A Competitive Swarm Optimizer for Large Scale Optimization

By Ran Cheng, Yaochu Jin. IEEE Transactions on Cybernetics, 2015

926 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/9cfad16564dca92ce99ee0afa061f886d638c698

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Citations in the corpus

A Framework for Large-Scale Multiobjective Optimization Based on Problem Transformation (2018, methodology): “Among others a competitive single-objective PSO algorithm [13] was developed in 2015 which showed good results for large-scale benchmark problems with up to 5000 decision variables.”
Heterogeneous comprehensive learning particle swarm optimization with enhanced exploration and exploitation (2015, neutral): “The empirical analysis of exploration and exploitation abilities showed that CSO achieved a good balance between exploration and exploitation [18].”
Efficient Large-Scale Multiobjective Optimization Based on a Competitive Swarm Optimizer (2020, background): “where r0 and r1 are uniformly randomly distributed values in [0, 1] according to [45] and [49], respectively.”
DG2: A Faster and More Accurate Differential Grouping for Large-Scale Black-Box Optimization (2017, methodology): “Other methods such as swarm intelligence [18]–[20], memetic algorithms [21]–[23], differential evolution [24], [25], evolution strategies [26], and estimation of distribution algorithms [27], [28] have also gained popularity for large-scale optimization because of their ability to deal with black-bo”
INFO: An efficient optimization algorithm based on weighted mean of vectors (2022, background): “Currently, PSO has taken its place in the fields of large-scale optimisation problems[19], feature selection[20], single-objective optimization problem[21], multiobjective optimisation problems[22], and high-dimensional expensive problem[23].”
Feature selection for high-dimensional classification using a competitive swarm optimizer (2016, methodology): “However, a slower convergence rate of CSO-k NN than the PSO variants is expected, which indicates that CSO-k NN has better exploration ability that helps maintain a better swarm diversity, which has also been con-ﬁrmed by the empirical analyses presented in Cheng and Jin (2015).”
Evolutionary Large-Scale Multi-Objective Optimization: A Survey (2021, methodology): “The nondecomposition approaches search for well-converged solutions by suggesting efficient reproduction operators, which are represented by parameter adaptation based differential evolution [158] and new learning strategy based particle swarm optimization [24].”
Surrogate-Assisted Cooperative Swarm Optimization of High-Dimensional Expensive Problems (2017, background): “the competitive swarm optimizer [60], and the SL-PSO [53] showed better performance on preserving the diversity of the swarm and discouraging the premature convergence.”
A competitive mechanism based multi-objective particle swarm optimizer with fast convergence (2018, methodology): “The updated velocity v′ i and position p ′ i of the i-th particle are calculated using the following equations as suggested in competitive swarm optimizer [2]: v′ i = R1vi +R2(pw − pi), (3)”
A social learning particle swarm optimization algorithm for scalable optimization (2015, background): “As suggested in [9,58], pairwise comparison in selection can not only reduce memory usage, but also make it easier to adopt a compact encoding.”
Metaheuristics in large-scale global continues optimization: A survey (2015, methodology): “In [24], a competitive PSO (CPSO) was introduced based on the random pairwise competition mechanism to solve LSGO problems (up to 5000D).”
Test Problems for Large-Scale Multiobjective and Many-Objective Optimization (2017, background): “Despite the fact that largescale optimization has attracted much attention in singleobjective optimization [35]–[37], little work has been rep orted on large-scale multi- and many-objective optimization wit h few exceptions [38]–[40].”