Abstract: The major problem encountered when modeling complex systems with agent-based modeling and simulation techniques is the existence of large parameter spaces. A complex system model cannot be expected to reflect the whole of the real system, but by specifying the most appropriate parameters, the actual system can be represented by the model under certain conditions. When the studies conducted in recent years were reviewed, it has been observed that there are few studies for parameter tuning problem in agent based simulations, and these studies have focused on tuning parameters of a single model. In this study, an approach of parameter tuning is proposed by using metaheuristic algorithms such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Artificial Bee Colonies (ABC), Firefly (FA) algorithms. With this hybrid structured study, the parameter tuning problems of the models in the different fields were solved. The new approach offered was tested in two different models, and its achievements in different problems were compared. The simulations and the results reveal that this proposed study is better than the existing parameter tuning studies.
Abstract: Today’s modern interconnected power system is
highly complex in nature. In this, one of the most important
requirements during the operation of the electric power system is the
reliability and security. Power and frequency oscillation damping
mechanism improve the reliability. Because of power system
stabilizer (PSS) low speed response against of major fault such as
three phase short circuit, FACTs devise that can control the network
condition in very fast time, are becoming popular. But FACTs
capability can be seen in a major fault present when nonlinear models
of FACTs devise and power system equipment are applied. To realize
this aim, the model of multi-machine power system with FACTs
controller is developed in MATLAB/SIMULINK using Sim Power
System (SPS) blockiest. Among the FACTs device, Static
synchronous series compensator (SSSC) due to high speed changes
its reactance characteristic inductive to capacitive, is effective power
flow controller. Tuning process of controller parameter can be
performed using different method. But Genetic Algorithm (GA)
ability tends to use it in controller parameter tuning process. In this
paper firstly POD controller is used to power oscillation damping.
But in this station, frequency oscillation dos not has proper damping
situation. So FOD controller that is tuned using GA is using that
cause to damp out frequency oscillation properly and power
oscillation damping has suitable situation.
Abstract: Robot manipulators are highly coupled nonlinear
systems, therefore real system and mathematical model of dynamics
used for control system design are not same. Hence, fine-tuning of
controller is always needed. For better tuning fast simulation speed
is desired. Since, Matlab incorporates LAPACK to increase the speed
and complexity of matrix computation, dynamics, forward and
inverse kinematics of PUMA 560 is modeled on Matlab/Simulink in
such a way that all operations are matrix based which give very less
simulation time. This paper compares PID parameter tuning using
Genetic Algorithm, Simulated Annealing, Generalized Pattern Search
(GPS) and Hybrid Search techniques. Controller performances for all
these methods are compared in terms of joint space ITSE and
cartesian space ISE for tracking circular and butterfly trajectories.
Disturbance signal is added to check robustness of controller. GAGPS
hybrid search technique is showing best results for tuning PID
controller parameters in terms of ITSE and robustness.
Abstract: The one-class support vector machine “support vector
data description” (SVDD) is an ideal approach for anomaly or outlier
detection. However, for the applicability of SVDD in real-world
applications, the ease of use is crucial. The results of SVDD are
massively determined by the choice of the regularisation parameter C
and the kernel parameter of the widely used RBF kernel. While for
two-class SVMs the parameters can be tuned using cross-validation
based on the confusion matrix, for a one-class SVM this is not
possible, because only true positives and false negatives can occur
during training. This paper proposes an approach to find the optimal
set of parameters for SVDD solely based on a training set from
one class and without any user parameterisation. Results on artificial
and real data sets are presented, underpinning the usefulness of the
approach.
Abstract: An additive fuzzy system comprising m rules with
n inputs and p outputs in each rule has at least t m(2n + 2 p + 1)
parameters needing to be tuned. The system consists of a large
number of if-then fuzzy rules and takes a long time to tune its
parameters especially in the case of a large amount of training data
samples. In this paper, a new learning strategy is investigated to cope
with this obstacle. Parameters that tend toward constant values at the
learning process are initially fixed and they are not tuned till the end
of the learning time. Experiments based on applications of the
additive fuzzy system in function approximation demonstrate that the
proposed approach reduces the learning time and hence improves
convergence speed considerably.
Abstract: In this paper, an improved ant colony optimization
(ACO) algorithm is proposed to enhance the performance of global
optimum search. The strategy of the proposed algorithm has the
capability of fuzzy pheromone updating, adaptive parameter tuning,
and mechanism resetting. The proposed method is utilized to tune the
parameters of the fuzzy controller for a real beam and ball system.
Simulation and experimental results indicate that better performance
can be achieved compared to the conventional ACO algorithms in the
aspect of convergence speed and accuracy.
Abstract: This paper proposes a scheduling scheme using feedback
control to reduce the response time of aperiodic tasks with soft
real-time constraints. We design an algorithm based on the proposed
scheduling scheme and Total Bandwidth Server (TBS) that is a
conventional server technique for scheduling aperiodic tasks. We then
describe the feedback controller of the algorithm and give the control
parameter tuning methods. The simulation study demonstrates that the
algorithm can reduce the mean response time up to 26% compared
to TBS in exchange for slight deadline misses.