A novel particle swarm optimization algorithm with adaptive inertia weight
Applied Soft Computing
Protein structure prediction using particle swarm optimization and a distributed parallel approach
Proceedings of the 3rd workshop on Biologically inspired algorithms for distributed systems
An adaptive staged PSO based on particles' search capabilities
ICSI'10 Proceedings of the First international conference on Advances in Swarm Intelligence - Volume Part I
Exponential inertia weight for particle swarm optimization
ICSI'12 Proceedings of the Third international conference on Advances in Swarm Intelligence - Volume Part I
Protein structure prediction using distributed parallel particle swarm optimization
Natural Computing: an international journal
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The inertia weight is one of the parameter in Particle Swarm Optimization algorithm. It gets important effect on balancing the global search and the local search in PSO. Basing on the linear descending inertia weight and the random inertia weight, this paper presents the strategy of chaotic descending inertia weight and the strategy of chaotic random inertia weight by introduced chaotic optimization mechanism into PSO. They make PSO algorithm has the characteristics of preferable convergence precision, quickly convergence velocity and better global search ability. The PSO using the chaotic random inertia weight performs especial outstanding comparing with the PSO using random inertia weight, owing to it has rough search stage and minute search stage alternately in all its evolutionary process. Keywords Particle Swarm Optimization; inertia weight; chaos