An Improved Chaotic Optimization Algorithm Applied to a DC Electrical Motor Modeling

Fiori, Simone; Filippo, Ruben Di

doi:10.3390/e19120665

Cited by 11 publications

(10 citation statements)

References 15 publications

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“…Simple chaotic systems can provide a simplified framework and can be an efficient tool for the mentioned applications [ 12 , 13 , 14 , 15 ]. The chaotic systems are also useful in nonlinear programming (optimization) [ 16 ].…”

Section: Introductionmentioning

confidence: 99%

The Intricacies of Sprott-B System with Fractional-Order Derivatives: Dynamical Analysis, Synchronization, and Circuit Implementation

Lu,

Alexander,

Natiq

et al. 2023

Entropy

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Studying simple chaotic systems with fractional-order derivatives improves modeling accuracy, increases complexity, and enhances control capabilities and robustness against noise. This paper investigates the dynamics of the simple Sprott-B chaotic system using fractional-order derivatives. This study involves a comprehensive dynamical analysis conducted through bifurcation diagrams, revealing the presence of coexisting attractors. Additionally, the synchronization behavior of the system is examined for various derivative orders. Finally, the integer-order and fractional-order electronic circuits are implemented to validate the theoretical findings. This research contributes to a deeper understanding of the Sprott-B system and its fractional-order dynamics, with potential applications in diverse fields such as chaos-based secure communications and nonlinear control systems.

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Section: Introductionmentioning

confidence: 99%

The Intricacies of Sprott-B System with Fractional-Order Derivatives: Dynamical Analysis, Synchronization, and Circuit Implementation

Lu,

Alexander,

Natiq

et al. 2023

Entropy

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“…The chaotic imperialist competitive algorithm (CICA), which is an ICA-based algorithm combined with chaos theory, has shown excellent performance in global optimization problems [12]. However, its computational complexity is increased by the introduction of chaotic maps [13].…”

Section: Introductionmentioning

confidence: 99%

Efficient Chaotic Imperialist Competitive Algorithm with Dropout Strategy for Global Optimization

et al. 2020

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The imperialist competitive algorithm combined with chaos theory (CICA) demonstrates excellent performance in global optimization problems. However, its computational complexity increases with the introduction of chaotic maps. To address this, we integrate CICA with a dropout strategy that randomly samples the dimensions of each solution at each iteration of the computation. We investigate the potential of the proposed algorithm with different chaotic maps through six symmetric and six asymmetric benchmark functions. We also apply the proposed algorithm to AUVs’ path planning application showing its performance and effectiveness in solving real problems. The simulation results show that the proposed algorithm not only has low computational complexity but also enhances local search capability near the globally optimal solution with an insignificant loss in the success rate.

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“…Compared with other methods, this approach is easy to implement and not sensitive to the considered system. Thus, optimization algorithms are very popular for parameter identification, including the differential evolution (DE) algorithm [ 28 ], a hybrid algorithm that combines particle swarm optimization (PSO) and ant colony optimization (ACO) algorithms [ 29 ], the artificial bee colony (ABC) algorithm [ 30 ], the bird swarm algorithm (BSA) [ 31 ], and the improved hybrid chaotic optimization algorithms [ 32 , 33 ]. However, all of these optimization algorithms were proposed for the study of continuous chaotic systems.…”

Section: Introductionmentioning

confidence: 99%

Parameter Identification of Fractional-Order Discrete Chaotic Systems

Peng

Sun

et al. 2019

Entropy

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Research on fractional-order discrete chaotic systems has grown in recent years, and chaos synchronization of such systems is a new topic. To address the deficiencies of the extant chaos synchronization methods for fractional-order discrete chaotic systems, we proposed an improved particle swarm optimization algorithm for the parameter identification. Numerical simulations are carried out for the Hénon map, the Cat map, and their fractional-order form, as well as the fractional-order standard iterated map with hidden attractors. The problem of choosing the most appropriate sample size is discussed, and the parameter identification with noise interference is also considered. The experimental results demonstrate that the proposed algorithm has the best performance among the six existing algorithms and that it is effective even with random noise interference. In addition, using two samples offers the most efficient performance for the fractional-order discrete chaotic system, while the integer-order discrete chaotic system only needs one sample.

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An Improved Chaotic Optimization Algorithm Applied to a DC Electrical Motor Modeling

Cited by 11 publications

References 15 publications

The Intricacies of Sprott-B System with Fractional-Order Derivatives: Dynamical Analysis, Synchronization, and Circuit Implementation

The Intricacies of Sprott-B System with Fractional-Order Derivatives: Dynamical Analysis, Synchronization, and Circuit Implementation

Efficient Chaotic Imperialist Competitive Algorithm with Dropout Strategy for Global Optimization

Parameter Identification of Fractional-Order Discrete Chaotic Systems

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