In this paper, a novel learning algorithm, called symbiotic adaptive particle swarm optimization (SAPSO), that tunes the parameters of NFNs is proposed.
In this section, we will introduce the symbiotic adaptive particle swarm optimization (SAPSO) for NFN design.
Updating velocities and sub-particles: When the fitness value of each sub-particle is obtained from the fitness assignment step, the Lbest of each subparticle and the Gbest of each sub-swarm are updated simultaneously using adaptive particle swarm optimization with neighborhood operator (APSO-NO).
Adaptive particle swarm optimization (APSO) is implemented for the optimization of the parameters of SVM (c, g) as shown below.
Adaptive Particle Swarm Optimization. Particle swarm optimization (PSO) is an evolutionary algorithm based on swarm intelligence .
Adaptive particle swarm optimization
algorithm was introduced by Zhan to improve the performance of PSO, many operators were proposed to help the swarm jump out of the local optima and the algorithms have been evaluated on 12 benchmark functions [10, 11].
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