversible and secure patient information hiding system for IoT driven e-health. International Journal of Information Management 45 (2019), 262–275.Google ScholarDigital Library
- James Kennedy. 2011. Particle swarm optimization. In Encyclopedia of Machine Learning. Springer, New York, 760–766.Google Scholar
- James Kennedy and Russell Eberhart. 1995. Particle swarm optimization. In Proceedings of ICNN’95-International Conference on Neural Networks, Vol. 4. IEEE, Perth, WA, 1942–1948.Google ScholarCross Ref
- Manju Khari, Aditya Kumar Garg, Amir H. Gandomi, Rashmi Gupta, Rizwan Patan, and Balamurugan Balusamy. 2019. Securing data in Internet of Things (IoT) using cryptography and steganography techniques. IEEE Transactions on Systems, Man, and Cybernetics: Systems 50, 1 (2019), 73–80.Google ScholarCross Ref
- Baiying Lei, Ee-Leng Tan, Siping Chen, Dong Ni, Tianfu Wang, and Haijun Lei. 2014. Reversible watermarking scheme for medical image based on differential evolution. Expert Systems with Applications 41, 7 (2014), 3178–3188. Google ScholarDigital Library
- Ming Li, Wenqiang Du, and Fuzhong Nian. 2014. An adaptive particle swarm optimization algorithm based on directed weighted complex network. Mathematical Problems in Engineering 2014 (2014), 1–7.Google ScholarCross Ref
- Weibo Liu, Zidong Wang, Yuan Yuan, Nianyin Zeng, Kate Hone, and Xiaohui Liu. 2019. A novel sigmoid-function-based adaptive weighted particle swarm optimizer. IEEE Transactions on Cybernetics Early Access (2019), 1–10. DOI:https://doi.org/10.1109/TCYB.2019.2925015Google ScholarCross Ref
- Leandros A. Maglaras, Ki-Hyung Kim, Helge Janicke, Mohamed Amine Ferrag, Stylianos Rallis, Pavlina Fragkou, Athanasios Maglaras, and Tiago J. Cruz. 2018. Cyber security of critical infrastructures. ICT Express 4, 1 (2018), 42–45.Google ScholarCross Ref
- Gunasekaran Manogaran, Chandu Thota, Daphne Lopez, and Revathi Sundarasekar. 2017. Big Data Security Intelligence for Healthcare Industry 4.0. Springer International Publishing, Cham, 103–126. Google Scholar
- Angelina Jane Reyes Medina, Gregorio Toscano Pulido, and José Gabriel Ramírez-Torres. 2009. A comparative study of neighborhood topologies for particle swarm optimizers. In IJCCI. SciTePress, Mexico, 152–159.Google Scholar
- Xiannong Meng. 2002. Gap Test. Retrieved January 11, 2020 from https://www.eg.bucknell.edu/ xmeng/Course/CS6337/Note/master/node46.html.Google Scholar
- Seyedali Mirjalili and Jin Song Dong. 2020. Multi-Objective Optimization Using Artificial Intelligence Techniques. Springer, Cham. Google ScholarDigital Library
- Seyedali Mirjalili, Seyed Mohammad Mirjalili, and Andrew Lewis. 2014. Grey wolf optimizer. Advances in Engineering Software 69 (2014), 46–61. Google ScholarDigital Library
- Kamlesh Mistry, Li Zhang, Siew Chin Neoh, Chee Peng Lim, and Ben Fielding. 2016. A micro-GA embedded PSO feature selection approach to intelligent facial emotion recognition. IEEE Transactions on Cybernetics 47, 6 (2016), 1496–1509.Google ScholarCross Ref
- Afsoon Moaref and Vahid Sattari Naeini. 2013. A particle swarm optimization based on a ring topology for fuzzy-rough feature selection. In Proceedings of the 13th Iranian Conference on Fuzzy Systems (IFSC’13). IEEE, Qazvin, Iran, 1–6.Google ScholarCross Ref
- Saraju P. Mohanty, Venkata P. Yanambaka, Elias Kougianos, and Deepak Puthal. 2020. PUFchain: A hardware-assisted blockchain for sustainable simultaneous device and data security in the Internet of Everything (IoE). IEEE Consumer Electronics Magazine 9, 2 (2020), 8–16.Google ScholarCross Ref
- A. Mullai and K. Mani. 2020. Enhancing the security in RSA and elliptic curve cryptography based on addition chain using simplified swarm optimization and particle swarm optimization for mobile devices. International Journal of Information Technology - (2020), 1–14. DOI:https://doi.org/10.1007/s41870-019-00413-8Google Scholar
- Talat Naheed, Imran Usman, Tariq M. Khan, Amir H. Dar, and Muhammad Farhan Shafique. 2014. Intelligent reversible watermarking technique in medical images using GA and PSO. Optik 125, 11 (2014), 2515–2525.Google ScholarCross Ref
- Qingjian Ni and Jianming Deng. 2013. A new logistic dynamic particle swarm optimization algorithm based on random topology. The Scientific World Journal 2013 (2013), 1–8.Google ScholarCross Ref
- Thuy Xuan Pham, Patrick Siarry, and Hamouche Oulhadj. 2018. Integrating fuzzy entropy clustering with an improved PSO for MRI brain image segmentation. Applied Soft Computing 65 (2018), 230–242. Google ScholarDigital Library
- Sandeep Pirbhulal, Oluwarotimi Williams Samuel, Wanqing Wu, Arun Kumar Sangaiah, and Guanglin Li. 2019. A joint resource-aware and medical data security framework for wearable healthcare systems. Future Generation Computer Systems 95 (2019), 382–391.Google ScholarDigital Library
- Carlo Puliafito, Enzo Mingozzi, Francesco Longo, Antonio Puliafito, and Omer Rana. 2019. Fog computing for the Internet of Things: A survey. ACM Transactions on Internet Technology (TOIT) 19, 2 (2019), 1–41. Google ScholarDigital Library
- Asanga Ratnaweera, Saman K. Halgamuge, and Harry C. Watson. 2004. Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients. IEEE Transactions on Evolutionary Computation 8, 3 (2004), 240–255. Google ScholarDigital Library
- Vipul Sharma and Roohie Naaz Mir. 2019. An enhanced time efficient technique for image watermarking using ant colony optimization and light gradient boosting algorithm. Journal of King Saud University-Computer and Information Sciences - (2019), in press. DOI:https://doi.org/10.1016/j.jksuci.2019.03.009Google ScholarCross Ref
- Yuhui Shi and Russell C Eberhart. 1998. Parameter selection in particle swarm optimization. In International Conference on Evolutionary Programming. Springer, San Diego, CA, USA, 591–600. Google ScholarDigital Library
- Mojtaba Taherkhani and Reza Safabakhsh. 2016. A novel stability-based adaptive inertia weight for particle swarm optimization. Applied Soft Computing 38 (2016), 281–295. Google ScholarDigital Library
- Kurt Thomas, Frank Li, Ali Zand, Jacob Barrett, Juri Ranieri, Luca Invernizzi, Yarik Markov, Oxana Comanescu, Vijay Eranti, Angelika Moscicki, Daniel Margolis, Vern Paxson, and Elie Bursztein. 2017. Data Breaches, Phishing, or Malware? Understanding the Risks of Stolen Credentials. In Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security (CCS'17). Association for Computing Machinery, New York, NY, USA, 1421--1434. DOI:https://doi.org/10.1145/3133956.3134067 Google ScholarDigital Library
- Zhou Wang, Alan C. Bovik, Hamid R. Sheikh, and Eero P Simoncelli. 2004. Image quality assessment: From error visibility to structural similarity. IEEE Transactions on Image Processing 13, 4 (2004), 600–612. Google ScholarDigital Library
- Xin Yao, Yong Liu, and Guangming Lin. 1999. Evolutionary programming made faster. IEEE Transactions on Evolutionary Computation 3, 2 (1999), 82–102. Google ScholarDigital Library
- Hamza Yapici and Nurettin Cetinkaya. 2019. A new meta-heuristic optimizer: Pathfinder algorithm. Applied Soft Computing 78 (2019), 545–568.Google ScholarDigital Library
- Xingyi Zhang, Ye Tian, Ran Cheng, and Yaochu Jin. 2014. An efficient approach to nondominated sorting for evolutionary multiobjective optimization. IEEE Transactions on Evolutionary Computation 19, 2 (2014), 201–213.Google ScholarDigital Library
- Yong Zhang, Dun-wei Gong, Xiao-yan Sun, and Yi-nan Guo. 2017. A PSO-based multi-objective multi-label feature selection method in classification. Scientific Reports 7, 1 (2017), 1–12.Google Scholar
- Eckart Zitzler, Kalyanmoy Deb, and Lothar Thiele. 2000. Comparison of multiobjective evolutionary algorithms: Empirical results. Evolutionary Computation 8, 2 (2000), 173–195. Google ScholarDigital Library
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- A Novel Multiobjective GDWCN-PSO Algorithm and Its Application to Medical Data Security
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