Do topological properties have any impact on the search behavior of population-based stochastic optimization methods like particle swarm optimization (PSO)? Can a PSO algorithm improve efficiency if such properties are tuned adaptively while integrating cellular learning automata (CLA)? Swarm behavior is behavior that is displayed by a flock of birds, wherein a single bird (also called a particle) utilizes its own historical positions and global best positions to update its own flying velocity and associated movement within the swarm. This approach has often been cited as a reason for the major limitation of getting trapped in local optima.
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