2006 1st International Symposium on Systems and Control in Aerospace and Astronautics
DOI: 10.1109/isscaa.2006.1627487
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A New Adaptive Well-Chosen Inertia Weight Strategy to Automatically Harmonize Global and Local Search Ability in Particle Swarm Optimization

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Cited by 30 publications
(8 citation statements)
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“…The second type is a time-varying inertia weight approach, in which the inertia weight differs with the number of iterations over time. A linearly decreasing inertia approach was introduced by Lei et al [14], which effectively contributed to the positive modification of PSO characteristics. Similarly, other linear methods [15] and nonlinear approaches [16] have proven to be effective inertia weight approaches.…”
Section: Inertia Weightmentioning
confidence: 99%
“…The second type is a time-varying inertia weight approach, in which the inertia weight differs with the number of iterations over time. A linearly decreasing inertia approach was introduced by Lei et al [14], which effectively contributed to the positive modification of PSO characteristics. Similarly, other linear methods [15] and nonlinear approaches [16] have proven to be effective inertia weight approaches.…”
Section: Inertia Weightmentioning
confidence: 99%
“…A hybrid PSO-LMS-DFE algorithm for MIMO channels was presented [23] to improve performance in terms of complexity. In single-carrier (SC) modulation methods, FDE is proposed, which is less sensitive to radio frequency impairments than OFDM [24]. In [25], an adaptive FD PSO equalization is used for a SC-FD (single carrier FD) multiple access system.…”
Section: Introductionmentioning
confidence: 99%
“…Balance of the exploration and exploitation is also controlled by the inertia weight (Bansal et al 2011). The control rules of inertia weight can be classified as follows: constant (Shi and Eberhart 1998); random (Eberhart and Shi 2001); time varying (linear decreasing Shi and Eberhart 1999, sigmoid increasing/decreasing Malik et al 2007, simulated annealing Al-Hassan et al 2006, Sugeno function Lei et al 2006, exponential decreasing law Chen et al 2006;Li and Gao 2009, logarithmic decreasing law Gao et al 2008); adaptive control using feedbacks of the optimization process (best fitness Saber et al 2006;Shi and Eberhart 2001, fitness of the current and previous iterations Yang et al 2007, global best and average local best fitness Arumugam and Rao 2008, particle rank Panigrahi et al 2008, distance to particle and global best positions Qin et al 2006 and distance to global best position Suresh et al 2008). The value of inertia weight mostly presented in the literature is within the range of [0.4, 0.9] (see Jordehi and Jasni 2013).…”
Section: Introductionmentioning
confidence: 99%