A new design method for adaptive IIR system identification using hybrid CPSO and DE

Seck-Tuoh-Mora, Juan Carlos; Carlos, Juan

doi:10.1007/s11071-017-3383-7

Cited by 34 publications

(17 citation statements)

References 36 publications

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“…In the algorithm proposed in this work, a different strategy is taken, inspired by the operation of a cellular automaton, where the solution of a complex problem is achieved through simple operators’ cooperation. This inspiration has been proved before in the design of infinite-impulse filters ( Lagos-Eulogio et al, 2017 ) and a concurrent layout and scheduling problem ( Hernández-Gress et al, 2020 ), obtaining good results.…”

Section: Global-local Neighborhood Search Algorithm For the Fjspmentioning

confidence: 71%

“…Other related work using cellular automaton-like neighborhoods to design IIR filters can be consulted in Lagos-Eulogio et al (2017) . Recently, another discrete optimization algorithm has been presented for the concurrent layout scheduling problem in the job-shop environment ( Hernández-Gress et al, 2020 ), where the idea of cellular automata inspires the neighborhood strategy used by the proposed algorithm.…”

Section: Introductionmentioning

confidence: 99%

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A global-local neighborhood search algorithm and tabu search for flexible job shop scheduling problem

Serna¹,

Seck-Tuoh-Mora²,

Medina-Marín³

et al. 2021

PeerJ Computer Science

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The Flexible Job Shop Scheduling Problem (FJSP) is a combinatorial problem that continues to be studied extensively due to its practical implications in manufacturing systems and emerging new variants, in order to model and optimize more complex situations that reflect the current needs of the industry better. This work presents a new metaheuristic algorithm called the global-local neighborhood search algorithm (GLNSA), in which the neighborhood concepts of a cellular automaton are used, so that a set of leading solutions called smart-cells generates and shares information that helps to optimize instances of the FJSP. The GLNSA algorithm is accompanied by a tabu search that implements a simplified version of the Nopt1 neighborhood defined in Mastrolilli & Gambardella (2000) to complement the optimization task. The experiments carried out show a satisfactory performance of the proposed algorithm, compared with other results published in recent algorithms, using four benchmark sets and 101 test problems.

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Section: Global-local Neighborhood Search Algorithm For the Fjspmentioning

confidence: 71%

Section: Introductionmentioning

confidence: 99%

A global-local neighborhood search algorithm and tabu search for flexible job shop scheduling problem

Serna¹,

Seck-Tuoh-Mora²,

Medina-Marín³

et al. 2021

PeerJ Computer Science

Self Cite

View full text Add to dashboard Cite

show abstract

“…PSO has been extensively applied to solve diverse wireless communications optimization problems in different areas such as wireless sensor networks (WSNs) [204][205][206][207], cognitive radio networks (CRNs) [208][209][210][211][212], intelligent reflecting surfaces (IRSs) systems [213], edge computing [214][215][216], ad-hoc networks [217][218][219], signal processing [220][221][222] and antenna design [223][224][225]. PSO is used to tackle many WSNs challenges such as node localization, optimal deployment and clustering.…”

Section: B Wireless Communicationsmentioning

confidence: 99%

Particle Swarm Optimization: A Comprehensive Survey

et al. 2022

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Particle swarm optimization (PSO) is one of the most well-regarded swarm-based algorithms in the literature. Although the original PSO has shown good optimization performance, it still severely suffers from premature convergence. As a result, many researchers have been modifying it resulting in a large number of PSO variants with either slightly or significantly better performance. Mainly, the standard PSO has been modified by four main strategies: modification of the PSO controlling parameters, hybridizing PSO with other well-known meta-heuristic algorithms such as genetic algorithm (GA) and differential evolution (DE), cooperation and multi-swarm techniques. This paper attempts to provide a comprehensive review of PSO, including the basic concepts of PSO, binary PSO, neighborhood topologies in PSO, recent and historical PSO variants, remarkable engineering applications of PSO, and its drawbacks. Moreover, this paper reviews recent studies that utilize PSO to solve feature selection problems. Finally, eight potential research directions that can help researchers further enhance the performance of PSO are provided.

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“…The PSO algorithm is fast and suitable for parallel computing. Since chaotic mapping has deterministic, ergodic and stochastic properties, chaos mapping is introduced into PSO, which is called CPSO [ 34 , 35 ].…”

Section: The Principles Of Algorithmsmentioning

confidence: 99%

Modeling and Compensation of Random Drift of MEMS Gyroscopes Based on Least Squares Support Vector Machine Optimized by Chaotic Particle Swarm Optimization

Xing

Hou

Lin

et al. 2017

Sensors

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MEMS (Micro Electro Mechanical System) gyroscopes have been widely applied to various fields, but MEMS gyroscope random drift has nonlinear and non-stationary characteristics. It has attracted much attention to model and compensate the random drift because it can improve the precision of inertial devices. This paper has proposed to use wavelet filtering to reduce noise in the original data of MEMS gyroscopes, then reconstruct the random drift data with PSR (phase space reconstruction), and establish the model for the reconstructed data by LSSVM (least squares support vector machine), of which the parameters were optimized using CPSO (chaotic particle swarm optimization). Comparing the effect of modeling the MEMS gyroscope random drift with BP-ANN (back propagation artificial neural network) and the proposed method, the results showed that the latter had a better prediction accuracy. Using the compensation of three groups of MEMS gyroscope random drift data, the standard deviation of three groups of experimental data dropped from 0.00354°/normals, 0.00412°/normals, and 0.00328°/normals to 0.00065°/normals, 0.00072°/normals and 0.00061°/normals, respectively, which demonstrated that the proposed method can reduce the influence of MEMS gyroscope random drift and verified the effectiveness of this method for modeling MEMS gyroscope random drift.

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A new design method for adaptive IIR system identification using hybrid CPSO and DE

Cited by 34 publications

References 36 publications

A global-local neighborhood search algorithm and tabu search for flexible job shop scheduling problem

A global-local neighborhood search algorithm and tabu search for flexible job shop scheduling problem

Particle Swarm Optimization: A Comprehensive Survey

Modeling and Compensation of Random Drift of MEMS Gyroscopes Based on Least Squares Support Vector Machine Optimized by Chaotic Particle Swarm Optimization

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