Particle Swarm Optimization and Intelligence: Advances and Applications
Particle Swarm Optimization and Intelligence: Advances and Applications
A rank based particle swarm optimization algorithm with dynamic adaptation
Journal of Computational and Applied Mathematics
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
Particle swarm optimization with deliberate loss of information
Soft Computing - A Fusion of Foundations, Methodologies and Applications
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Standard Particle Swarm Optimization (PSO) allocates the total available computational budget, in terms of function evaluations, equally among the particles at each iteration of the algorithm. The present work introduces an alternative, which employs neighborhood ranking for allocating the computational budget to the particles. The proposed PSO variant favors the particles that belong to more promising neighborhoods by providing them with more function evaluations than the rest, based on a stochastic neighborhood selection scheme. Preliminary experimental results on standard test problems reveal that the proposed approach is highly competitive.