An ACO-based Hyper-heuristic for Sequencing Many-objective Evolutionary Algorithms that Consider Different Ways to Incorporate the DM's Preferences

Rivera, Gilberto; Cruz-Reyes, Laura; Fernández, Eduardo; Gómez-Santillán, Claudia; Rangel-Valdez, Nelson; Coello, Carlos A. Coello

doi:10.1016/j.swevo.2022.101211

Cited by 5 publications

(1 citation statement)

References 43 publications

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“…Chuang et al [ 19 ] suggested a real-time two-stage ant colony (RTACO) algorithm to reduce the strain on the task offloading algorithm and create a dependable, high-performance edge computing system. Rivera et al [ 20 ] suggested HyperACO, a hyper-heuristic algorithm that seeks to identify the optimal combination of multiple interval ranking models that are embedded in MOEA to address a multi-objective optimization issue. HyperACO not only has the ability to choose the most suitable model, but also to combine existing models to accurately address a particular multi-objective optimization problem.…”

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%