Particle Swarm Optimization-Based Session Key Generation for Wireless Communication (PSOSKG)

2015 ◽  
pp. 640-677 ◽  
Author(s):  
Arindam Sarkar ◽  
Jyotsna Kumar Mandal
Keyword(s):  
Particle Swarm Optimization ◽  
Wireless Communication ◽  
Velocity Vector ◽  
Particle Swarm ◽  
Threshold Value ◽  
Key Generation ◽  
Session Key ◽  
Swarm Optimization ◽  
Minimum Probability ◽  
Velocity Level

In this chapter, a Particle Swarm Optimization-Based Session Key Generation for wireless communication (PSOSKG) is proposed. This cryptographic technique is solely based on the behavior of the particle swarm. Here, particle and velocity vector are formed for generation of keystream by setting up the maximum dimension of each particle and velocity vector. Each particle position and probability value is evaluated. Probability value of each particle can be determined by dividing the position of a particular particle by its length. If probability value of a particle is less than minimum probability value then a velocity is applied to move each particle into a new position. After that, the probability value of the particle at the new position is calculated. A threshold value is selected to evaluate against the velocity level of each particle. The particle having the highest velocity more than predefined threshold value is selected as a keystream for encryption.

scholarly journals UCAV Path Planning by Fitness-Scaling Adaptive Chaotic Particle Swarm Optimization

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Yudong Zhang ◽  
Lenan Wu ◽  
Shuihua Wang
Keyword(s):  
Particle Swarm Optimization ◽  
Path Planning ◽  
Optimal Path ◽  
Particle Swarm ◽  
Search Space ◽  
Swarm Optimization ◽  
Adaptive Parameter ◽  
Bee Colony ◽  
Minimum Probability ◽  
Chaotic Particle Swarm Optimization

Path planning plays an extremely important role in the design of UCAVs to accomplish the air combat task fleetly and reliably. The planned path should ensure that UCAVs reach the destination along the optimal path with minimum probability of being found and minimal consumed fuel. Traditional methods tend to find local best solutions due to the large search space. In this paper, a Fitness-scaling Adaptive Chaotic Particle Swarm Optimization (FAC-PSO) approach was proposed as a fast and robust approach for the task of path planning of UCAVs. The FAC-PSO employed the fitness-scaling method, the adaptive parameter mechanism, and the chaotic theory. Experiments show that the FAC-PSO is more robust and costs less time than elite genetic algorithm with migration, simulated annealing, and chaotic artificial bee colony. Moreover, the FAC-PSO performs well on the application of dynamic path planning when the threats cruise randomly and on the application of 3D path planning.

scholarly journals An efficient intrusion detection mechanism based on particle swarm optimization and KNN

2020 ◽  
Vol 5 (20) ◽  
pp. 36-41
Author(s):  
Anand Vijay ◽  
Kailash Patidar ◽  
Manoj Yadav ◽  
Rishi Kushwah
Keyword(s):  
Particle Swarm Optimization ◽  
Intrusion Detection ◽  
Classification Accuracy ◽  
Denial Of Service ◽  
Particle Swarm ◽  
Threshold Value ◽  
Detection Mechanism ◽  
Swarm Optimization ◽  
Average Classification Accuracy

In this paper an efficient intrusion detection mechanism based on particle swarm optimization and KNN has been presented. In our approach experimentation has been performed for the intrusion detection considering NSL-KDD dataset. Then the selected weights have been added directly to the final classification which has been received safely. Then the remaining selected weights have been added for the classification. These nodes are originally safe but received unsafe. It has been input for the classification process. KNN has been used for the classification of the initial features and the content features. The remaining features have been transferred to the particle swarm optimization. PSO has been used for the classification of the traffic and host features. It has been classified based on 50% threshold value. The results show that by using our approach the average classification accuracy is approximately 98%. The attack considered here are Denial of Service (DoS), User to Root (U2R), Remote to User /Login (R2L) and Probe.

AN INFEASIBLE ELITIST BASED PARTICLE SWARM OPTIMIZATION FOR CONSTRAINED MULTIOBJECTIVE OPTIMIZATION AND ITS CONVERGENCE

2010 ◽  
Vol 24 (03) ◽  
pp. 381-400 ◽  
Author(s):  
JINGXUAN WEI ◽  
YUPING WANG
Keyword(s):  
Particle Swarm Optimization ◽  
Pareto Front ◽  
Optimization Problems ◽  
Early Stage ◽  
Particle Swarm ◽  
Threshold Value ◽  
Second Phase ◽  
Total Force ◽  
Swarm Optimization ◽  
Two Phases

In this paper, an infeasible elitist based particle swarm optimization is proposed for solving constrained optimization problems. Firstly, an infeasible elitist preservation strategy is proposed, which keeps some infeasible solutions with smaller rank values at the early stage of evolution regardless of how large the constraint violations are, and keep some infeasible solutions with smaller constraint violations and rank values at the later stage of evolution. In this manner, the true Pareto front will be found easier. Secondly, in order to find a set of diversity and uniformly distributed Pareto optimal solutions, a new crowding distance function is designed. It can assign large function values not only for the particles located in the sparse regions of the objective space but also for the crowded particles located near to the boundary of the Pareto front as well. Thirdly, a new mutation operator with two phases is proposed. In the first phase, the particles whose constraint violations are less than the threshold value will be used to compute the total force, then the force will be used as a mutation direction, being helpful to find the better solutions along this direction. In order to guarantee the convergence of the algorithm, the second phase of mutation is proposed. Finally, the convergence of the algorithm is proved. The comparative study shows that the proposed algorithm can generate widespread and uniformly distributed Pareto fronts and outperforms those compared algorithms.

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