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In PSO, each single solution is a 'bird' in the search space (particle).
In PSO, the variables (gbest and pbest) are affected by their neighborhood.
In PSO, each possible solution, called particle, is assigned location and velocity.
In PSO, the particles possess a fitness criterion for all the positions they visit.
In PSO, each particle can adjust its 'flying' according to its own flying experience and its companions' flying experience.
In PSO, particles update themselves with the internal velocity as they also have memory, which is important to the algorithm.
In PSO, a particle is individual, and a number of particles are grouped as a swarm [29].
In PSO, every particle is considered as a possible solution to the numerical optimization problem in a D-dimensional space.
In PSO application a centralized coordinator is required to maintain several attributes of the particles for every generation.
In PSO minimum value index from the Jlocal and corresponding bacteria's position is termed as the local best position of particle for each bacteria.
In PSO, the trajectory of each particle is guided by the updated velocity based on gbest and pbest values, therefore a proper balance between them is needed.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com