Optimizing Laying Hen Diet using Multi-Swarm Particle Swarm Optimization

Alfarisy, Gusti Ahmad Fanshuri; Mahmudy, Wayan Firdaus; Natsir, Muhammad Halim

doi:10.12928/telkomnika.v16i4.7765

Cited by 17 publications

(14 citation statements)

References 21 publications

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“…In this paper, in addition to the modification of the SMC input control, it also carried out the optimization of the four parameters of each DoF with particle swarm optimization (PSO). PSO has a robust ability for nonlinearity problems [26] with velocity values and positions of each parameter. And the renewal function on each parameter, i.e.…”

Section: Methodsmentioning

confidence: 99%

Velocity control of ROV using modified integral SMC with optimization tuning based on Lyapunov analysis

et al. 2020

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Remotely Operated Vehicle also known as ROV is a vehicle with high nonlinearity and uncertainty parameters that requires a robust control system to maintain stability. The nonlinearity and uncertainty of ROV are caused by underwater environmental conditions and by the movement of the vehicle. SMC is one of the control systems that can overcome nonlinearity and uncertainty with the given robust system. This work aims to control velocity of the vehicle with proposes the use of modified integral SMC compensate error in ROV and the use of particle swarm optimization (PSO) to optimize the adjustment of SMC parameters. The ROV used in this paper has a configuration of six thrusters with five DoF movements that can be controlled. Modified integral sliding mode is used to control all force direction to increase the convergence of speed error. Adjustment optimization techniques with PSO are used to determine four values of sliding control parameters for five DoF. Using Lyapunov stability approach control law of sliding mode is derived and its global stability proved mathematically. Simulation results are conducted to evaluate the effectiveness of Modified Integral SMC and compared with nonlinear control.

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Section: Methodsmentioning

confidence: 99%

Velocity control of ROV using modified integral SMC with optimization tuning based on Lyapunov analysis

et al. 2020

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“…PSO is one of the smart optimization algorithms. It's belongs to Metaheuristics which is one of the classes of optimization algorithms [19,20]. It is based on praline swarm intelligent and is inspired by the animals' behaviors as birds or fish.…”

Section: Particle Swarm Optimization (Pso)mentioning

confidence: 99%

Medical remotely caring with COVID-19 virus infected people using optimized wireless arm tracing system

2020

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A human arm makes many functions that a robotic arm always programmed to make same functions. The human limbs motion can be captured using sensors that they will always copy hand movement. The rapid spread of the Coronavirus (COVID-19) virus and contacting the infections make the number of patients doubled in short time. The system proposed in this research can protect clinicians against infection with virus by reducing the contact with the infected and treat them remotely. This system type can be useful in different other fields of industrial works and defense where dangerous and delicate task can be done remotely without actual touch. Xbee shield is used to allow a hand glove flex sensor to communicate with the robotic arm using Zigbee wirelessly. Zigbee here is based on Xbee module from Max stream that can be communicate outdoor for 300 feet with the line of sight and indoor for 100 feet. Proportional, integral and derivative (PID) controller used in the proposed system to achieve smooth movement of limbs. The desired signal comes from flex sensor that connected to each limb. Kalman estimator proposed to find current state of each limb. In order to get better performance, particle swarm optimization (PSO) is used.

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“…This algorithm was first introduced by Kennedy and Eberhart in 1995 [14]. The Particle Swarm Optimization algorithm will model the best solution activity in the search space, the position of particles in the solution space is the optimization variables used as optimization candidates [15]. Each of these positions will be associated with objective values or referred to as fitness values .…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

Extreme Learning Machine Weight Optimization using Particle Swarm Optimization to Identify Sugar Cane Disease

Alauddin

Mahmudy

Abadi

2019

JITeCS

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Sugar cane disease is a major factor in reducing sugar cane yields. The low intensity of experts to go into the field to check the condition of sugar cane causes the handling of sugarcane disease tends to be slow. This problem can be solved by instilling expert intelligence on sugar cane into an expert system. In this study the method of classification of sugar cane disease was proposed using Extreme Learning Machine (ELM). However, ELM alone is not enough to classify multilabel and multiclass disease case data in this study. Therefore, it is proposed to optimize the weight of hidden neurons in ELM using Particle Swarm Optimization (PSO). The experimental results show that the classification using ELM alone can reach an accuracy rate of 71%. After the weight of hidden neurons from ELM was optimized, the accuracy rate became 79.92% or an increase of 8.92%.

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Optimizing Laying Hen Diet using Multi-Swarm Particle Swarm Optimization

Cited by 17 publications

References 21 publications

Velocity control of ROV using modified integral SMC with optimization tuning based on Lyapunov analysis

Velocity control of ROV using modified integral SMC with optimization tuning based on Lyapunov analysis

Medical remotely caring with COVID-19 virus infected people using optimized wireless arm tracing system

Extreme Learning Machine Weight Optimization using Particle Swarm Optimization to Identify Sugar Cane Disease

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