Global genetic learning particle swarm optimization with diversity enhancement by ring topology

Lin, Anping; Sun, Wei; Huang, Yu; Wu, Guohua; Tang, Hongwei

doi:10.1016/j.swevo.2018.07.002

Cited by 88 publications

(25 citation statements)

References 59 publications

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“…Typical variants can be summarized in the following four types: (1) neighborhood topology [29], [30]; (2) parameter control [31], [32]; (3) hybrid methods [33], [34] and (4) novel learning schemes [35], [36]. Since genetic algorithms (GAs) have good exploration ability, genetic learning PSO (GLPSO) has been proposed [37] to strength the performance of PSO by generating high-quality exemplars to guide the evolution of the particles [38].…”

Section: B Psomentioning

confidence: 99%

Particle Swarm Optimized Autonomous Learning Fuzzy System

Shen

Angelov

2021

IEEE Trans. Cybern.

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The antecedent and consequent parts of a first-order evolving intelligent system (EIS) determine the validity of the learning results and overall system performance. Nonetheless, the state-of-the-art techniques mostly stress on the novelty from the system identification point of view but pay less attention to the optimality of the learned parameters. Using the recently introduced autonomous learning multiple model (ALMMo) system as the implementation basis, this paper introduces a particle swarmbased approach for EIS optimization. The proposed approach is able to simultaneously optimize the antecedent and consequent parameters of ALMMo and effectively enhance the system performance by iteratively searching for optimal solutions in the problem spaces. In addition, the proposed optimization approach does not adversely influence the "one pass" learning ability of ALMMo. Once the optimization process is complete, ALMMo can continue to learn from new data to incorporate unseen data patterns recursively without a full retraining. Experimental studies with a number of real-world benchmark problems validate the proposed concept and general principles. It is also verified that the proposed optimization approach can be applied to other types of EISs with similar operating mechanisms.

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Section: B Psomentioning

confidence: 99%

Particle Swarm Optimized Autonomous Learning Fuzzy System

Shen

Angelov

2021

IEEE Trans. Cybern.

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“…Lin et al [ 32 ] proposed an enhanced genetic learning PSO (GL-PSO) algorithm for global optimisation. In GL-PSO, the genetic operators and a ring topology were employed for the generation of fitter exemplars, which were subsequently used to guide the swarm particles.…”

Section: Related Studiesmentioning

confidence: 99%

Section: Related Studiesmentioning

confidence: 99%

“…Moreover, most of the aforementioned existing PSO variants employed purely the single global best solution [ 19 , 22 , 24 , 31 , 32 , 34 , 35 , 39 , 41 , 44 , 45 , 51 ] to guide the search process. In addition, except for a few studies such as Lin et al [ 32 ], Srisukkham et al [ 42 ], Tan et al [ 27 ], and Yu et al [ 36 ], other existing work did not adopt any exemplar breeding strategies to enhance the optimal signals or generate hybrid leaders. Although some studies adopted diverse search mechanisms [ 19 , 24 , 27 , 33 , 42 , 52 ], the search processes in many existing studies [ 22 , 31 , 32 , 34 , 35 , 36 , 39 , 41 , 44 , 45 , 51 ] are mainly conducted by single position updating formula.…”

Section: Related Studiesmentioning

confidence: 99%

“…In addition, except for a few studies such as Lin et al [ 32 ], Srisukkham et al [ 42 ], Tan et al [ 27 ], and Yu et al [ 36 ], other existing work did not adopt any exemplar breeding strategies to enhance the optimal signals or generate hybrid leaders. Although some studies adopted diverse search mechanisms [ 19 , 24 , 27 , 33 , 42 , 52 ], the search processes in many existing studies [ 22 , 31 , 32 , 34 , 35 , 36 , 39 , 41 , 44 , 45 , 51 ] are mainly conducted by single position updating formula. Therefore, they are more likely to suffer from premature convergence.…”

Section: Related Studiesmentioning

confidence: 99%

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Feature Selection Using Enhanced Particle Swarm Optimisation for Classification Models

Xie

Zhang

Lim

et al. 2021

Sensors

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In this research, we propose two Particle Swarm Optimisation (PSO) variants to undertake feature selection tasks. The aim is to overcome two major shortcomings of the original PSO model, i.e., premature convergence and weak exploitation around the near optimal solutions. The first proposed PSO variant incorporates four key operations, including a modified PSO operation with rectified personal and global best signals, spiral search based local exploitation, Gaussian distribution-based swarm leader enhancement, and mirroring and mutation operations for worst solution improvement. The second proposed PSO model enhances the first one through four new strategies, i.e., an adaptive exemplar breeding mechanism incorporating multiple optimal signals, nonlinear function oriented search coefficients, exponential and scattering schemes for swarm leader, and worst solution enhancement, respectively. In comparison with a set of 15 classical and advanced search methods, the proposed models illustrate statistical superiority for discriminative feature selection for a total of 13 data sets.

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Genetic Learning Particle Swarm Optimization with Interlaced Ring Topology

Borowska

2020

Lecture Notes in Computer Science

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Global genetic learning particle swarm optimization with diversity enhancement by ring topology

Cited by 88 publications

References 59 publications

Particle Swarm Optimized Autonomous Learning Fuzzy System

Particle Swarm Optimized Autonomous Learning Fuzzy System

Feature Selection Using Enhanced Particle Swarm Optimisation for Classification Models

Genetic Learning Particle Swarm Optimization with Interlaced Ring Topology

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