Extended PSO Based Collaborative Searching for Robotic Swarms With Practical Constraints

Yang, Jian; Wang, Xin; Bauer, Péter

doi:10.1109/access.2019.2921621

Cited by 36 publications

(26 citation statements)

References 28 publications

(31 reference statements)

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“…(2) evaluating the particles, that is, calculating the fitness values; (3) searching for individual extrema P; (4) searching for the global optimal solution G; (5) modifying the particles' speeds and positions (Yang, Wang et al, 2019). The update equations are:…”

Section: Adaptive Mutation Particle Swarmmentioning

confidence: 99%

Non-destructive detection of highway hidden layer defects using a ground-penetrating radar and adaptive particle swarm support vector machine

Liu

Hao

Wang

et al. 2021

PeerJ Computer Science

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In this paper, a method that uses a ground-penetrating radar (GPR) and the adaptive particle swarm support vector machine (SVM) method is proposed for detecting and recognizing hidden layer defects in highways. Three common road features, namely cracks, voids, and subsidence, were collected using ground-penetrating imaging. Image segmentation was performed on acquired images. Original features were extracted from thresholded binary images and were compressed using the kl algorithm. The SVM classification algorithm was used for condition classification. For parameter optimization of the SVM algorithm, the grid search method and particle swarm optimization algorithm were used. The recognition rate using the grid search method was 88.333%; the PSO approach often yielded local maxima, and the recognition rate was 86.667%; the improved adaptive PSO algorithm avoided local maxima and increased the recognition rate to 91.667%.

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Section: Adaptive Mutation Particle Swarmmentioning

confidence: 99%

Non-destructive detection of highway hidden layer defects using a ground-penetrating radar and adaptive particle swarm support vector machine

Liu

Hao

Wang

et al. 2021

PeerJ Computer Science

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“…The strength of these approaches lies in using agents distributed in the workspace, sharing information to search for the optimum of a fitness function. In particular, this community strongly uses the PSO algorithm [13][14][15][18][19][20][21]28]. It is important to stress that optimization algorithms like PSO, can also drive a swarm of actual robots [29].…”

Section: Introductionmentioning

confidence: 99%

Control of a Robotic Swarm Formation to Track a Dynamic Target With Communication Constraints: Analysis and Simulation

Coquet¹,

Arnold²,

Bouvet³

2021

Preprint

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We describe and analyze the Local Charged Particle Swarm Optimization (LCPSO) algorithm, that we designed to solve the problem of tracking a moving target releasing scalar information in a constrained environment using a swarm of agents. This method is inspired by flocking algorithms and the PSO algorithm for function optimization. Four parameters drive LCPSO: the number of agents; the inertia weight; the attraction/repulsion weight; and the inter-agent distance. Using APF, we provide a mathematical analysis of the LCPSO algorithm under some simplifying assumptions. First, the swarm will aggregate and attain a stable formation, whatever the initial conditions. Second, the swarm moves thanks to an attractor in the swarm, which serves as a guide for the other agents to head for the target. By focusing on a simple application of target tracking with communication constraints, we then remove those assumptions one by one. We show the algorithm is resilient to constraints on the communication range, and the behavior of the target. Results on simulation confirm our theoretical analysis. This provides useful guidelines to understand and control the LCPSO algorithm as a function of swarm characteristics as well as the nature of the target.

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“…Particle Swarm Optimization [30] is a heuristic swarm intelligence algorithm inspired by the behavior of bird swarms in the biological world. Due to the simple and efficient implementation of particle swarm optimization, it has been widely used in unconstrained optimization problems [31], constrained optimization problems [32], and other practical applications.…”

Section: ) Introduction Of Particle Swarm Algorithmmentioning

confidence: 99%

Two-Stage Adaptive Constrained Particle Swarm Optimization Based on Bi-Objective Method

Qian

Wang

et al. 2020

IEEE Access

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For the sake of better balancing the relationship between diversity and convergence when handling constrained optimization problems, a two-stage adaptive constrained particle swarm optimization algorithm based on bi-objective method (TABC-PSO) is proposed. In accordance with different phases of the constraint process, the target-constraint space derived from the angle is partitioned adaptively, and simultaneously the global best particle is selected and the external archive set is safeguarded. In the first stage, the whole space is divided adaptively in term of the angular distribution of individual, and the feasible region is explored comprehensively. In the second stage, local regions are adaptively compartmentalized and in-depth exploitation is carried out. Primary and secondary external archive sets are established to maintain population diversity and accelerate convergence. The two phases are switched adaptively in light of the storage status of the two external archive sets. We evaluated TABC-PSO algorithm on the benchmark functions in CEC 2006 and CEC 2017. The experimental results show that TABC-PSO algorithm compared with other state-of-the-art algorithms can be superior to applied to test functions with different types of constraints and possesses a competitive search capability. INDEX TERMS Constrained optimization, particle swarm optimization algorithm, bi-objective optimization, adaptive.

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Extended PSO Based Collaborative Searching for Robotic Swarms With Practical Constraints

Cited by 36 publications

References 28 publications

Non-destructive detection of highway hidden layer defects using a ground-penetrating radar and adaptive particle swarm support vector machine

Non-destructive detection of highway hidden layer defects using a ground-penetrating radar and adaptive particle swarm support vector machine

Control of a Robotic Swarm Formation to Track a Dynamic Target With Communication Constraints: Analysis and Simulation

Two-Stage Adaptive Constrained Particle Swarm Optimization Based on Bi-Objective Method

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