Orthogonal Learning Particle Swarm Optimization

By Zhi-hui Zhan, Jun Zhang, Yun Li, Yu-hui Shi. IEEE Transactions on Evolutionary Computation, 2009

738 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/c1fb5fc9d1e182ff353a49fa5a18621915a5c471

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Citations in the corpus

Self regulating particle swarm optimization algorithm (2015, methodology): “This list comprises of teaching and peer learning PSO [40], bidirectional teaching and peer-learning PSO [39], adaptive two-layer PSO with elitist learning strategy [38], orthogonal learning strategy for PSO [82], adaptive learning PSO [34], ageing mechanism transformed PSO [10], distance based loca”
Particle swarm optimization algorithm: an overview (2017, methodology): “(Zhan et al. 2011), to determine the initial population of”
A Novel Artificial Bee Colony Algorithm Based on Modified Search Equation and Orthogonal Learning (2013, methodology): “Each factor has three possible values shown in Table II [32], and we say that each factor has three levels.”
Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches (2015, background): “However, as cloud scheduling can face challenges of a high volume, velocity, and variety of data arriving during the runtime, a more efﬁcient use of search information and problem information during the evolutionary process should be explored [Zhang et al. 2011c; Zhan et al. ].”
Multiple Populations for Multiple Objectives: A Coevolutionary Technique for Solving Multiobjective Optimization Problems (2013, methodology): “1) applying other algorithms like GA [59], [60], ant colony optimization [61]–[63], DE [64]–[67], EDA, MA [68]–[72], and their improved variants (like enhanced PSO variants [73]–[76])”
Genetic Learning Particle Swarm Optimization (2016, methodology): “FIPS [26], CLPSO [27], and OLPSO [28], as described in Section III-A, focus on developing novel learning schemes and constructing effective exemplars.”
Bio inspired computing - A review of algorithms and scope of applications (2016, background): “…tries to focus on a more suitable candidate solution from among the locally available candidate solutions, based on different learning mechanisms (Zhan et al., 2011; Yang et al, 2013), a new movement direction is identified along with an inertial influence to gradually guide the particles…”
A social learning particle swarm optimization algorithm for scalable optimization (2015, methodology): “The proposed SL-PSO has been tested on 47 functions, including 12 widely used basic test functions [91,4,97,96,8] (f 1 to f 12, refer to Table 1), 28 functions taken from the whole test suite for CEC’13 special session on real-parameter optimization (f 13 to f 40, refer to [47]), and 7 functions tak”
Evolutionary Computation Meets Machine Learning: A Survey (2011, methodology): “[52] also used the OED in PSO to enhance the algorithm performance and developed orthogonal learning PSO (OLPSO).”
Particle Swarm Optimization for Single Objective Continuous Space Problems: A Review (2017, background): “2 for details on this issue) were investigated in (Zhan et al., 2011)27.”
Position-Transitional Particle Swarm Optimization-Incorporated Latent Factor Analysis (2022, background): “[57] propose an orthogonal learning strategy to adjust the flying direction of each particle for enhancing the swarm searching ability.”
Heterogeneous comprehensive learning particle swarm optimization with enhanced exploration and exploitation (2015, positive): “…optimization (OLPSO), orthogonal learning strategy was developed in which a particle learnt from the combination of its own best experience (cognitive learning) and its neighborhood best experience (social learning) to compromise the balance between the exploration and exploitation search [15].”