Fractional-Order Ant Colony Algorithm: A Fractional Long Term Memory Based Cooperative Learning Approach

Pu, YoungGun; Siarry, Patrick; Zhu, Wu-Yang; Wang, Jian; Ni, Zhaohui

doi:10.1016/j.swevo.2021.101014

Cited by 27 publications

(2 citation statements)

References 38 publications

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“…A forbidden search operator containing five neighborhood operators was constructed to improve the local search capability of the algorithm, and a simulated annealing mechanism was introduced to update the global pheromone to increase the diversity of the population. Pu et al [ 24 ] suggested a novel fractional order ACO (FACA) algorithm, which is based on the cooperative learning approach of fractional long-term memory. The integer order ACO algorithm’s transformation behavior is altered by utilizing the inherent power of fractional algorithms, which is achieved by substituting straightforward one-step probabilities with more intricate fractional derivatives that include some forward-looking information.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Ant Colony Optimization with Sub-Population and Fuzzy Logic for 3D Laser Scanning Path Planning

Song,

Pu,

2024

Sensors

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For the precise measurement of complex surfaces, determining the position, direction, and path of a laser sensor probe is crucial before obtaining exact measurements. Accurate surface measurement hinges on modifying the overtures of a laser sensor and planning the scan path of the point laser displacement sensor probe to optimize the alignment of its measurement velocity and accuracy. This manuscript proposes a 3D surface laser scanning path planning technique that utilizes adaptive ant colony optimization with sub-population and fuzzy logic (SFACO), which involves the consideration of the measurement point layout, probe attitude, and path planning. Firstly, this study is based on a four-coordinate measuring machine paired with a point laser displacement sensor probe. The laser scanning four-coordinate measuring instrument is used to establish a coordinate system, and the relationship between them is transformed. The readings of each axis of the object being measured under the normal measuring attitude are then reversed through the coordinate system transformation, thus resulting in the optimal measuring attitude. The nominal distance matrix, which demonstrates the significance of the optimal measuring attitude, is then created based on the readings of all the points to be measured. Subsequently, a fuzzy ACO algorithm that integrates multiple swarm adaptive and dynamic domain structures is suggested to enhance the algorithm’s performance by refining and utilizing multiple swarm adaptive and fuzzy operators. The efficacy of the algorithm is verified through experiments with 13 popular TSP benchmark datasets, thereby demonstrating the complexity of the SFACO approach. Ultimately, the path planning problem of surface 3D laser scanning measurement is addressed by employing the proposed SFACO algorithm in conjunction with a nominal distance matrix.

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Section: Introductionmentioning

confidence: 99%

Adaptive Ant Colony Optimization with Sub-Population and Fuzzy Logic for 3D Laser Scanning Path Planning

Song,

Pu,

2024

Sensors

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“…Studies have introduced fractional calculus to traditional memristor elements and formulated various natural forms of fractional impedance [10][11][12]. Additionally, recent works have combined fractional calculus with classic swarm intelligence algorithms, spawning new methods such as fractional neural networks and fractional ant colony algorithms based on the fractional steepest descent approach; these algorithms have shown promising results [13][14][15][16][17]. In the realm of image processing, pioneering work by Pu et al laid the groundwork for applying fractional calculus.…”

Section: Introductionmentioning

confidence: 99%

Research on Application of Fractional Calculus Operator in Image Underlying Processing

Huang,

Qin,

Chen

et al. 2024

Fractal Fract

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Fractional calculus extends traditional, integer-based calculus to include non-integer orders, offering a powerful tool for a range of engineering applications, including image processing. This work delves into the utility of fractional calculus in two crucial aspects of image processing: image enhancement and denoising. We explore the foundational theories of fractional calculus together with its amplitude–frequency characteristics. Our focus is on the effectiveness of fractional differential operators in enhancing image features and reducing noise. Experimental results reveal that fractional calculus offers unique benefits for image enhancement and denoising. Specifically, fractional-order differential operators outperform their integer-order counterparts in accentuating details such as weak edges and strong textures in images. Moreover, fractional integral operators excel in denoising images, not only improving the signal-to-noise ratio but also better preserving essential features such as edges and textures when compared to traditional denoising techniques. Our empirical results affirm the effectiveness of the fractional-order calculus-based image-processing approach in yielding optimal results for low-level image processing.

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A Slime Mold Fractional-Order Ant Colony Optimization Algorithm for Travelling Salesman Problems

Rong

Gong

Wang

et al. 2021

Lecture Notes in Computer Science

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Fractional-Order Ant Colony Algorithm: A Fractional Long Term Memory Based Cooperative Learning Approach

Cited by 27 publications

References 38 publications

Adaptive Ant Colony Optimization with Sub-Population and Fuzzy Logic for 3D Laser Scanning Path Planning

Adaptive Ant Colony Optimization with Sub-Population and Fuzzy Logic for 3D Laser Scanning Path Planning

Research on Application of Fractional Calculus Operator in Image Underlying Processing

A Slime Mold Fractional-Order Ant Colony Optimization Algorithm for Travelling Salesman Problems

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