Cryptanalysis of Merkle-Hellman cipher using ant colony optimization

The Merkle-Hellman (MH) cryptosystem is one of the earliest public key cryptosystems, which is introduced by Ralph Merkle and Martin Hellman in 1978 based on an NP-hard problem, known as the subset-sum problem. Furthermore, ant colony optimization (ACO) is one of the most nature-inspired meta-heuristic optimization, which simulates the social behaviour of ant colonies. ACO has demonstrated excellent performance in solving a wide variety of complex problems. In this paper, we present a novel ant colony optimization (ACO) based attack for cryptanalysis of MH cipher algorithm, where two different search techniques are used. Moreover, experimental study is included, showing the effectiveness of the proposed attacking scheme. The results show that ACO based attack is more suitable than many other algorithms like genetic algorithm (GA) and particle swarm optimization (PSO).

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Fast Ant Colony Optimization for Clustering

Indonesian Journal of Electrical Engineering and Computer Science ◽

10.11591/ijeecs.v12.i1.pp78-86 ◽

2018 ◽

Vol 12 (1) ◽

pp. 78

Author(s):

Abba Suganda Girsang ◽

Tjeng Wawan Cenggoro ◽

Ko-Wei Huang

Keyword(s):

Ant Colony Optimization ◽

Data Clustering ◽

Heuristic Algorithms ◽

Computation Time ◽

Ant Colony ◽

Experimental Result ◽

Cluster Problem ◽

Swarm Optimization ◽

Clustering Problem ◽

Np Hard Problem

Data clustering is popular data analysis approaches, which used to organizing data into sensible clusters based on similarity measure, where data within a cluster are similar to each other but dissimilar to that of another cluster. In the recently, the cluster problem has been proven as NP-hard problem, thus, it can be solved with meta-heuristic algorithms, such as the particle swarm optimization (PSO), genetic algorithm (GA), and ant colony optimization (ACO), respectively. This paper proposes an algorithm called Fast Ant Colony Optimization for Clustering (FACOC) to reduce the computation time of Ant Colony Optimization (ACO) in clustering problem. FACOC is developed by the motivation that a redundant computation is occurred in ACO for clustering. This redundant computation can be cut in order to reduce the computation time of ACO for clustering. The proposed FACOC algorithm was verified on 5 well-known benchmarks. Experimental result shows that by cutting this redundant computation, the computation time can be reduced about 28% while only suffering a small quality degradation.

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Packets routing and bandwidth sensing in a network traffic: Ant colony optimization tactic.

Global Journal of Pure and Applied Sciences ◽

10.4314/gjpas.v24i2.12 ◽

2018 ◽

Vol 24 (2) ◽

pp. 223-228

Author(s):

Felix U. Ogban ◽

Roy Nentui

Keyword(s):

Ant Colony Optimization ◽

Network Traffic ◽

Priority Queue ◽

Ant Colony ◽

Ant Colony Optimization Algorithm ◽

Ant Colonies ◽

Lower Priority ◽

Link State ◽

Discrete Problems ◽

Network Platform

Packets routing and bandwidth sensing in a network platform remains an integral part of the study of signal flow.The algorithm to route packets in a network link called the AntNet algorithm was inspired by the behavior of real ant colonies. At each node in the network, a forward ant deposits some amount of pheromones at different links that responds to the node’s queue length. In this paper, we propose the inclusion of the computation of paths to adapt with the Depth Search Ant Explorer Network (DS-ANTENet) algorithm for discrete problems as an IP based mechanism. This method is tested and the efficiency is compared to the original AntNet algorithm and the Link-State algorithm to check the transmission of computing traffic flows between the nodes. We then made comparison with the algorithms proposed in the literature. The protocols were sorted out in terms of average number of lost packets ranging from the higher priority queue to the lower priority queue which then resulted to the fact that; First, AntNetBW (loss ratios reduction of 9.6% when compared to the AntNet and the Link-State algorithm respectively. Secondly, SANTENetBW (loss ratios reduction of 8.3% and 36.7% when compared to the AntNet and the Link-State algorithm respectively. Finally, DS-ANTENet (loss ratios reduction 0.7% and 33.2% when compared to the AntNet and the Link- State algorithm respectively.Keywords: Packets Routing, Bandwidth Sensing, Network Traffic, Ant Colony Optimization Algorithm, AntNet

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QALPA- An Efficient QoS Aware Hybrid Localization Model Using Particle Swarm Optimization and Ant Colony Optimization for Cognitive Wireless Sensor Networks

International Journal of Intelligent Engineering and Systems ◽

10.22266/ijies2021.0228.26 ◽

2021 ◽

Vol 14 (1) ◽

pp. 270-280

Author(s):

Abhijit Halkai ◽

◽

Sujatha Terdal ◽

Keyword(s):

Particle Swarm Optimization ◽

Sensor Networks ◽

Ant Colony Optimization ◽

Large Scale ◽

Cluster Head ◽

Particle Swarm ◽

Base Station ◽

Ant Colony ◽

Swarm Optimization ◽

Localization Model

A sensor network operates wirelessly and transmits detected information to the base station. The sensor is a small sized device, it is battery-powered with some electrical components, and the protocols should operate efficiently in such least resource availability. Here, we propose a novel improved framework in large scale applications where the huge numbers of sensors are distributed over an area. The designed protocol will address the issues that arise during its communication and give a consistent seamless communication system. The process of reasoning and learning in cognitive sensors guarantees data delivery in the network. Localization in Scarce and dense sensor networks is achieved by efficient cluster head election and route selection which are indeed based on cognition, improved Particle Swarm Optimization, and improved Ant Colony Optimization algorithms. Factors such as mobility, use of sensor buffer, power management, and defects in channels have been identified and solutions are presented in this research to build an accurate path based on the network context. The achieved results in extensive simulation prove that the proposed scheme outperforms ESNA, NETCRP, and GAECH algorithms in terms of Delay, Network lifetime, Energy consumption.

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Particle Swarm Optimization Combined with Ant Colony Optimization for the Multiple Traveling Salesman Problem

Materials Science Forum ◽

10.4028/www.scientific.net/msf.626-627.717 ◽

2009 ◽

Vol 626-627 ◽

pp. 717-722 ◽

Cited By ~ 1

Author(s):

Hong Kui Feng ◽

Jin Song Bao ◽

Jin Ye

Keyword(s):

Particle Swarm Optimization ◽

Ant Colony Optimization ◽

Traveling Salesman Problem ◽

Particle Swarm ◽

Traveling Salesman ◽

Ant Colony ◽

Great Success ◽

Swarm Optimization ◽

Continuous Space ◽

Multiple Traveling Salesman Problem

A lot of practical problem, such as the scheduling of jobs on multiple parallel production lines and the scheduling of multiple vehicles transporting goods in logistics, can be modeled as the multiple traveling salesman problem (MTSP). Due to the combinatorial complexity of the MTSP, it is necessary to use heuristics to solve the problem, and a discrete particle swarm optimization (DPSO) algorithm is employed in this paper. Particle swarm optimization (PSO) in the continuous space has obtained great success in resolving some minimization problems. But when applying PSO for the MTSP, a difficulty rises, which is to find a suitable mapping between sequence and continuous position of particles in particle swarm optimization. For overcoming this difficulty, PSO is combined with ant colony optimization (ACO), and the mapping between sequence and continuous position of particles is established. To verify the efficiency of the DPSO algorithm, it is used to solve the MTSP and its performance is compared with the ACO and some traditional DPSO algorithms. The computational results show that the proposed DPSO algorithm is efficient.

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ACO powered by Local Searches Algorithms for the Solution of TSP Problems

Qubahan Academic Journal ◽

10.48161/qaj.v1n1a5 ◽

2020 ◽

Vol 1 (1) ◽

pp. 1-10

Author(s):

Nisreen L. Ahmed

Keyword(s):

Ant Colony Optimization ◽

Optimization Technique ◽

General Purpose ◽

Ant Colony ◽

Ant Colony Optimization Algorithm ◽

New Approach ◽

Local Searches ◽

Probabilistic Technique ◽

The Social ◽

Great Deluge Algorithm

Swarm intelligence is a relatively new approach to problem solving that takes inspiration from the social behaviors of insects and other animals. Ants, in particular, have inspired a number of methods and techniques among which the most studied and successful is the general-purpose optimization technique, also known as ant colony optimization, In computer science and operations research, the ant colony optimization algorithm (ACO) is a probabilistic technique for solving computational problems which can be reduced to finding good paths through graphs. Ant Colony Optimization (ACO) algorithm is used to arrive at the best solution for TSP. In this article, the researcher has introduced ways to use a great deluge algorithm with the ACO algorithm to increase the ability of the ACO in finding the best tour (optimal tour). Results are given for different TSP problems by using ACO with great deluge and other local search algorithms.

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