Authentication and Encryption Using Modified Elliptic Curve Cryptography with Particle Swarm Optimization and Cuckoo Search Algorithm

2018 ◽  
Vol 99 (4) ◽  
pp. 343-351
Author(s):  
Sujatha Kota ◽  
Venkata Nageswara Rao Padmanabhuni ◽  
Kishor Budda ◽  
Sruthi K
Keyword(s):  
Particle Swarm Optimization ◽  
Elliptic Curve ◽  
Elliptic Curve Cryptography ◽  
Search Algorithm ◽  
Particle Swarm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Swarm Optimization

An Optimized K-Harmonic Means Algorithm Combined with Modified Particle Swarm Optimization and Cuckoo Search Algorithm

2015 ◽  
Vol 29 (1) ◽  
pp. 1-18
Author(s):  
Asgarali Bouyer ◽  
Nacer Farajzadeh
Keyword(s):  
Particle Swarm Optimization ◽  
Search Algorithm ◽  
Particle Swarm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Fast Convergence ◽  
Data Sets ◽  
Swarm Optimization ◽  
Local Optima ◽  
Harmonic Means

Abstract Among the data clustering algorithms, the k-means (KM) algorithm is one of the most popular clustering techniques because of its simplicity and efficiency. However, KM is sensitive to initial centers and it has a local optima problem. The k-harmonic means (KHM) clustering algorithm solves the initialization problem of the KM algorithm, but it also has a local optima problem. In this paper, we develop a new algorithm for solving this problem based on a modified version of particle swarm optimization (MPSO) algorithm and KHM clustering. In the proposed algorithm, MPSO is equipped with the cuckoo search algorithm and two new concepts used in PSO in order to improve the efficiency, fast convergence, and escape from local optima. MPSO updates the positions of particles based on a combination of global worst, global best with personal worst, and personal best to dynamically be used in each iteration of the MPSO. The experimental result on eight real-world data sets and two artificial data sets confirms that this modified version is superior to KHM and the regular PSO algorithm. The results of the simulation show that the new algorithm is able to create promising solutions with fast convergence, high accuracy, and correctness while markedly improving the processing time.

scholarly journals An Optimized K-Harmonic Means Algorithm Combined with Modified Particle Swarm Optimization and Cuckoo Search Algorithm

2016 ◽  
Vol 41 (2) ◽  
pp. 99-121 ◽  
Author(s):  
Asgarali Bouyer
Keyword(s):  
Particle Swarm Optimization ◽  
Search Algorithm ◽  
Particle Swarm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Fast Convergence ◽  
Experimental Result ◽  
Swarm Optimization ◽  
Local Optima ◽  
Harmonic Means

AbstractAmong the data clustering algorithms, k-means (KM) algorithm is one of the most popular clustering techniques due to its simplicity and efficiency. However, k-means is sensitive to initial centers and it has the local optima problem. K-harmonic-means (KHM) clustering algorithm solves the initialization problem of k-means algorithm, but it also has local optima problem. In this paper, we develop a new algorithm for solving this problem based on an improved version of particle swarm optimization (IPSO) algorithm and KHM clustering. In the proposed algorithm, IPSO is equipped with Cuckoo Search algorithm and two new concepts used in PSO in order to improve the efficiency, fast convergence and escape from local optima. IPSO updates positions of particles based on a combination of global worst, global best with personal worst and personal best to dynamically be used in each iteration of the IPSO. The experimental result on five real-world datasets and two artificial datasets confirms that this improved version is superior to k-harmonic means and regular PSO algorithm. The results of the simulation show that the new algorithm is able to create promising solutions with fast convergence, high accuracy and correctness while markedly improving the processing time.

scholarly journals A Modified Cuckoo Search Algorithm for Data Clustering

2022 ◽  
Vol 13 (1) ◽  
pp. 0-0
Keyword(s):  
Particle Swarm Optimization ◽  
Search Algorithm ◽  
Particle Swarm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Swarm Optimization ◽  
Bee Colony ◽  
Nature Inspired Algorithms ◽  
Benchmark Test Functions ◽  
Optimal Cluster

Clustering of data is one of the necessary data mining techniques, where similar objects are grouped in the same cluster. In recent years, many nature-inspired based clustering techniques have been proposed, which have led to some encouraging results. This paper proposes a Modified Cuckoo Search (MoCS) algorithm. In this proposed work, an attempt has been made to balance the exploration of the Cuckoo Search (CS) algorithm and to increase the potential of the exploration to avoid premature convergence. This algorithm is tested using fifteen benchmark test functions and is proved as an efficient algorithm in comparison to the CS algorithm. Further, this method is compared with well-known nature-inspired algorithms such as Ant Colony Optimization (ACO), Artificial Bee Colony (ABC), Particle Swarm Optimization (PSO), Particle Swarm Optimization with Age Group topology (PSOAG) and CS algorithm for clustering of data using six real datasets. The experimental results indicate that the MoCS algorithm achieves better results as compared to other algorithms in finding optimal cluster centers.

scholarly journals A Hybrid Modified Method of the Sine Cosine Algorithm Using Latin Hypercube Sampling with the Cuckoo Search Algorithm for Optimization Problems

Electronics ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 1786
Author(s):  
Siti Julia Rosli ◽  
Hasliza A Rahim ◽  
Khairul Najmy Abdul Rani ◽  
Ruzelita Ngadiran ◽  
R. Badlishah Ahmad ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Particle Swarm ◽  
Latin Hypercube Sampling ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Latin Hypercube ◽  
Swarm Optimization ◽  
Sine Cosine Algorithm

The metaheuristic algorithm is a popular research area for solving various optimization problems. In this study, we proposed two approaches based on the Sine Cosine Algorithm (SCA), namely, modification and hybridization. First, we attempted to solve the constraints of the original SCA by developing a modified SCA (MSCA) version with an improved identification capability of a random population using the Latin Hypercube Sampling (LHS) technique. MSCA serves to guide SCA in obtaining a better local optimum in the exploitation phase with fast convergence based on an optimum value of the solution. Second, hybridization of the MSCA (HMSCA) and the Cuckoo Search Algorithm (CSA) led to the development of the Hybrid Modified Sine Cosine Algorithm Cuckoo Search Algorithm (HMSCACSA) optimizer, which could search better optimal host nest locations in the global domain. Moreover, the HMSCACSA optimizer was validated over six classical test functions, the IEEE CEC 2017, and the IEEE CEC 2014 benchmark functions. The effectiveness of HMSCACSA was also compared with other hybrid metaheuristics such as the Particle Swarm Optimization–Grey Wolf Optimization (PSOGWO), Particle Swarm Optimization–Artificial Bee Colony (PSOABC), and Particle Swarm Optimization–Gravitational Search Algorithm (PSOGSA). In summary, the proposed HMSCACSA converged 63.89% faster and achieved a shorter Central Processing Unit (CPU) duration by a maximum of up to 43.6% compared to the other hybrid counterparts.

Optimal Elliptic Curve Cryptography-Based Effective Approach for Secure Data Storage in Clouds

2020 ◽  
Vol 11 (4) ◽  
pp. 65-81
Author(s):  
Anju Malik ◽  
Mayank Aggarwal ◽  
Bharti Sharma ◽  
Akansha Singh ◽  
Krishna Kant Singh
Keyword(s):  
Elliptic Curve ◽  
Data Storage ◽  
Elliptic Curve Cryptography ◽  
Search Algorithm ◽  
Distributed Storage ◽  
Data Retrieval ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Sensitive Data ◽  
Cloud Server

With the rapid development of cloud advancement, a data security challenge has emerged. In this paper, a technique based on elliptical cryptography and cuckoo search algorithm is proposed. With this technique, data owners securely store their data files in the cloud server. Initially the user sends a file storage request to store a file in a cloud server provider (CSP). The input file is checked whether it is sensitive or non-sensitive by the user. If the file is sensitive, then it would be split and stored in different virtual machines (VMs), and if the file is non-sensitive, then it would be assigned in a single VM. This approach was used for the first time as per the survey. To add further security, the sensitive data retrieval needs an encryption process that is supported by the proposed algorithm. If the data owner stores the sensitive data to cloud server, the data owner's document is encrypted by the double encryption technique. Here RSA and optimal elliptic curve cryptography (OECC) algorithm is used to encrypt the document with high security. The authors have used cuckoo search algorithm to identify the optimal key in ECC. This paper has proposed a novel cryptography approach for delivering mass distributed storage by which user's original data cannot be directly reached by cloud operators. Hence, this research has proved that the proposed work will give better securable data storage solving the security issues.

scholarly journals Procurement Interaction Minimize Test Arrangement Formation of Software Testing Using Cuckoo Search Methods

2018 ◽  
Vol 7 (4.6) ◽  
pp. 302
Author(s):  
Dr. Anandam Velagandula ◽  
P. Buddha Reddy ◽  
N. Hanuman Reddy ◽  
G. Srikanth Reddy ◽  
Ch Anil
Keyword(s):  
Particle Swarm Optimization ◽  
Service Oriented Architecture ◽  
Heuristic Algorithms ◽  
Search Algorithm ◽  
Harmony Search ◽  
Interaction Strength ◽  
Particle Swarm ◽  
Cuckoo Search ◽  
Test Cases ◽  
Swarm Optimization

As of late number of meta based heuristic algorithms are suggested to fill in as the premise of test era technique (where shows the interaction strength) embracing  with Simulated Annealing (SA), Ant Colony Optimization (ACO), Cuckoo Search (CS), Genetic Algorithms (GA), Harmony Search (HS) and Particle Swarm Optimization (PSO). Albeit helpful methodologies are requiring particular area learning so as to permit successful tuning before great quality arrangements can be gotten. The multi-target molecule swarm optimization, and multithreading is utilized to overwhelm the compound judgement criteria for an ideal arrangement. The procedure and its related algorithms are assessed broadly utilizing diverse benchmarks and examinations. In our proposed technique test cases are advanced by utilizing Particle Swarm Optimization algorithm (PSO). At that point the streamlined test cases are organized by utilizing to enhanced Cuckoo Search algorithm (ECSA). As the quantity of inserted systems increments quickly, there has been developing interest for the utilization of Service Oriented Architecture (SOA) for some requests. At last, the enhanced outcome will be assessed by programming quality measures.

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