A biologically inspired improvement strategy for particle filter: Ant colony optimization assisted particle filter

Zhong, Junpei; Fung, Yu-Fai; Dai, Mingjun

doi:10.1007/s12555-010-0304-7

Cited by 18 publications

(3 citation statements)

References 33 publications

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“…PF provides an approximate solution for the discrete-time recursive updating of the posterior probability density function p(λ t |y 1:t ) [36]. Under PF, the posterior distribution of λ t is approximated by a collection of weighted particles λ t = {λ n t , w n t } N n=1 .…”

Section: Introductionmentioning

confidence: 99%

Fashion Sales Forecasting With a Panel Data-Based Particle-Filter Model

Ren

Choi

Liu

2015

IEEE Trans. Syst. Man Cybern, Syst.

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In this paper, we propose and explore a novel panel data-based particle-filter (PDPF) model to conduct fashion sales forecasting. We evaluate the performance of proposed model by using real data collected from the fashion industry. The experimental results indicate that the proposed panel data models outperform both the traditional statistical and intelligent methods, which provide strong evidence on the importance of employing the panel-data approach. Further analysis reveals that: 1) our proposed PDPF method yields a better forecasting result in item-based sales forecasting than in color-based sales forecasting; 2) a larger degree of Granger causality relationship between sales and price will imply a better sales forecasting result of the PDPF model; 3) increasing the amount of historical data does not necessarily improve forecasting accuracy; and 4) the PDPF method is suitable for conducting fashion sales forecasting with limited data. These findings provide novel insights on the use of panel data for conducting fashion sales forecasting.Index Terms-Fashion sales forecasting, industrial problems, panel data analysis, particle filter.

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Section: Introductionmentioning

confidence: 99%

Fashion Sales Forecasting With a Panel Data-Based Particle-Filter Model

Ren

Choi

Liu

2015

IEEE Trans. Syst. Man Cybern, Syst.

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“…In recent years, with the application of different evolutionary optimization algorithms in various scenarios [16][17][18], some researchers have studied them in combination with visual tracking problems and made significant progress. By combining an evolutionary optimization algorithm with the particle filter, the researchers [19,20] found that the evolutionary algorithm does not depend on the prior knowledge of the particle filter, and can be explored and exploited in the face of uncertainty so that the particles show arbitrariness. Based on this characteristic, it can improve the particle impoverishment problem by combining the particle filter with an evolutionary optimization algorithm.…”

Section: Improved Particle Filter Based On Sample Impoverishmentmentioning

confidence: 99%

Particle Filter Based on Harris Hawks Optimization Algorithm for Underwater Visual Tracking

Yang

Yao

Yang

2023

JMSE

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Due to the complexity of the underwater environment, tracking underwater targets via traditional particle filters is a challenging task. To resolve the problem that the tracking accuracy of a traditional particle filter is low due to the sample impoverishment caused by resampling, in this paper, a new tracking algorithm using Harris-hawks-optimized particle filters (HHOPF) is proposed. At the same time, the problem of particle filter underwater target feature construction and underwater target scale transformation is addressed, the corrected background-weighted histogram method is introduced into underwater target feature recognition, and the scale filter is combined to realize target scaling transformation during tracking. In addition, to enhance the computational speed of underwater target tracking, this paper constructs a nonlinear escape energy using the Harris hawks algorithm in order to balance the exploration and exploitation processes. Based on the proposed HHOPF tracker, we performed detection and evaluation using the Underwater Object Tracking (UOT100) vision database. The proposed method is compared with evolution-based tracking algorithms and particle filters, as well as with recent tracker-based correlation filters and some other state-of-the-art tracking methods. By comparing the results of tracking using the test data sets, it is determined that the presented algorithm improves the overlap accuracy and tracking accuracy by 11% compared with other algorithms. The experiments demonstrate that the presented HHOPF visual tracking provides better tracking results.

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“…In recent years, researchers worked extensively on this topic. Many algorithms, such as particle swarm algorithms [21][22][23][24][25][26], ant colony algorithms [27][28][29][30][31][32], genetic algorithms [33][34][35][36][37][38], and bat algorithms [39][40][41][42][43][44], have made great developments and attracted more and more attention, especially in the field of solving path planning problems in obstacle environments. UAVs perform firefighting tasks in forest fire areas, and the actual trajectory of UAVs in forest firefighting must be processed based on the appropriate trajectory generation algorithms in conjunction with the characteristics of the UAV itself and the environmental characteristics to ensure that the final trajectory matches the dynamics of the UAV [45][46][47][48].…”

Section: Introductionmentioning

confidence: 99%

A Novel Bat Algorithm with Asymmetrical Weighed Variational Method in the Path Planning of UAVs

Cao

Wang

2023

Symmetry

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In this paper, a novel bat algorithm with an asymmetrical weighed variational method (AWVM-BA) is proposed. The proposed algorithm employs the BA with a point-to-point modified asymmetrical variation above the three-dimensional flying region, which treats the space as sets of geodesics in a second order Euclidean weighed warped space. Mutation and the local selection procedure can be avoided at the same time, which solves the problem of a local optimum in concave regions. As shown in the results, the proposed algorithm does not have much impact on the calculation complexity and time in convex regions. It can greatly reduce the calculation time and avoid local optimization in concave regions. The disadvantage of the proposed algorithm is that the iteration number increases comparatively faster with the increase in the deviation of the wind speed. Therefore, it requires a higher hardware calculation ability.

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A biologically inspired improvement strategy for particle filter: Ant colony optimization assisted particle filter

Cited by 18 publications

References 33 publications

Fashion Sales Forecasting With a Panel Data-Based Particle-Filter Model

Fashion Sales Forecasting With a Panel Data-Based Particle-Filter Model

Particle Filter Based on Harris Hawks Optimization Algorithm for Underwater Visual Tracking

A Novel Bat Algorithm with Asymmetrical Weighed Variational Method in the Path Planning of UAVs

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