Signature PSO: A novel inertia weight adjustment using fuzzy signature for LQR tuning

Komarudin, Achmad; Setyawan, Novendra; Kamajaya, Leonardo; Achmadiah, Mas Nurul; Zulfatman, Zulfatman

doi:10.11591/eei.v10i1.2667

Cited by 8 publications

(1 citation statement)

References 19 publications

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“…where I(i, t) denotes the ith particle at the time t position function, X denotes the total number of particles passing through the binomial probability improvement position, p denotes the improvement success probability, q denotes the failure probability, the particle swarm is independent of each other, and p = q = 0.5. Komarudin et al [77] combined the multi-dimensional direct fuzzy signature, parametric feedback success counter, and CBPE methods to transform the fuzzy process into aggregation equations for the adaptive updating of inertia weights. Equation (38) represents the structural hierarchy of the inertia weights, Equation (39) represents the best particle to reach the minimization goal, Equation (40) represents the best particle percentage, Equation (41) represents the particle distribution in the search space, and Equation ( 42) represents the NCBPE normalization process for measuring the best fitness value of the solution:…”

Section: Algorithm Parameter Optimizationmentioning

confidence: 99%

A Survey on Particle Swarm Optimization for Association Rule Mining

Wang

Chen

et al. 2022

Electronics

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Association rule mining (ARM) is one of the core techniques of data mining to discover potentially valuable association relationships from mixed datasets. In the current research, various heuristic algorithms have been introduced into ARM to address the high computation time of traditional ARM. Although a more detailed review of the heuristic algorithms based on ARM is available, this paper differs from the existing reviews in that we expected it to provide a more comprehensive and multi-faceted survey of emerging research, which could provide a reference for researchers in the field to help them understand the state-of-the-art PSO-based ARM algorithms. In this paper, we review the existing research results. Heuristic algorithms for ARM were divided into three main groups, including biologically inspired, physically inspired, and other algorithms. Additionally, different types of ARM and their evaluation metrics are described in this paper, and the current status of the improvement in PSO algorithms is discussed in stages, including swarm initialization, algorithm parameter optimization, optimal particle update, and velocity and position updates. Furthermore, we discuss the applications of PSO-based ARM algorithms and propose further research directions by exploring the existing problems.

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Section: Algorithm Parameter Optimizationmentioning

confidence: 99%