Parameter Selection in Particle Swarm Optimization
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
Population structure and particle swarm performance
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Population structure and particle swarm performance
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Markov chain models of bare-bones particle swarm optimizers
Proceedings of the 9th annual conference on Genetic and evolutionary computation
An Adaptive Particle Swarm Optimization for Global Optimization
ICNC '07 Proceedings of the Third International Conference on Natural Computation - Volume 04
Benchmark Tests of Robust Modified Particle Swarm Optimization
ICNC '07 Proceedings of the Third International Conference on Natural Computation - Volume 04
A novel memetic algorithm for global optimization based on PSO and SFLA
ISICA'07 Proceedings of the 2nd international conference on Advances in computation and intelligence
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
The fully informed particle swarm: simpler, maybe better
IEEE Transactions on Evolutionary Computation
Stability analysis of the particle dynamics in particle swarm optimizer
IEEE Transactions on Evolutionary Computation
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In the later period of optimization by particle swarm optimization (PSO) algorithm, the diversity scarcity of population easily causes the algorithm fall into the local optimum. Therefore, an improved PSO (IPSO) algorithm is presented, in which each particle has the ability of keeping its inertia motion and learning from another randomly selected particle with better performance; moreover, for the particle with better performance, the inertia weight will be larger and the learning coefficient will be smaller. Thus, for the particles sorted in order of decreasing performance, the inertia weights are decreased and the learning rate coefficients are increased gradually. The new learning approach develops the diversity of the population, while the new parameters setting approach develops the adaptability of the population. Comparison results with the basic PSO on the examination of some well-known benchmark functions show that the IPSO algorithm has higher searching speed and stronger global searching ability.