2009 Second International Conference on Intelligent Computation Technology and Automation 2009
DOI: 10.1109/icicta.2009.53
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Improved Particle Swarm Optimization Using Two Novel Parallel Inertia Weights

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Cited by 6 publications
(4 citation statements)
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“…Experiment 5 (comparison of LDIW-PSO with DLPSO2) . The results for LIDIW-PSO and DLPSO2 [ 11 ] in Table 8 are compared based on the best fitness, mean best fitness, convergence speed, as well as standard deviation (Std. Dev.)…”
Section: Resultsmentioning
confidence: 99%
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“…Experiment 5 (comparison of LDIW-PSO with DLPSO2) . The results for LIDIW-PSO and DLPSO2 [ 11 ] in Table 8 are compared based on the best fitness, mean best fitness, convergence speed, as well as standard deviation (Std. Dev.)…”
Section: Resultsmentioning
confidence: 99%
“…In [ 11 ], two types of variants were proposed to solve the premature convergence problem of PSO. In this variant, two dynamic nonlinear methods and logistic chaotic map were used to adjust the inertia weight in parallel according to different fitness values.…”
Section: A Review Of Ldiw-pso and Some Of Its Competing Pso Varianmentioning
confidence: 99%
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“…The Linearly Decreasing [12] [14], Linearly increasing [15] , Nonlinear [16], Sigmoid decreasing [17] , Adaptive [18], Random [19], Chaotic [20], Oscillating [21], Logarithmic decreasing [22] , Exponent Decreasing inertia weight strategies [23], [24], [25] and Fuzzy Adaptive Inertia Weight [26] were used in the literature. The Linearly Decreasing strategy improves the efficiency and performance of PSO.…”
Section:  Inertia Weightmentioning
confidence: 99%