A Novel Convolutional Neural Network Model Based on Beetle Antennae Search Optimization Algorithm for Computerized Tomography Diagnosis

Chen, Dechao; Li, Xiang; Li, Shuai

doi:10.1109/tnnls.2021.3105384

Cited by 42 publications

(11 citation statements)

References 59 publications

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“…Chen et al [ 39 ] proposed a BASCNN method by applying a recently proposed meta-heuristic algorithm, i.e., beetle antenna search (BAS) [ 40 ], for optimization of CNN hyper-parameters. The BAS algorithm models food-sensing behaviors of beetles and searches for optimal solution in the search space using a single search agent.…”

Section: Related Studiesmentioning

confidence: 99%

Evolving Deep Architecture Generation with Residual Connections for Image Classification Using Particle Swarm Optimization

Lawrence

Zhang

Rogage

et al. 2021

Sensors

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Automated deep neural architecture generation has gained increasing attention. However, exiting studies either optimize important design choices, without taking advantage of modern strategies such as residual/dense connections, or they optimize residual/dense networks but reduce search space by eliminating fine-grained network setting choices. To address the aforementioned weaknesses, we propose a novel particle swarm optimization (PSO)-based deep architecture generation algorithm, to devise deep networks with residual connections, whilst performing a thorough search which optimizes important design choices. A PSO variant is proposed which incorporates a new encoding scheme and a new search mechanism guided by non-uniformly randomly selected neighboring and global promising solutions for the search of optimal architectures. Specifically, the proposed encoding scheme is able to describe convolutional neural network architecture configurations with residual connections. Evaluated using benchmark datasets, the proposed model outperforms existing state-of-the-art methods for architecture generation. Owing to the guidance of diverse non-uniformly selected neighboring promising solutions in combination with the swarm leader at fine-grained and global levels, the proposed model produces a rich assortment of residual architectures with great diversity. Our devised networks show better capabilities in tackling vanishing gradients with up to 4.34% improvement of mean accuracy in comparison with those of existing studies.

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Section: Related Studiesmentioning

confidence: 99%

Evolving Deep Architecture Generation with Residual Connections for Image Classification Using Particle Swarm Optimization

Lawrence

Zhang

Rogage

et al. 2021

Sensors

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“…Paper [ 25 ] integrated BAS with a recurrent neural network to create a novel robot control framework for redundant robotic manipulator trajectory planning and obstacle avoidance. BAS is applied in [ 26 ] to optimize the initial parameters of a convolutional neural network for medical imaging diagnosis, resulting in high accuracy and a short period of tuning time.…”

Section: Introductionmentioning

confidence: 99%

Egret Swarm Optimization Algorithm: An Evolutionary Computation Approach for Model Free Optimization

Chen

Francis

et al. 2022

Biomimetics

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A novel meta-heuristic algorithm named Egret Swarm Optimization Algorithm (ESOA) is proposed in this paper, which is inspired by two egret species’ hunting behavior (Great Egret and Snowy Egret). ESOA consists of three primary components: a sit-and-wait strategy, aggressive strategy as well as discriminant conditions. The learnable sit-and-wait strategy guides the egret to the most probable solution by applying a pseudo gradient estimator. The aggressive strategy uses random wandering and encirclement mechanisms to allow for optimal solution exploration. The discriminant model is utilized to balance the two strategies. The proposed approach provides a parallel framework and a strategy for parameter learning through historical information that can be adapted to most scenarios and has well stability. The performance of ESOA on 36 benchmark functions as well as 3 engineering problems are compared with Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Differential Evolution (DE), Grey Wolf Optimizer (GWO), and Harris Hawks Optimization (HHO). The result proves the superior effectiveness and robustness of ESOA. ESOA acquires the winner in all unimodal functions and reaches statistic scores all above 9.9, while the scores are better in complex functions as 10.96 and 11.92.

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“…This study is conducted by Winkler et al [16]. Another study is about drone path planning using lion swarm hybrid differential evolutionary algorithm made by Liu et al Lastly, different from swarm robotic applications, a medical imaging diagnostic system was studied for accelerating training speed and increasing accuracy using genetic and PSO algorithms by Chen et al [17]. As can be seen from the last research example, these algorithms find wide application not only in swarm robotics but also in many other fields.…”

Section: Introductionmentioning

confidence: 99%

Usage of Evolutionary Algorithms in Swarm Robotics and Design Problems

Turkler

Akkan

AKKAN>

2022

Sensors

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In this study, the general structure of swarm robotics is examined. Algorithms inspired by nature, which form the basis of swarm robotics, are introduced. Communication topologies in robotic swarms, which are similar to the communication methods between living things moving in nature, are included and how these can be used in swarm communication is emphasized. With the developed algorithms, how the swarm can imitate nature and what tasks it can perform have been explained. The various problems that will be encountered in terms of the design of the optimization methods used during the control of the swarm and the solutions are simulated using the Webots software. As a result, ideas on the solutions of these problems and suggestions are proposed.

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A Novel Convolutional Neural Network Model Based on Beetle Antennae Search Optimization Algorithm for Computerized Tomography Diagnosis

Cited by 42 publications

References 59 publications

Evolving Deep Architecture Generation with Residual Connections for Image Classification Using Particle Swarm Optimization

Evolving Deep Architecture Generation with Residual Connections for Image Classification Using Particle Swarm Optimization

Egret Swarm Optimization Algorithm: An Evolutionary Computation Approach for Model Free Optimization

Usage of Evolutionary Algorithms in Swarm Robotics and Design Problems

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