Particle swarm optimisation with spatial particle extension
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Multi-strategy ensemble particle swarm optimization for dynamic optimization
Information Sciences: an International Journal
An Improved Particle Swarm Optimization With Fuzzy c-Means Clustering Algorithm
IHMSC '09 Proceedings of the 2009 International Conference on Intelligent Human-Machine Systems and Cybernetics - Volume 02
Fuzzy C-means and fuzzy swarm for fuzzy clustering problem
Expert Systems with Applications: An International Journal
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A multi-swarm PSO (MPSO) was proposed, with which the whole swarm is divided into by K-means clustering algorithm randomly to accelerate searching process of global optimum. The big swarm clustering will obey the standard PSO principle to search the global optimal result, which the number of particle is more than a threshold. The small swarm clustering will search randomly inner neighborhood of the global optimal value, and then the outlier particle does not care about the optimal result but flies freely according to themselves velocities and positions. The proposed algorithm enhances its global searching space, and enriches particles' diversity in order to let particles jump out local optimization points. Testing and comparing results with standard PSO and linearly decreasing weight PSO using several benchmark functions show the proposed algorithm is better than other algorithms. Furthermore, the MPSO algorithm is used to optimize the operational conditions in a chemical process case for an ethylene cracking furnace.