Optimal node placement in industrial Wireless Sensor Networks using adaptive mutation probability binary Particle Swarm Optimization algorithm ; Print ISBN: 978- ...
Optimal node placement in industrial Wireless Sensor Networks using adaptive mutation probability binary Particle Swarm Optimization algorithm · Figures and ...
an adaptive mutation probability binary Particle Swarm. Optimization algorithm (AMPBPSO) inspired by Particle. Page 2. 2200. Swarm Optimization (PSO) to solve ...
The experimental results show that AMPBPSO is effective to tackle IWSNs node placement problems and outperforms discrete binary Particle Swarm Optimization ...
Then an improved adaptive mutation probability binary particle swarm optimization algorithm. (AMPBPSO) is proposed for searching out the best placement scheme.
Then an improved adaptive mutation probability binary particle swarm optimization algorithm (AMPBPSO) is proposed for searching out the best placement scheme.
Aiming at network coverage rate, node dormancy rate and network coverage uniformity, the idea of genetic algorithm mutation is introduced based on the discrete ...
Then an improved adaptive mutation probability binary particle swarm optimization algorithm (AMPBPSO) is proposed for searching out the best placement scheme.
The approach, named Dynamical Relay Node placement Solution (DRNS), is based on the use of Particle Swarm Optimization (PSO) algorithms and is inspired by Model ...
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Apr 5, 2021 · This paper introduces a new approach by combining particle swarm optimization and iterated local search (PSO-ILS) to have an optimum coverage ...
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