The particle swarm optimization algorithm: convergence analysis and parameter selection
Information Processing Letters
Natural Computing: an international journal
A study on multidisciplinary collaborative optimisation based on an improved PSO
International Journal of Computer Applications in Technology
Particle swarm optimisation with simple and efficient neighbourhood search strategies
International Journal of Innovative Computing and Applications
Identification for fractional order rational models based on particle swarm optimisation
International Journal of Computer Applications in Technology
On the optimal size of a robust swarm
International Journal of Computer Applications in Technology
Particle swarm optimisation of a discontinuous control for a wheeled mobile robot with two trailers
International Journal of Computer Applications in Technology
Self-Organization particle swarm optimization based on information feedback
ICNC'06 Proceedings of the Second international conference on Advances in Natural Computation - Volume Part I
A diversity-guided quantum-behaved particle swarm optimization algorithm
SEAL'06 Proceedings of the 6th international conference on Simulated Evolution And Learning
Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients
IEEE Transactions on Evolutionary Computation
Implementation of evolutionary fuzzy systems
IEEE Transactions on Fuzzy Systems
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To improve the sole perception method of population diversity and premature stagnation, a self-organisation particle swarm optimisation algorithm based on L norm multi-measurements diversity feedback SOPSO-L is proposed, which introduces negative feedback mechanism to imitate the information interaction between the individuals. Position diversity, velocity diversity and self-cognitive diversity based on L norm are defined as perception information of the swarm. The proposed algorithm adopts multi-measurements swarm diversity as dynamic perception information to tune key parameters such as inertia weight and acceleration coefficients to make the algorithm in convergence or divergence stage. The corresponding characteristics of population diversities were studied. SOPSO-L is tested on six typical test functions and is compared to other variants of PSO presented in the literature. The results show that the proposed method not only greatly improves the global searching capability and computational efficiency, but also effectively avoids the local stagnation problem.