Defining a Standard for Particle Swarm Optimization

By Daniel Bratton, James Kennedy. IEEE Symposium on Swarm Intelligence, 2007

1,353 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/3d0feaed033b0689b624428f65408d2d13664af7

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In Artificial Intelligence

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2. Wei Fang
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4. Xiaojun Wu
5. Lei Liu
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In Computational Theory and Mathematics

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Citations in the corpus

• Quantum-Behaved Particle Swarm Optimization: Analysis of Individual Particle Behavior and Parameter Selection (2012, methodology): “For the standard PSO, the LBEST ring topology was used with other parameters set as those in PSO-Co (Bratton and Kennedy, 2007).”
• A Comprehensive Review of Swarm Optimization Algorithms (2015, background): “0 with the aim of encouraging exploration at an early stage and exploitation of the best area found towards the end [62].”
• Transforming geocentric cartesian coordinates to geodetic coordinates by using differential search algorithm (2012, methodology): “, Duncan, Bonferroni, Sheffe, Fisher, Holm, and Tukey correction (Bratton and Kennedy, 2007).”
• Fireworks Algorithm for Optimization (2010, background): “Since then, the PSO has attracted the attentions of researchers around the globe, and a number of variants of PSO have been continually proposed [7, 8].”
• Modified cuckoo search: A new gradient free optimisation algorithm (2011, methodology): “This is done for the same reasons that the inertia constant is reduced in the PSO [3], i.”
• Particle swarm optimization algorithm: an overview (2017, methodology): “Most researches (Bratton and Kennedy 2007; Kennedy andMendes2002;Li2010)usedthestatictopologystructure.”
• Particle Swarm Optimization for Single Objective Continuous Space Problems: A Review (2017, background): “For example, in Bratton and Kennedy (2007), a set of parameters for SPSO were suggested which resulted in a good performance of the algorithm on some benchmark problems.”
• A review of population-based meta-heuristic algorithm (2013, result): “According to Bratton and Kennedy [53], the lbest model can return better results than the gbest model in many problems however; it might have lower convergence rate than gbest model.”
• Particle Swarm Optimization (PSO) for the constrained portfolio optimization problem (2011, methodology): “The detailed algorithm can be found in the paper written by Bratton & Kennedy, 2007.”
• Backtracking Search Optimization Algorithm for numerical optimization problems (2013, background): “The problem-based pairwise statistical comparisons for the solutions obtained in Test 2 (see Table 9) show that the algorithms closest to BSA in terms of problem-solving success are in the order of JDE, CLPSO, CMAES, SADE, ABC and PSO.”