2021
DOI: 10.1109/access.2021.3097182
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Particle Swarm Optimization Algorithm for Detecting Distributed Predicates

Abstract: Metaheuristic algorithms are widely used to solve NP-complete problems in several domains. Distributed predicates detection is a fundamental distributed systems problem that has many useful applications. The problem of distributed predicates detection, in general, is known to be an NP-complete problem. In this paper, we developed a detection algorithm inspired by the particle swarm optimization algorithm, one of the well-known metaheuristic algorithms applied to solve problems in several domains. The proposed … Show more

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Cited by 4 publications
(2 citation statements)
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“…The experimental results showed that the migration learning mechanism could speed up the search efficiency of particle swarm optimization algorithm and enabled the algorithm to obtain a better optimal path. Maghayreh et al [7] studied an image encryption algorithm based on multi-objective particle swarm optimization algorithm, DNA coding sequence and one-dimensional logistic mapping. Simulation experiments and security analysis showed that the correlation coefficient and entropy of the ciphertext were very good, which could resist various typical attacks and had good encryption effect.…”
Section: Related Workmentioning
confidence: 99%
“…The experimental results showed that the migration learning mechanism could speed up the search efficiency of particle swarm optimization algorithm and enabled the algorithm to obtain a better optimal path. Maghayreh et al [7] studied an image encryption algorithm based on multi-objective particle swarm optimization algorithm, DNA coding sequence and one-dimensional logistic mapping. Simulation experiments and security analysis showed that the correlation coefficient and entropy of the ciphertext were very good, which could resist various typical attacks and had good encryption effect.…”
Section: Related Workmentioning
confidence: 99%
“…In the first stage, the genetic algorithm is used. and the randomness, rapidity and completeness of PSO to obtain a series of sub-optimal solutions (preliminary search), and use them to initialize the pheromone of the ant colony algorithm, and in the second stage, use high-precision solutions for detailed search [4]. Although the PSO problem is widely used in various practical optimization problems, there are relatively few studies applying it to the DS optimization problem.…”
Section: Introductionmentioning
confidence: 99%