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In addition, since two methods are used to improve PSO, it is necessary to identify if both could shed positive effect on PSO algorithm.
For the evolutionary computation (EC) techniques of particle swarm optimization (PSO), it only requires fewer parameters compared to the GA-based approach.
When using PSO, it can be pointed out, that the light source position can be determined within micro-meter accuracy, which effectively reduces the overall measurement error of the system.
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Known by its code name, PSO J318.5-22, it was discovered about 80 light years away from Earth by scientists analysing data from the Pan-STARRS 1 telescope sited on top of the Haleakala volcanic mountain on the island of Maui in Hawaii.
This strategy not only provides a series of flexible parameters for PSO but it also provides a new way to analyze particle trajectories that incorporates engineering practices.
The particle swarm optimization (PSO), as it is called now, does not require any gradient information of the function to be optimized, uses only primitive mathematical operators and is conceptually very simple.
The following analyses are limited to PSO because it is one of the two suitable optimizers.
The rapid convergence and relative simplicity of PSO make it very suitable to solve multi-objective optimization named as multi-objective PSO (MOPSO).
According to the astronomers who found it, PSO J318.5-22 is one of the lowest-mass free-floating objects known, perhaps the lowest.
The reason for incorporating the PSO is that it is less complex and gives better results in less time.
First, the window starts from the first nucleotide of the conserved sequences and the PSO algorithm applies it to the designed primer pair within.
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