Comprehensive learning particle swarm optimizer for global optimization of multimodal functions

By Jing J. Liang, A. K. Qin, Ponnuthurai Nagaratnam Suganthan, Subramanian Baskar. IEEE Transactions on Evolutionary Computation, 2006

3,597 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/2142bb3f3c3dbce455506c1e5cd15826f5c3093a

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Citations in the corpus

Complex Network Clustering by Multiobjective Discrete Particle Swarm Optimization Based on Decomposition (2014, background): “PSO can be applied to various difficult optimization problems such as noisy and dynamic problems [17]–[19].”
A review on particle swarm optimization algorithm and its variants to clustering high-dimensional data (2013, methodology): “In Comprehensive Learning PSO (CLPSO) algorithm, a new velocity updating function is proposed and employed to construct CLPSO and then the new algorithm is tested using a group of benchmark functions (Liang et al. 2006).”
Rosenbrock artificial bee colony algorithm for accurate global optimization of numerical functions (2011, methodology): “Tables 14 and 15 show that RABC performed better than other algorithms except for CLPSO.”
Self regulating particle swarm optimization algorithm (2015, methodology): “Recently, researchers focussed on developing new PSO algorithms that avoid this premature convergence problem [60, 12, 45, 52, 59, 36, 77, 9].”
Diversity enhanced particle swarm optimization with neighborhood search (2013, methodology): “For D = 30, MAX_FEs is set to 200,000 [34].”
Adaptive Particle Swarm Optimization (2008, methodology): “Further, the APSO can successfully jump out of the local optima on most of the multimodal functions and surpasses all the other algorithms on functions f7, f8, and f9, where the global optimum of f7 (Schwefel’s function) is far away from any of the local optima [53], and the globally best solutions ”
Enhancing particle swarm optimization using generalized opposition-based learning (2011, methodology): “Results of PSO-w, PSO-cf, UPSO, FDR-PSO, FIPS, CPSO-H and CLPSO are taken from Table 4 in [19].”
DE/BBO: a hybrid differential evolution with biogeography-based optimization for global numerical optimization (2010, background): “…optimization has been very active, producing different kinds of EC for optimization in the continuous domain, such as genetic algorithms [9], evolution strategy [10], evolutionary programming [8], particle swarm optimization [11], immune clonal algorithm [12], differential evolution [2], etc.”
RUN beyond the metaphor: An efficient optimization algorithm based on Runge Kutta method (2021, positive): “…SCADE (Nenavath & Jatoth, 2018), BMWOA (Heidari, Aljarah, et al., 2019), BWOA (Chen, Xu, et al., 2019), OBLGWO (Heidari, Abbaspour, et al., 2019), CAMES (Hansen, et al., 2003), GL25 (García-Martínez, et al., 2008), and CLPSO (Liang, et al., 2006) in solving the CEC-BC-2017 benchmark functions.”
INFO: An efficient optimization algorithm based on weighted mean of vectors (2022, methodology): “The calculated results prove the superior capability of the proposed INFO algorithm to solve benchmark functions compared to other optimizers, such as GA, PSO, BA, GWO, SCA, GSA, and the advanced algorithms like the SCADE, CGSCA, OBLGWO, RDWOA, CCMWOA, BMWOA, CLPSO, RCBA, and CBA.”
Self-adaptive learning based particle swarm optimization (2011, methodology): “In the first section, we stated that the performance of previous PSO variants on ill-conditioned problems is very poor [19,28,36,41].”
Seeker Optimization Algorithm for Optimal Reactive Power Dispatch (2009, methodology): “[43] proposed a variant of PSO called comprehensive learning particle swarm optimizer (CLPSO), which is adept at complex multimodal problems.”
S-shaped versus V-shaped transfer functions for binary Particle Swarm Optimization (2013, methodology): “Theminimizationandstatisticalresultsof Table5 ComparisonofVPSO,PBSO,MBPSO1,MBPSO2,BCLPSO,BFIPS,andBDMS-PSOonallbenchmarkfunctionsover30independentruns.”
Comprehensive Learning Particle Swarm Optimization Algorithm With Local Search for Multimodal Functions (2019, background): “It has been widely used in many benchmark problems [28], [36] and engineering applications [37], [38].”
Differential evolution based on covariance matrix learning and bimodal distribution parameter setting (2014, result): “CoBiDE was also compared with three other EAs: CLPSO [34], CMA-ES [25], and GL-25 [35].”