Nonlinear Inertia Weight Variation for Dynamic Adaptation in Particle Swarm Optimization

By Wudai Liao, Junyan Wang, Jiangfeng Wang. International Conference on Swarm Intelligence, 2011

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How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Particle Swarm Optimization: Technique, System and Challenges (2011, background): “There are some researchers that have develop inertia weight application, such as :[7], [8], [9], [10]”
Committee-Based Active Learning for Surrogate-Assisted Particle Swarm Optimization of Expensive Problems (2017, background): “4 with generation g as (9), where gmax is the maximal number of generations to be conducted [36]”
Particle Swarm Optimization for Single Objective Continuous Space Problems: A Review (2017, methodology): “The idea of decreasing inertia weight was extended by Chatterjee and Siarry (2006) in a way that a non-linear function was used to decrease the value of the inertia weight: ωi = (maxi−i) n maxin (ωs − ωe) + ωe, where ωs and ωe were starting and final inertia weights, and maxi was the maximum allowed”
Adaptive Particle Swarm Optimization (2008, background): “The time-varying controlling strategies proposed for the PSO parameters so far are based on the generation number in the PSO iterations using either linear [13], [18] or nonlinear [15] rules.”
Particle swarm optimization algorithm: an overview (2017, methodology): “…population information (Zhan et al. 2009), adjusted based on Bayesian techniques (Zhang et al. 2015), exponential decreasing inertia weight strategy (Lu et al. 2015a), declined according to the nonlinear function (Chatterjee and Siarry 2006), and Sugeno function (Lei et al. 2005) in search process.”