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Subject: search.filters.subject.Particle swarm optimization

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    An analysis of the operation factors of three PSO-GA-ED meta-heuristic search methods for solving a single-objective optimization problem
    (Hindawi Limited, 2022-01-01)
    In this study, we evaluate several nongradient (evolutionary) search strategies for minimizing mathematical function expressions. We developed and tested the genetic algorithms, particle swarm optimization, and differential evolution in order to assess their general efficacy in optimization of mathematical equations. A comparison is then made between the results and the efficiency, which is determined by the number of iterations, the observed accuracy, and the overall run time. Additionally, the optimization employs 12 functions from Easom, Holder table, Michalewicz, Ackley, Rastrigin, Rosen, Rosen Brock, Shubert, Sphere, Schaffer, Himmelblau's, and Spring Force Vanderplaats. Furthermore, the crossover rate, mutation rate, and scaling factor are evaluated to determine the effectiveness of the following algorithms. According to the results of the comparison of optimization algorithms, the DE algorithm has the lowest time complexity of the others. Furthermore, GA demonstrated the greatest degree of temporal complexity. As a result, using the PSO method produces different results when repeating the same algorithm with low reliability in terms of locating the optimal location.
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    An efficient intelligent intrusion detection system using fuzzy logic based on the particle swarm optimization algorithm: A case study
    (IOS Press, 2024-10-25)
    This paper presents an intelligent intrusion detection system using fuzzy logic based on particle swarm optimization algorithm. The main goal of this research is to survey the convergence capability of the particle swarm optimization algorithm using fuzzy logic in intelligent intrusion detection of a designable system. In order to simulate intelligent attacks on a system, KDD99 data are used. Based on the findings, the Particle Swarm Optimization (PSO) algorithm is highly capable of detecting an intelligent attack on a system. In this study, we considered 1800 times attack, in which the PSO algorithm was capable of repelling attacks in 7.24 seconds and converged. The best convergence occurred at stage 775, and then all attacks were eliminated from the system. Results showed that the stability and convergence of the system improved after each attack. Also, the number of attacks increased to 2500 times to investigate unpredictable intrusions and converge accrued at the attack 771st. Finally, the results obtained by the PSO algorithms were compared to the results obtained by the Genetic Algorithm (GA) and Simulated Annealing (SA) algorithm. The findings indicate that the PSO algorithm is highly capable of detecting intelligent intrusions into a system. It is also suggested to employ this algorithm in cloud computing systems because of its high capability of repelling smart attacks.
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    An efficient intelligent intrusion detection system using fuzzy logic based on the Particle Swarm Optimization algorithm: A case study
    (IOS Press, 2025-04-01)
    This paper presents an intelligent intrusion detection system using fuzzy logic based on particle swarm optimization algorithm. The main goal of this research is to survey the convergence capability of the particle swarm optimization algorithm using fuzzy logic in intelligent intrusion detection of a designable system. In order to simulate intelligent attacks on a system, KDD99 data are used. Based on the findings, the Particle Swarm Optimization (PSO) algorithm is highly capable of detecting an intelligent attack on a system. In this study, we considered 1800 times attack, in which the PSO algorithm was capable of repelling attacks in 7.24 s and converged. The best convergence occurred at stage 775, and then all attacks were eliminated from the system. Results showed that the stability and convergence of the system improved after each attack. Also, the number of attacks increased to 2500 times to investigate unpredictable intrusions and converge accrued at the attack 771st. Finally, the results obtained by the PSO algorithms were compared to the results obtained by the Genetic Algorithm (GA) and Simulated Annealing (SA) algorithm. The findings indicate that the PSO algorithm is highly capable of detecting intelligent intrusions into a system. It is also suggested to employ this algorithm in cloud computing systems because of its high capability of repelling smart attacks.
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