Purpose The purpose of this paper is to investigate the performance of chaotic gravitational search algorithm (CGSA) in solving mechanical engineering design frameworks including welded beam design (WBD), compression spring design (CSD) and pressure vessel design (PVD). Design/methodology/approach In this study, ten chaotic maps were combined with gravitational constant to increase the exploitation power of gravitational search algorithm (GSA). Also, CGSA has been used for maintaining the adaptive capability of gravitational constant. Furthermore, chaotic maps were used for overcoming premature convergence and stagnation in local minima problems of standard GSA. Findings The chaotic maps have shown efficient performance for WBD and PVD problems. Further, they have depicted competitive results for CSD framework. Moreover, the experimental results indicate that CGSA shows efficient performance in terms of convergence speed, cost function minimization, design variable optimization and successful constraint handling as compared to other participating algorithms. Research limitations/implications The use of chaotic maps in standard GSA is a new beginning for research in GSA particularly convergence and time complexity analysis. Moreover, CGSA can be used for solving the infinite impulsive response (IIR) parameter tuning and economic load dispatch problems in electrical sciences. Originality/value The hybridization of chaotic maps and evolutionary algorithms for solving practical engineering problems is an emerging topic in metaheuristics. In the literature, it can be seen that researchers have used some chaotic maps such as a logistic map, Gauss map and a sinusoidal map more rigorously than other maps. However, this work uses ten different chaotic maps for engineering design optimization. In addition, non-parametric statistical test, namely, Wilcoxon rank-sum test, was carried out at 5% significance level to statistically validate the simulation results. Besides, 11 state-of-the-art metaheuristic algorithms were used for comparative analysis of the experimental results to further raise the authenticity of the experimental setup.
Cyberspace plays a dominant role in the world of electronic communication. It is a virtual space where the interconnecting network has an independent technology infrastructure. The internet is the baseline for the cyberspace which can be openly accessible. Cyber-security is a set of techniques used to protect network integrity and data from vulnerability. The protection mechanism involves the identification of threats and taking precaution by predicting the vulnerabilities in the environment. The main cause of security violation will be threats, that are caused by the intruder who attacks the network or any electronic devices with the intention to cause damage in the communication network. These threats must be taken into consideration for the mitigation process to improve the system efficiency and performance. Machine learning helps to increase the accuracy level in the detection of threats and their mitigation process in an efficient way. This chapter describes the way in which threats can be detected and mitigated in cyberspace with certain strategies using machine learning.
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