A Hierarchical Sorting Swarm Optimizer for Large-Scale Optimization

Lan, Rushi; Zhang, Li; Tang, Zhiling; Liu, Zhenbing; Luo, Xiangfeng

doi:10.1109/access.2019.2906082

Cited by 20 publications

(4 citation statements)

References 39 publications

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“…Each particle tracks its coordinates in exploration area. The coordinates of each particle tracks are associated with the best solution [ 15 ] (fitness) achieved by itself so far, which is called

, and the entire group’s best solution, which is called

. The particle can be described as

, where

is the current position of particle, and

is the current velocity of particle.…”

Section: Methodsmentioning

confidence: 99%

Particle Swarm Optimization-Based Approach for Optic Disc Segmentation

Ran

Yang

2022

Entropy

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Fundus segmentation is an important step in the diagnosis of ophthalmic diseases, especially glaucoma. A modified particle swarm optimization algorithm for optic disc segmentation is proposed, considering the fact that the current public fundus datasets do not have enough images and are unevenly distributed. The particle swarm optimization algorithm has been proved to be a good tool to deal with various extreme value problems, which requires little data and does not require pre-training. In this paper, the segmentation problem is converted to a set of extreme value problems. The scheme performs data preprocessing based on the features of the fundus map, reduces noise on the picture, and simplifies the search space for particles. The search space is divided into multiple sub-search spaces according to the number of subgroups, and the particles inside the subgroups search for the optimal solution in their respective sub-search spaces. The gradient values are used to calculate the fitness of particles and contours. The entire group is divided into some subgroups. Every particle flies in their exploration for the best solution. During the iteration, particles are not only influenced by local and global optimal solutions but also additionally attracted by particles between adjacent subgroups. By collaboration and information sharing, the particles are capable of obtaining accurate disc segmentation. This method has been tested with the Drishti-GS and RIM-ONE V3 dataset. Compared to several state-of-the-art methods, the proposed method substantially improves the optic disc segmentation results on the tested datasets, which demonstrates the superiority of the proposed work.

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, and the entire group’s best solution, which is called

. The particle can be described as

, where

is the current position of particle, and

is the current velocity of particle.…”

Section: Methodsmentioning

confidence: 99%

Particle Swarm Optimization-Based Approach for Optic Disc Segmentation

Ran

Yang

2022

Entropy

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“…To evaluate the effectiveness of the DSRegPSO algorithm, we used the CEC'13 benchmark functions, which are widely used in similar works like those in [41][42][43][44][45][46][47] as a standard test suite for LSGO problems. The CEC'13 test is composed of 15 optimization functions, including the Sphere Function, Elliptic Function, Rastrigin's Function, Ackley's Function, Schwefel's Problem 1.2 Function, Rosenbrock's Function, and their variants [44].…”

Section: Contributionmentioning

confidence: 99%

Dynamical Sphere Regrouping Particle Swarm Optimization: A Proposed Algorithm for Dealing with PSO Premature Convergence in Large-Scale Global Optimization

Rivera,

Guerrero-Mendez,

Lopez-Betancur

et al. 2023

Mathematics

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Optimizing large-scale numerical problems is a significant challenge with numerous real-world applications. The optimization process is complex due to the multi-dimensional search spaces and possesses several locally optimal regions. In response to this issue, various metaheuristic algorithms and variations have been developed, including evolutionary and swarm intelligence algorithms and hybrids of different artificial intelligence techniques. Previous studies have shown that swarm intelligence algorithms like PSO perform poorly in high-dimensional spaces, even with algorithms focused on reducing the search space. However, we propose a modified version of the PSO algorithm called Dynamical Sphere Regrouping PSO (DSRegPSO) to avoid stagnation in local optimal regions. DSRegPSO is based on the PSO algorithm and modifies inertial behavior with a regrouping dynamical sphere mechanism and a momentum conservation physics effect. These behaviors maintain the swarm’s diversity and regulate the exploration and exploitation of the search space while avoiding stagnation in optimal local regions. The DSRegPSO mechanisms mimic the behavior of birds, moving particles similar to birds when they look for a new food source. Additionally, the momentum conservation effect mimics how birds react to collisions with the boundaries in their search space or when they are looking for food. We evaluated DSRegPSO by testing 15 optimizing functions with up to 1000 dimensions of the CEC’13 benchmark, a standard for evaluating Large-Scale Global Optimization used in Congress on Evolutionary Computation, and several journals. Our proposal improves the behavior of all variants of PSO registered in the toolkit of comparison for CEC’13 and obtains the best result in the non-separable functions against all the algorithms.

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“…Lan et al [53] developed a hierarchical sorting swarm optimiser (HSSO) to solve large-scale optimisation problems. HSSO incorporated a new learning strategy to sort the particles into a hierarchical structure based on fitness scores.…”

Section: Variants Of Particle Swarm Optimisationmentioning

confidence: 99%

An evolving ensemble model of multi-stream convolutional neural networks for human action recognition in still images

Slade

Zhang

et al. 2022

Neural Comput & Applic

Self Cite

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Still image human action recognition (HAR) is a challenging problem owing to limited sources of information and large intra-class and small inter-class variations which requires highly discriminative features. Transfer learning offers the necessary capabilities in producing such features by preserving prior knowledge while learning new representations. However, optimally identifying dynamic numbers of re-trainable layers in the transfer learning process poses a challenge. In this study, we aim to automate the process of optimal configuration identification. Specifically, we propose a novel particle swarm optimisation (PSO) variant, denoted as EnvPSO, for optimal hyper-parameter selection in the transfer learning process with respect to HAR tasks with still images. It incorporates Gaussian fitness surface prediction and exponential search coefficients to overcome stagnation. It optimises the learning rate, batch size, and number of re-trained layers of a pre-trained convolutional neural network (CNN). To overcome bias of single optimised networks, an ensemble model with three optimised CNN streams is introduced. The first and second streams employ raw images and segmentation masks yielded by mask R-CNN as inputs, while the third stream fuses a pair of networks with raw image and saliency maps as inputs, respectively. The final prediction results are obtained by computing the average of class predictions from all three streams. By leveraging differences between learned representations within optimised streams, our ensemble model outperforms counterparts devised by PSO and other state-of-the-art methods for HAR. In addition, evaluated using diverse artificial landscape functions, EnvPSO performs better than other search methods with statistically significant difference in performance.

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A Hierarchical Sorting Swarm Optimizer for Large-Scale Optimization

Cited by 20 publications

References 39 publications

Particle Swarm Optimization-Based Approach for Optic Disc Segmentation

Particle Swarm Optimization-Based Approach for Optic Disc Segmentation

Dynamical Sphere Regrouping Particle Swarm Optimization: A Proposed Algorithm for Dealing with PSO Premature Convergence in Large-Scale Global Optimization

An evolving ensemble model of multi-stream convolutional neural networks for human action recognition in still images

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