Showing 8 results for Heuristic
M. Abadi, S. Jalili,
Volume 2, Issue 3 (7-2006)
Abstract
Intruders often combine exploits against multiple vulnerabilities in order to
break into the system. Each attack scenario is a sequence of exploits launched by an
intruder that leads to an undesirable state such as access to a database, service disruption,
etc. The collection of possible attack scenarios in a computer network can be represented by
a directed graph, called network attack graph (NAG). The aim of minimization analysis of
network attack graphs is to find a minimum critical set of exploits that completely
disconnect the initial nodes and the goal nodes of the graph. In this paper, we present an ant
colony optimization algorithm, called AntNAG, for minimization analysis of large-scale
network attack graphs. Each ant constructs a critical set of exploits. A local search heuristic
has been used to improve the overall performance of the algorithm. The aim is to find a
minimum critical set of exploits that must be prevented to guarantee no attack scenario is
possible. We compare the performance of the AntNAG with a greedy algorithm for
minimization analysis of several large-scale network attack graphs. The results of the
experiments show that the AntNAG can be successfully used for minimization analysis of
large-scale network attack graphs.
M. Padma Lalitha, V.c Veera Reddy, N. Sivarami Reddy,
Volume 6, Issue 4 (12-2010)
Abstract
Distributed Generation (DG) is a promising solution to many power system problems such as voltage regulation, power loss, etc. This paper presents a new methodology using Fuzzy and Artificial Bee Colony algorithm(ABC) for the placement of Distributed Generators(DG) in the radial distribution systems to reduce the real power losses and to improve the voltage profile. A two-stage methodology is used for the optimal DG placement . In the first stage, Fuzzy is used to find the optimal DG locations and in the second stage, ABC algorithm is used to find the size of the DGs corresponding to maximum loss reduction. The ABC algorithm is a new population based meta heuristic approach inspired by intelligent foraging behavior of honeybee swarm. The advantage of ABC algorithm is that it does not require external parameters such as cross over rate and mutation rate as in case of genetic algorithm and differential evolution and it is hard to determine these parameters in prior. The proposed method is tested on standard IEEE 33 bus test system and the results are presented and compared with different approaches available in the literature. The proposed method has outperformed the other methods in terms of the quality of solution and computational efficiency.
M. R. Mosavi, M. Khishe, Y. Hatam Khani, M. Shabani,
Volume 13, Issue 1 (3-2017)
Abstract
Radial Basis Function Neural Networks (RBF NNs) are one of the most applicable NNs in the classification of real targets. Despite the use of recursive methods and gradient descent for training RBF NNs, classification improper accuracy, failing to local minimum and low-convergence speed are defects of this type of network. In order to overcome these defects, heuristic and meta-heuristic algorithms have been conventional to training RBF network in the recent years. This study uses Stochastic Fractal Search Algorithm (SFSA) for training RBF NNs. The particles in the new algorithm explore the search space more efficiently by using the diffusion property, which is observed regularly in arbitrary fractals. To assess the performance of the proposed classifier, this network will be evaluated with the two benchmark datasets and a high-dimensional practical dataset (i.e., sonar). Results indicate that new classifier classifies sonar dataset six percent better than the best algorithm and its convergence speed is better than the other algorithms. Also has better performance than classic benchmark algorithms about all datasets.
N. Okati, M. R. Mosavi, H. Behroozi,
Volume 13, Issue 4 (12-2017)
Abstract
Node cooperation can protect wireless networks from eavesdropping by using the physical characteristics of wireless channels rather than cryptographic methods. Allocating the proper amount of power to cooperative nodes is a challenging task. In this paper, we use three cooperative nodes, one as relay to increase throughput at the destination and two friendly jammers to degrade eavesdropper’s link. For this scenario, the secrecy rate function is a non-linear non-convex problem. So, in this case, exact optimization methods can only achieve suboptimal solution. In this paper, we applied different meta-heuristic optimization techniques, like Genetic Algorithm (GA), Partial Swarm Optimization (PSO), Bee Algorithm (BA), Tabu Search (TS), Simulated Annealing (SA) and Teaching-Learning-Based Optimization (TLBO). They are compared with each other to obtain solution for power allocation in a wiretap wireless network. Although all these techniques find suboptimal solutions, but they appear superlative to exact optimization methods. Finally, we define a Figure of Merit (FOM) as a rule of thumb to determine the best meta-heuristic algorithm. This FOM considers quality of solution, number of required iterations to converge, and CPU time.
M. Nezhadshahbodaghi, K. Bahmani, M. R. Mosavi, D. Martín,
Volume 19, Issue 2 (6-2023)
Abstract
Today, it can be said that in every field in which timely information is needed, we can use the applications of time-series prediction. In this paper, among so many chaotic systems, the Mackey-Glass and Loranz are chosen. To predict them, Multi-Layer Perceptron Neural Network (MLP NN) trained by a variety of heuristic methods are utilized such as genetic, particle swarm, ant colony, evolutionary strategy algorithms, and population-based incremental learning. Also, in addition to expressed methods, we propose two algorithms of Bio-geography-Based Optimization (BBO) and fuzzy system to predict these chaotic systems. Simulation results show that if the MLP NN is trained based on the proposed meta-heuristic algorithm of BBO, training and testing accuracy will be improved by 28.5% and 51%, respectively. Also, if the presented fuzzy system is utilized to predict the chaotic systems, it outperforms approximately by 98.5% and 91.3% in training and testing accuracy, respectively.
Nguyen Cong Chinh,
Volume 20, Issue 3 (9-2024)
Abstract
This paper presents an intelligent meta-heuristic algorithm, named improved equilibrium optimizer (IEO), for addressing the optimization problem of multi-objective simultaneous integration of distributed generators at unity and optimal power factor in a distribution system. The main objective of this research is to consider the multi-objective function for minimizing total power loss, improving voltage deviation, and reducing integrated system operating costs with strict technical constraints. An improved equilibrium optimizer is an enhanced version of the equilibrium optimizer that can provide better performance, stability, and convergence characteristics than the original algorithm. For evaluating the effectiveness of the suggested method, the IEEE 69-bus radial distribution system is chosen as a test system, and simulation results from this method are also compared fairly with many previously existing methods for the same targets and constraints. Thanks to its ability to intelligently expand the search space and avoid local traps, the suggested method has become a robust stochastic optimization method in tackling complex optimization tasks.
Trung Kien Do, Thanh Long Duong,
Volume 21, Issue 1 (3-2025)
Abstract
Frequency instability is one of the causes of severe disturbances in the power system, including load shedding and widespread blackouts. Especially in modern power systems, frequency instability has even more serious consequences due to the propagation occurring in interconnected regions. Load frequency control (LFC) is a powerful tool in power system operation to ensure that the frequency is always within the allowable limits. The control parameters of LFC must be optimally adjusted for stable system operation. However, researchers are currently unable to find a suitable and robust method for optimal tuning of LFC control parameters. The paper proposes the Puma Optimizer (PO) algorithm to optimize the parameters of PID, FOPID, and FOPTID+1 controllers for solving the LFC problem. The proposed PO algorithm is evaluated through two models of single-area and two-area power systems with different power sources, including thermal power, hydropower, and gas power. The simulation results show that the integral time absolute error (ITAE) value of the proposed PO method is smaller by 5.25%, 18.16%, 28.35%, and 59.92% compared to Particle Swarm Optimization (PSO), Crested Porcupine Optimization (CPO), Newton-Raphson-based optimization (NRBO), and Global Neighborhood Algorithm (GNA), respectively. The results obtained demonstrate that the PO algorithm is a reliable and efficient tool for finding solutions to the LFC problem.
Humairah Mansor, Shazmin Aniza Abdul Shukor, Razak Wong Chen Keng, Nurul Syahirah Khalid,
Volume 21, Issue 2 (6-2025)
Abstract
Building fixtures like lighting are very important to be modelled, especially when a higher level of modelling details is required for planning indoor renovation. LIDAR is often used to capture these details due to its capability to produce dense information. However, this led to the high amount of data that needs to be processed and requires a specific method, especially to detect lighting fixtures. This work proposed a method named Size Density-Based Spatial Clustering of Applications with Noise (SDBSCAN) to detect the lighting fixtures by calculating the size of the clusters and classifying them by extracting the clusters that belong to lighting fixtures. It works based on Density-Based Spatial Clustering of Applications with Noise (DBSCAN), where geometrical features like size are incorporated to detect and classify these lighting fixtures. The final results of the detected lighting fixtures to the raw point cloud data are validated by using F1-score and IoU to determine the accuracy of the predicted object classification and the positions of the detected fixtures. The results show that the proposed method has successfully detected the lighting fixtures with scores of over 0.9. It is expected that the developed algorithm can be used to detect and classify fixtures from any 3D point cloud data representing buildings.
