Dynamic multi-swarm particle swarm optimizer with local search

By Jing J. Liang, Ponnuthurai Nagaratnam Suganthan. IEEE Congress on Evolutionary Computation, 2005

298 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/a77ef39d18fbca475edc567a85102402e79f9010

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Citations in the corpus

Accelerating Differential Evolution Using an Adaptive Local Search (2008, methodology): “Finally, we compared our proposed algorithm with the dynamic multiswarm particle swarm optimizer with LS (DMS-PSO) algorithm using the results reported in [47].”
Self-Adaptive Multimethod Search for Global Optimization in Real-Parameter Spaces (2009, methodology): “These methods are inspired by models of adaptation in natural systems, and typically use a genetic algorithm for global exploration of the search space (although recently also PSO algorithms are used: [29], combined with a local search heuristic for exploitation.”
Particle Swarm Optimization With an Aging Leader and Challengers (2013, unknown): “In the literature, various types of search operators have been used for this, including genetic operators such as selection [19], crossover [20], and mutation [12], [21]–[23]; evolutionary computation paradigms such as genetic algorithm (GA) [24], differential evolution [25], and ant colony optimiza”
Red deer algorithm (RDA): a new nature-inspired meta-heuristic (2020, background): “Regarding this fact, in production systems, there are shipping dates for orders, inventory carrying costs, which are incurred if the jobs (tasks) are ﬁnished earlier than the shipping dates (i.e., earliness).”
Adaptive Particle Swarm Optimization (2008, methodology): “, selection [21], crossover [22], and mutation [23], other techniques such as local search [24] and differential evolution [39] have been used to combine with PSO.”
The Social Engineering Optimizer (SEO) (2018, unknown): “In addition, all of them are adopted from CEC2005 (Liang and Suganthan, 2005) and CEC2010 (Zhao et al., 2010), too.”
S-shaped versus V-shaped transfer functions for binary Particle Swarm Optimization (2013, methodology): “Theminimizationandstatisticalresultsof Table5 ComparisonofVPSO,PBSO,MBPSO1,MBPSO2,BCLPSO,BFIPS,andBDMS-PSOonallbenchmarkfunctionsover30independentruns.”
Teaching–learning-based optimization algorithm for unconstrained and constrained real-parameter optimization problems (2012, methodology): “The performance of TLBO is compared with seven other optimization algorithms: PSOrecombination with dynamic linkage (PSO-RDL) (Jian 2006), PSO-dynamic multiswarm optimization (DMS-PSO) (Liang and Suganthan 2005), SPC-PNX (Ballester et al.”
A study on the use of non-parametric tests for analyzing the evolutionary algorithms’ behaviour: a case study on the CEC’2005 Special Session on Real Parameter Optimization (2009, unknown): “Average error rate obtained in CEC'2005 Special Session in dimension 10 A l g o r i t h m f 1 f 2 f 3 f 4 f 5 f 6 f 7 f 8 f 9 BLX-GL50 10 −9 10 −9 5.705 · 10 2 10 −9 10 −9 099 · 10 4.02 Algorithm f10 f11 f12 f13 f14 f15 f16 f17 f18 BLX-GL50 4.975 2.334 4.069 · 10 2 7.498 · 10 −1 2.172 4 · 10 2 9.349”