Particle swarm optimization with Gaussian mutation

By N. Higashi, Hitoshi Iba. Proceedings of the 2003 IEEE Swarm Intelligence Symposium. SIS'03 (Cat. No.03EX706), 2003

601 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/12e9ca5f426eabdc5a9063946aaac99404616219

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Citations in the corpus

A review on particle swarm optimization algorithm and its variants to clustering high-dimensional data (2013, background): “Hiqushi and Iba (2003) proposed the PSO with Gaussian mutation (GPSO), whose global searching ability is more efficient than the standard PSO.”
Gaussian quantum-behaved particle swarm optimization approaches for constrained engineering design problems (2010, methodology): “…probability distributions to generate random numbers to updating the velocity equation of PSO have been proposed (Coelho & Alotto, 2008; Coelho & Herrera, 2007; Coelho & Lee, 2008; Higashi & Iba, 2003; Kennedy, 2003; Krohling, 2004; Krohling & Coelho, 2006a, 2006b; Secrest & Lamont, 2003).”
Swarm Intelligence in Optimization (2008, methodology): “Gaussian distribution was employed in a PSO variant as a mutation operator in [79].”
Fundamentals of natural computing: an overview (2007, background): “Recent research into the particle swarm optimization algorithm has emphasized its application to multi-objective, dynamic, constrained and combinatorial optimization problems [33,52,214,287]; theoretical investigations, such as scalability issues, novel operators and convergence analysis [41, 136,28”
A review of particle swarm optimization. Part II: hybridisation, combinatorial, multicriteria and constrained optimization, and indicative applications (2008, methodology): “Gaussian mutation was combined with velocity and position update rules by Higashi and Iba (2003) and was tested on unimodal and multimodal functions. The hybrid achieved better results than those of GA and PSO alone. Juang (2004) also incorporated mutation alongside crossover and elitism.”
Self regulating particle swarm optimization algorithm (2015, methodology): “The techniques include embedding the unique evolutionary operators: selection, crossover and mutation inside PSO [43, 25], differential evolution [80, 19], diversity enhancing mechanism [66, 72], avoidance mechanism [53], combination of multi-crossover with bee colony mechanism [44], PSO with levy f”
A social learning particle swarm optimization algorithm for scalable optimization (2015, background): “Other specific search operators have also been incorporated into PSO, including mutation operators [29,51,61], local search operators [83,86], and some nature-inspired operators as well, such as the niching PSO [5], culture-based PSO [13] and the aging theory inspired PSO (ALC-PSO) [8].”
A New Particle Swarm Algorithm and Its Globally Convergent Modifications (2011, background): “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.”
DEPSO: hybrid particle swarm with differential evolution operator (2003, background): “Keywords: Particle swarm optimization, differential evolution, numerical optimization.”
Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients (2004, background): “Further, possible use of the concept of " mutation " in PSO (as explained in genetic algorithms), as a performance enhancing strategy, has also been investigated [38].”
Diversity enhanced particle swarm optimization with neighborhood search (2013, neutral): “(cid:6) PSO with Gaussian mutation (PSO-GM) [21].”
A Competitive Swarm Optimizer for Large Scale Optimization (2015, background): “Other hybrid PSO variants include PSO with Gaussian mutation [34], PSO with chaos [35], orthogonal learning PSO (OLPSO) [36], PSO with a moderate-random-search strategy [37], and a very recently proposed PSO with a periodic mutation strategy and neural networks [38].”
Coevolutionary Particle Swarm Optimization Using Gaussian Distribution for Solving Constrained Optimization Problems (2006, background): “Higashi and Iba [20] and Stacey et al.”