Adaptive hybrid harmony search optimization algorithm for point cloud fine registration

Yan, Yang; Li, M.; Ma, Xiaoyun

doi:10.1364/jot.88.000252

Cited by 3 publications

(2 citation statements)

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“…By introducing acceleration factors c1 and c2 into the basic SFLA [13], the ability of the worst individual to learn from best individual within the sub memeplexes or global best individual of the entire population was improved and the convergence rate of algorithm was accelerated. Meanwhile, some novel hybrid SFLA exhibited integrations in other intelligence algorithms, such as the genetic algorithm (GA) [10], simulated annealing (SA) [14], harmony search (HS) [15], particle swarm optimization (PSO) [16], moth-flame optimization (MFO) algorithm [17], and differential evolution (DE) algorithm [18] , which have been greatly advanced the hybridizing work of SFLA algorithms. Technically speaking, these improved algorithms or their variants can improve the SFLA's performance, such as faster convergence speed, higher accuracy of solution, increased local exploration ability and so on.…”

Section: Introductionmentioning

confidence: 99%

A modified shuffled frog leaping algorithm with inertia weight

Zhao

Wang

Liu

et al. 2023

Preprint

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The shuffled frog leaping algorithm (SFLA) is a promising metaheuristic bionics algorithm, which has been designed by the shuffled complex evolution (SCE) and the particle swarm optimization (PSO) framework. But it is easily trapped into local optimum and has the low optimization accuracy when it is used to optimize the complex engineering problems. To overcome the short-comings, a novel modified shuffled frog leaping algorithm (MSFLA) with inertia weight is proposed in this paper. To extend the scope of the direction and length of the updated worst frog (vector) of the original SFLA, the inertia weight α was introduced and its meaning and range of the new parameters are fully explained. Then the convergence of the MSFLA is deeply analyzed and proved theoretically by a new dynamic equation formed by Z-transform. Finally, we have compared the solution of 7 benchmark function with the original SFLA, other improved SFLAs, genetic algorithm (GA), PSO, artificial bee colony (ABC) algorithm, and the grasshopper optimization algorithm with invasive weed optimization (IWGOA). The testing results showed that the modified algorithms can effectively improve the solution accuracies and convergence properties, exhibited an excellent ability of global optimization in high-dimensional space and complex function problems.

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Section: Introductionmentioning

confidence: 99%

A modified shuffled frog leaping algorithm with inertia weight

Zhao

Wang

Liu

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…By introducing acceleration factors c 1 and c 2 into the basic SFLA 22 , the ability of the worst individual to learn from best individual within the sub-memeplexes or global best individual of the entire population was improved and the convergence rate of algorithm was accelerated. Meanwhile, some novel hybrid SFLA exhibited integrations in other intelligence algorithms, such as the genetic algorithm (GA) 19 , simulated annealing (SA) 23 , harmony search (HS) 24 , particle swarm optimization (PSO) 25 , which have been greatly advanced the hybridizing work of SFLA algorithms. Technically speaking, these improved algorithms or their variants can improve the SFLA's performance, such as faster convergence speed, higher accuracy of solution, increased local exploration ability and so on.…”

mentioning

confidence: 99%

A modified shuffled frog leaping algorithm with inertia weight

Zhao,

Wang,

Liu

et al. 2024

Sci Rep

View full text Add to dashboard Cite

The shuffled frog leaping algorithm (SFLA) is a promising metaheuristic bionics algorithm, which has been designed by the shuffled complex evolution and the particle swarm optimization (PSO) framework. However, it is easily trapped into local optimum and has the low optimization accuracy when it is used to optimize complex engineering problems. To overcome the shortcomings, a novel modified shuffled frog leaping algorithm (MSFLA) with inertia weight is proposed in this paper. To extend the scope of the direction and length of the updated worst frog (vector) of the original SFLA, the inertia weight α was introduced and its meaning and range of the new parameters are fully explained. Then the convergence of the MSFLA is deeply analyzed and proved theoretically by a new dynamic equation formed by Z-transform. Finally, we have compared the solution of the 7 benchmark functions with the original SFLA, other improved SFLAs, genetic algorithm, PSO, artificial bee colony algorithm, and the grasshopper optimization algorithm with invasive weed optimization. The testing results showed that the modified algorithms can effectively improve the solution accuracy and convergence property, and exhibited an excellent ability of global optimization in high-dimensional space and complex function problems.

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Registration of 3D point clouds using a local descriptor based on grid point normal

Wang

Kang

2021

Appl. Opt.

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The coarse-to-fine method is the prime technology for point cloud registration in 3D reconstruction. Aimed at the problem of low accuracy of coarse registration for the partially overlapping point clouds, a novel, to the best of our knowledge, 3D local feature descriptor named grid normals deviation angles statistics (GNDAS) for aligning roughly pairwise point clouds is proposed in this paper. The descriptor is designed by first dividing evenly the local surface into some grids along the x axis and y axis of the local reference frame, then making the statistics of the deviation angles of normals at grid points. Based on the correspondences generated by matching descriptors and a transformation estimation method, the initial registration result is obtained. The coarse registration result is used as the initial value of the iterative closest point algorithm to achieve the refinement of the registration result. Experimental comparisons on two public datasets demonstrate that our GNDAS descriptor has high descriptiveness and strong robustness to noise at low level and varying mesh resolution. The registration results also confirm the superiority of our registration approach over previous versions in accuracy and efficiency.

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Adaptive hybrid harmony search optimization algorithm for point cloud fine registration

Cited by 3 publications

References 0 publications

A modified shuffled frog leaping algorithm with inertia weight

A modified shuffled frog leaping algorithm with inertia weight

A modified shuffled frog leaping algorithm with inertia weight

Registration of 3D point clouds using a local descriptor based on grid point normal

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