Dissipative particle swarm optimization

By Xiao-Feng Xie, Wenjun Zhang, Zhilian Yang. Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600), 2002

320 citations recorded by the source corpus.

A dissipative particle swarm optimization is developed according to the self-organization of dissipative structure. The negative entropy is introduced to construct an opening dissipative system that is far-from-equilibrium so as to driving the irreversible evolution process with better fitness. The testing of two multimodal functions indicates it improves the performance effectively.

Subfields: Artificial Intelligence, Biomedical Engineering

View Paper: https://www.semanticscholar.org/paper/09873fdbc988b8cdcbe35af72adb0270b5fa2021

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Citations in the corpus

Particle swarm optimization algorithm: an overview (2017, methodology): “…(Riget and Vesterstrom 2002), predator-prey model (Gosciniak 2015), uncorrela-tive component analysis model (Fan et al. 2009), dissipative model (Xie et al. 2002), self-organizing model (Xie et al. 2004), life cycle model (Krink and Lovbjerg 2002), Bayesian optimization model (Monson and Seppi…”
Fitness-distance-ratio based particle swarm optimization (2003, background): “The latter researchers have also explored increasing diversity by increasing the randomness associated with the particle velocity and position updates [20], thereby discouraging swarm convergence, in the “Dissipative PSO” algorithm.”
Swarm Intelligence in Optimization (2008, methodology): “A dissipative PSO was described in [179] to increase randomness.”
A review of particle swarm optimization. Part I: background and development (2007, background): “Xie et al. (2002) turned to thermodynamics to resolve the issue.”
Comprehensive learning particle swarm optimizer for global optimization of multimodal functions (2006, methodology): “Negative entropy is used to discourage premature convergence in [27].”
Quantum-Inspired Particle Swarm Optimization for Valve-Point Economic Load Dispatch (2010, background): “Also four kinds of typical revised PSO are DPSO [31], HPSO [32], IPSO [33], and SPSO [34].”
Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients (2004, methodology): “improving the diversity of the population have been reported in the literature, considering the behavior of the particles in the swarm during the search [22]–[25].”
DEPSO: hybrid particle swarm with differential evolution operator (2003, background): “To maintain the diversity, the DPSO version [20] introduces random mutations on the xid o f particles with small probability cl, which is hard to be determined along with the evolution, at least not be too large to avoid disorganization of the swarm.”
Particle swarm optimization versus genetic algorithms for phased array synthesis (2004, methodology): “Many variants of the original particle swarm optimizer have been proposed, such as optimizing multiple subswarms simultaneously [38]–[40], adding negative entropy to stir up the particles [41], sharing discoveries among a local neighborhood of particles [42]–[45], time-varying search goals [46], usi”
Particle Swarm Optimization With an Aging Leader and Challengers (2013, methodology): “to replace the traditional global topology [13]–[17], hybridizing PSO with auxiliary search techniques [18]–[34], and using multiswarm techniques [35]–[38].”