Particle Swarm Optimization with Chaotic Maps and Gaussian Mutation for Function Optimization

Tian, Decheng

doi:10.14257/ijgdc.2015.8.4.12

Cited by 10 publications

(5 citation statements)

References 20 publications

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“…They concluded that chaotic inertia weight improves the accuracy of the solution. Modifications on the swarms itself are: insertions new swarms [26,27], mutation [28], and swarm initiation [29]. These modifications can increase the search diversity.…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

Creating Through Points in Linear Function with Parabolic Blends Path by Optimization Method

Saeed Al-khayyt

2018

alkej

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The linear segment with parabolic blend (LSPB) trajectory deviates from the specified waypoints. It is restricted to that the acceleration must be sufficiently high. In this work, it is proposed to engage modified LSPB trajectory with particle swarm optimization (PSO) so as to create through points on the trajectory. The assumption of normal LSPB method that parabolic part is centered in time around waypoints is replaced by proposed coefficients for calculating the time duration of the linear part. These coefficients are functions of velocities between through points. The velocities are obtained by PSO so as to force the LSPB trajectory passing exactly through the specified path points. Also, relations for velocity correction and exact velocity solution are derived. Simulation results show that the engagement of modified LSPB trajectory with PSO to work well on the tested cases. This proposed method is very simple which can be used for on-line path planning, and not necessarily to use high acceleration magnitude.

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Section: Particle Swarm Optimizationmentioning

confidence: 99%

Creating Through Points in Linear Function with Parabolic Blends Path by Optimization Method

Saeed Al-khayyt

2018

alkej

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“…As an overall outcome of the experiments carried out, chaotic inertia weight is the best strategy for better accuracy. Another modifications are presented which concern with the swarms itself such as swarm initiation, insertions new swarms, and mutation [23][24][25][26]. These modifications can increase the search diversity.…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

Visual Tracking Enhancement of Object on Circular Path based on Tuned Kalman Filter by Particle Swarm Optimization

Saeed¹,

Abdilatef²,

Yosif³

2016

IJCA

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Digital image presents information in two-dimensional data, which can be used as feedback measurement for robot visual servoing control. Median filter and morphological operation are used for object detection and extraction its features. Kalman filter is applied for visual measurements that contain noises and uncertainties captured by video camera over the time. Sinusoidal Kalman filter and sinusoidal measurement model is used. The derivations of noise's process and matrices' control are presented. The Kalman filter is tuned by using PSO optimization to produce values closer to the true spatial measurements of the target. A developed PSO is proposed in which adaptive inertia weight chaotic PSO algorithm and velocity constriction factor are used for the porpuse of getting rid from the local and the adjacent optimum convergence. Simulation for tracking object on circular path are presented. Experimental result shows good performance of the proposed method for noisy measurement of the target.

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“…The chaotic ant swarm is used in this research to analyze the dynamic properties of a distributed system in a networked multi agent system at the micro level for allocation. 35 Swarm optimization with various levy flight maps 2015…”

Section: Introductionmentioning

confidence: 99%

A levy flight based strategy to improve the exploitation capability of arithmetic optimization algorithm for engineering global optimization problems

Dhawale

Kamboj

Bath

2023

Trans Emerging Tel Tech

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The existing arithmetic optimization algorithm is a meta-heuristics algorithm that utilizes distribution behaviors for the different parameters in mathematics. The different mathematical operator like division, subtraction, addition, and multiplication holds the inherent capability to explore global maxima and minima. In the proposed research, levy flight-based improved arithmetic optimization algorithm has been proposed for better optimal solutions to various engineering design problems. The fundamental arithmetic optimization algorithm's local search is slow and has a slow convergence rate due to its weak exploitation capacity. In the proposed work, the exploration and exploitation phase of the existing arithmetic optimization algorithm has been enhanced using the levy flight mechanism. In order to validate the effectiveness of the proposed optimizer, the improved algorithm has been tested for 23 standard benchmark problems and 10 real-life engineering design problems. The proposed algorithm has been compared with other classical algorithms like biogeography based optimization algorithm, arithmetic optimization algorithm, moth-flame optimization algorithm, genetic algorithm, flower pollination algorithm, particle swarm optimization, gray wolf optimization algorithm, BAT algorithm, chi-square algorithm, firefly algorithm, gravitational search algorithm, and differential evolution algorithm. The obtained result reveals that the proposed hybrid levy flight arithmetic optimization algorithm performs best on the number of test functions including engineering design problems with excellent fitness value and excellent convergence. This article is helpful to improve the exploitation capability of arithmetic optimization algorithms for engineering global optimization problems.

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Particle Swarm Optimization with Chaotic Maps and Gaussian Mutation for Function Optimization

Cited by 10 publications

References 20 publications

Creating Through Points in Linear Function with Parabolic Blends Path by Optimization Method

Creating Through Points in Linear Function with Parabolic Blends Path by Optimization Method

Visual Tracking Enhancement of Object on Circular Path based on Tuned Kalman Filter by Particle Swarm Optimization

A levy flight based strategy to improve the exploitation capability of arithmetic optimization algorithm for engineering global optimization problems

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