An Adaptive Fuzzy Weight PSO Algorithm

Shi, Yang; Ouyang, Cheng; Zhu, Ping; Tang, Weidong

doi:10.1109/icgec.2010.10

Cited by 17 publications

(7 citation statements)

References 3 publications

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“…These parameters can have a large impact on optimization performance. Selection of the optimum values for all these parameters has been the subject of much research (Shi and Eberhart, 1999;Uy et al, 2007;Xin et al, 2009;Liu et al, 2010;Jordehi and Jashi, 2013). The aim of all metaheuristics techniques is to iteratively improve the quality of the population.…”

Section: Nn-based Ranging With the Pso Algorithmmentioning

confidence: 99%

In-Body Ranging for Ultra-Wide Band Wireless Capsule Endoscopy Using Neural Networks Based on Particle Swarm Optimization

Kanaan

Akay

Suveren

2018

Scitech

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ABSTRACT:We consider the problem of accurate in-body ranging for localization of a wireless capsule endoscope utilizing ultra-wide band (UWB) signaling. In this context, we explore the joint use of neural network structures and learning algorithms based on metaheuristics, an example of which is particle swarm optimization (PSO). The contributions of this paper are three-fold. First, we undertake a systematic performance analysis of the PSO technique for UWB-based in-body ranging and propose an improved version of the PSO algorithm. Second, we quantitatively compare the performance of PSO techniques against more traditional learning algorithms, such as Bayesian Regularization, LevenbergMarquardt and Single Conjugate Gradient. Third, we quantify the impact of activation functions used to define the neural network structure on performance. Our results indicate that PSO-based techniques can outperform traditional techniques by as much as 44%, depending on the activation functions used in the neural network.

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Section: Nn-based Ranging With the Pso Algorithmmentioning

confidence: 99%

In-Body Ranging for Ultra-Wide Band Wireless Capsule Endoscopy Using Neural Networks Based on Particle Swarm Optimization

Kanaan

Akay

Suveren

2018

Scitech

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“…Because of its simplicity and operability, parameters adjustment has become an important modification to improve the PSO. A typical method of adjustment for inertial weight includes linear decreasing or increasing weight [6], self-weighted linear weight [7], Gauss distribution weight [8], fussy adaptive weight [9], and so on. Meanwhile, the adjustment based on learning factor can balance the individual cognition and population one, which mainly includes contraction factor (CF) and variable one [10,11].…”

Section: Introductionmentioning

confidence: 99%

Team Collaboration Particle Swarm Optimization and Its Application on Reliability Optimization

Zheng¹,

Ma²,

Zhang³

et al. 2021

IJCIS

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Particle swarm optimization (PSO) tends to be premature convergence due to easily trapping into local suboptimal areas. In order to overcome the PSO's defects, the reasons causing the defects are analyzed and summarized as population diversity deficiency, insufficient information sharing, unbalance of exploitation and exploration, and single update strategy. On this basis, inspired by human team collaboration behavior, a team collaboration particle swarm optimization (TCPSO) is proposed. Diversified updates strategies, dynamic grouping strategy, selectivity vector, and decreasing and increasing inertia weight are designed in TCPSO to solve the defects' reasons and improve the optimization performance. Eight typical test functions have been used to evaluate and compare the performance of different PSO variants, and the results have been proven that the optimal results found by TCPSO are better compared with other PSO variants, which demonstrates the rationality and effectiveness of TCPSO. Finally, a real-world problem for reliability optimization are solved by five algorithms, and the results prove the convergence rate and stable optimization performance of TCPSO, TCPSO can provide better support for reliability optimization of complex system.

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“…These parameters are introduced so that exploration and exploitation can be improved and better performance can be achieved. Fuzzy systems have been used in finetuning the parameters of optimization algorithms so that better performance is achieved [25][26][27]. In GSA, fuzzy systems have been used to tune GSA's gravitational constant, epsilon and alpha [20][21][22][23].…”

Section: Introductionmentioning

confidence: 99%

Adaptive switching gravitational search algorithm: an attempt to improve diversity of gravitational search algorithm through its iteration strategy

Aziz¹,

Ibrahim²,

Mubin³

et al. 2017

Sādhanā

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An adaptive gravitational search algorithm (GSA) that switches between synchronous and asynchronous update is presented in this work. The proposed adaptive switching synchronous-asynchronous GSA (ASw-GSA) improves GSA through manipulation of its iteration strategy. The iteration strategy is switched from synchronous to asynchronous update and vice versa. The switching is conducted so that the population is adaptively switched between convergence and divergence. Synchronous update allows convergence, while switching to asynchronous update causes disruption to the population's convergence. The ASw-GSA agents switch their iteration strategy when the best found solution is not improved after a period of time. The period is based on a switching threshold. The threshold determines how soon is the switching, and also the frequency of switching in ASw-GSA. ASw-GSA has been comprehensively evaluated based on CEC2014's benchmark functions. The effect of the switching threshold has been studied and it is found that, in comparison with multiple and early switches, one-time switching towards the end of the search is better and substantially enhances the performance of ASw-GSA. The proposed ASw-GSA is also compared to original GSA, particle swarm optimization (PSO), genetic algorithm (GA), bat-inspired algorithm (BA) and grey wolf optimizer (GWO). The statistical analysis results show that ASw-GSA performs significantly better than GA and BA and as well as PSO, the original GSA and GWO.12

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An Adaptive Fuzzy Weight PSO Algorithm

Cited by 17 publications

References 3 publications

In-Body Ranging for Ultra-Wide Band Wireless Capsule Endoscopy Using Neural Networks Based on Particle Swarm Optimization

In-Body Ranging for Ultra-Wide Band Wireless Capsule Endoscopy Using Neural Networks Based on Particle Swarm Optimization

Team Collaboration Particle Swarm Optimization and Its Application on Reliability Optimization

Adaptive switching gravitational search algorithm: an attempt to improve diversity of gravitational search algorithm through its iteration strategy

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