Social interaction in particle swarm optimization, the ranked FIPS, and adaptive multi-swarms
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Convergence behavior of the fully informed particle swarm optimization algorithm
Proceedings of the 10th annual conference on Genetic and evolutionary computation
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
Speculative evaluation in particle swarm optimization
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part II
Particle swarm optimisation with gradually increasing directed neighbourhoods
Proceedings of the 13th annual conference on Genetic and evolutionary computation
The apiary topology: emergent behavior in communities of particle swarms
PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part II
BFO with information communicational system based on different topologies structure
ICIC'13 Proceedings of the 9th international conference on Intelligent Computing Theories and Technology
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Particle Swarm Optimization (PSO) has typically been used with small swarms of about 50 particles. However, PSO is more efficiently parallelized with large swarms. We formally describe existing topologies and identify variations which are better suited to large swarms in both sequential and parallel computing environments. We examine the performance of PSO for benchmark functions with respect to swarm size and topology. We develop and demonstrate a new PSO variant which leverages the unique strengths of large swarms. "Hearsay PSO" allows for information to flow quickly through the swarm, even with very loosely connected topologies. These loosely connected topologies are well suited to large scale parallel computing environments because they require very little communication between particles. We consider the case where function evaluations are expensive with respect to communication as well as the case where function evaluations are relatively inexpensive. We also consider a situation where local communication is inexpensive compared to external communication, such as multicore systems in a cluster.