Trajectory tracking performance comparison between genetic algorithm and ant colony optimization for PID controller tuning on pressure process

2010 ◽  
Vol 20 (3) ◽  
pp. 518-528 ◽  
Author(s):  
Muhammet Ünal ◽  
Hasan Erdal ◽  
Vedat Topuz
Keyword(s):  
Genetic Algorithm ◽  
Ant Colony Optimization ◽  
Trajectory Tracking ◽  
Pid Controller ◽  
Performance Comparison ◽  
Ant Colony ◽  
Tracking Performance ◽  
Controller Tuning ◽  
Pid Controller Tuning

PID CONTROLLER TUNING USING ANT COLONY METHOD FOR SERVO HYDRAULIC SYSTEM

2019 ◽  
Vol 42 (4) ◽  
pp. 148-152
Author(s):  
Shaymaa Mahmood Mahdi ◽  
Karam Samir Khalid ◽  
Shakir Mahmood Mahdi
Keyword(s):  
Hydraulic System ◽  
Pid Controller ◽  
Ant Colony ◽  
Controller Tuning ◽  
Pid Controller Tuning

scholarly journals Ant colony optimization with semi random initialization for nurse rostering problem

2021 ◽  
Vol 12 ◽  
pp. 31
Author(s):  
Said Achmad ◽  
Antoni Wibowo ◽  
Diana Diana
Keyword(s):  
Genetic Algorithm ◽  
Ant Colony Optimization ◽  
Performance Comparison ◽  
Ant Colony ◽  
Soft Constraint ◽  
Nurse Rostering ◽  
Hard Constraint ◽  
Nurse Rostering Problem ◽  
Random Initialization ◽  
Objective Value

A nurse rostering problem is an NP-Hard problem that is difficult to solve during the complexity of the problem. Since good scheduling is the schedule that fulfilled the hard constraint and minimizes the violation of soft constraint, a lot of approaches is implemented to improve the quality of the schedule. This research proposed an improvement on ant colony optimization with semi-random initialization for nurse rostering problems. Semi-random initialization is applied to avoid violation of the hard constraint, and then the violation of soft constraint will be minimized using ant colony optimization. Semi-random initialization will improve the construction solution phase by assigning nurses directly to the shift that is related to the hard constraint, so the violation of hard constraint will be avoided from the beginning part. The scheduling process will complete by pheromone value by giving weight to the rest available shift during the ant colony optimization process. This proposed method is tested using a real-world problem taken from St. General Hospital Elisabeth. The objective function is formulated to minimize the violation of the constraints and balance nurse workload. The performance of the proposed method is examined by using different dimension problems, with the same number of ant and iteration. The proposed method is also compared to conventional ant colony optimization and genetic algorithm for performance comparison. The experiment result shows that the proposed method performs better with small to medium dimension problems. The semi-random initialization is a success to avoid violation of the hard constraint and minimize the objective value by about 24%. The proposed method gets the lowest objective value with 0,76 compared to conventional ant colony optimization with 124 and genetic algorithm with 1.

scholarly journals PID controller tuning for a multivariable glass furnace process by genetic algorithm

2016 ◽  
Vol 13 (1) ◽  
pp. 64-72 ◽  
Author(s):  
Kumaran Rajarathinam ◽  
James Barry Gomm ◽  
Ding-Li Yu ◽  
Ahmed Saad Abdelhadi
Keyword(s):  
Genetic Algorithm ◽  
Glass Furnace ◽  
Pid Controller ◽  
Controller Tuning ◽  
Furnace Process ◽  
Pid Controller Tuning
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