Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients

By Asanga Ratnaweera, Saman Kumara Halgamuge, Harry C. Watson. IEEE Transactions on Evolutionary Computation, 2004

3,011 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/145e7f1f5a1f2f618b064fe5344cd292cd0b820c

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Citations in the corpus

A New Particle Swarm Algorithm and Its Globally Convergent Modifications (2011, methodology): “Since a mutated particle in [33]–[35] is assigned with a random value in the solution space, they need a relatively long time to carry out a precise search in the already sampled regions compared with the standard PSO.”
Frankenstein's PSO: A Composite Particle Swarm Optimization Algorithm (2009, neutral): “[16] proposed the self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients (HPSOTVAC), in which the inertia term in the velocity-update rule is eliminated.”
Self-adaptive learning based particle swarm optimization (2011, background): “As an important branch of swarm intelligence, PSO has attracted public attention from the research community and has been successfully implemented in various scientific and engineering applications, such as mixed discrete nonlinear programming [38], nearest neighborhood classification [8], receive-d”
Orthogonal Learning Particle Swarm Optimization (2009, methodology): “Further, a re-initialization operation and a self-organizing hierarchical technique have been used together with time-varying acceleration coefficients in order to bring diversity into the population [31].”
A New Particle Swarm Optimization Solution to Nonconvex Economic Dispatch Problems (2007, background): “After the introduction of PSO, many variations have been proposed for the basic PSO by various researchers [27]–[32].”
Self-Organizing Hierarchical Particle Swarm Optimization for Nonconvex Economic Dispatch (2008, background): “Index Terms—Nonconvex economic dispatch (NCED), premature convergence, prohibited operating zones (POZ), ramp rate limits, self-organizing hierarchical particle swarm optimization (SOH_PSO), time-varying acceleration coefficients (TVAC), valve point loading effect.”
Adaptive Particle Swarm Optimization for Simultaneous Design of UPFC Damping Controllers (2014, background): “Recently, a time-varying acceleration coefficient PSO (TVAC-PSO) is proposed to reduces the cognitive component and increases the social component of acceleration coefficient, c1 and c2, with time [22].”
Iteration PSO with time varying acceleration coefficients for solving non-convex economic dispatch problems (2012, background): “It is observed that original PSO suffers from premature convergence, especially for problems with multiple local optimums [26,27].”
Genetic Learning Particle Swarm Optimization (2016, methodology): “On contrast, HPSO-TVAC, OLPSO, and GL-PSO successfully overcome this deficiency and achieve high solution accuracy.”
Comprehensive learning particle swarm optimizer for global optimization of multimodal functions (2006, background): “In [11], inertia weight is set at zero, except at the time of reinitialization.”
Economic dispatch using particle swarm optimization: A review (2009, neutral): “Hence to increase the ability of global optimal solution at later stage, PSO with time varying acceleration coefficient (PSO-TVAC) [70], centroidal voronoi tessellation based initialization of swarm [80], active target particle swarm optimization (APSO) [81], exponential varying inertia weight”
Adaptive Particle Swarm Optimization (2008, methodology): “methods, the HPSO-TVAC shows the best overall performance [18].”