Huynh Thi Thanh Binh

Improved Cuckoo Search and Chaotic Flower Pollination optimization algorithm for maximizing area coverage in Wireless Sensor Networks

The popularity of Wireless Sensor Networks (WSNs) is rapidly growing due to its wide-ranged applications such as industrial diagnostics, environment monitoring or surveillance. High-quality construction of WSNs is increasingly demanding due to the ubiquity of WSNs. The current work is focused on improving one of the most crucial criteria that appear to exert an enormous impact on the WSNs performance, namely the area coverage. The proposed model is involved with sensor nodes deployment which maximizes the area coverage. This problem is proved to be NP-hard. Although such algorithms to handle this problem with fairly acceptable solutions had been introduced, most of them still heavily suffer from several issues including the large computation time and solution instability. Hence, the existing work proposed ways to overcome such difficulties by proposing two nature-based algorithms, namely Improved Cuckoo Search (ICS) and Chaotic Flower Pollination algorithm (CFPA). By adopting the concept of calculating the adaptability and a well-designed local search in previous studies, those two algorithms are able to improve their performance. The experimental results on 15 instances established a huge enhancement in terms of computation time, solution quality and stability.

A new hybrid Genetic Algorithm for solving the Bounded Diameter Minimum Spanning Tree problem

In this paper, a new hybrid genetic algorithm - known as HGA - is proposed for solving the bounded diameter minimum spanning tree (BDMST) problem. We experiment with HGA on two sets of benchmark problem instances, both Euclidean and Non-Euclidean. On the Euclidean problem instances, HGA is shown to outperform the previous best two genetic algorithms (GAs) reported in the BDMST literature, while on the Non-Euclidean problem instance, HGA performs comparably with these two GAs.

New Particle Swarm Optimization Algorithm for Solving Degree Constrained Minimum Spanning Tree Problem

Given a connected, weighted, undirected graph G=(V, E) and a bound d . The Degree-Constrained Minimum Spanning Tree problem (DCMST or d -MST) seeks the spanning tree with smallest weight in which no vertex have degree more than d . This problem is NP-hard with d ***2. This paper proposes a new Particle Swarm Optimization algorithm for solving the d -MST problem. The proposed algorithm uses some new methods for selecting vector of particles. Results of computational experiments are reported to show the efficiency of the algorithm.

An Efficient Framework for Pixel-wise Building Segmentation from Aerial Images

Detection of buildings in aerial images is an important and challenging task in computer vision and aerial image interpretation. This paper presents an efficient approach that combines Random forest (RF) and a fully connected conditional random field (CRF) on various features for the detection and segmentation of buildings at pixel level. RF allows one to learn extremely fast on big aerial image data. The unary potentials given by RF are then combined in a fully connected conditional random field model for pixel-wise classification. The use of high dimensional Gaussian filter for pairwise potentials makes the inference tractable while obtaining high classification accuracy. Experiments have been conducted on a challenging aerial image dataset from a recent ISPRS Semantic Labeling Contest [9]. We obtained state-of-the-art accuracy with a reasonable computation time.

Crawl Topical Vietnamese Web Pages Using Genetic Algorithm

A focused crawler traverses the web selecting out relevant pages according to a predefined topic. While browsing the internet it is difficult to identify relevant pages and predict which links lead to high quality pages. In this paper, we propose a crawler system using genetic algorithm to improve its crawling performance. Apart from estimating the best path to follow, our system also expands its initial keywords by using genetic algorithm during the crawling process. To crawl Vietnamese web pages, we apply a hybrid word segmentation approach which consists of combining automata and part of speech tagging techniques for the Vietnamese text classifier. We experiment our algorithm on Vietnamese websites. Experimental results are reported to show the efficiency of our system.

New Multi-parent Recombination in Genetic Algorithm for Solving Bounded Diameter Minimum Spanning Tree Problem

Given a connected, weighted, undirected graph G=(V, E) and a bound D, Bounded Diameter Minimum Spanning Tree problem (BDMST) seeks spanning tree on G with smallest weight in which no path between two vertices contains more than D edges. This problem is NP-hard for 4 ≤ D

All capacities modular cost survivable network design problem using genetic algorithm with completely connection encoding

We study the survivable network design problem (SNDP) for simultaneous unicast and anycast flows in networks where the link cost follows All Capacities Modular Cost (ACMC) model. Given a network modeled by a connected, undirected graph and a set of flow demands, this problem aims at finding a set of connections with a minimized network cost in order to protect the network against any single failure. This paper proposes a new Genetic Algorithm with an efficient encoding to solve the SNDP in networks with ACMC model (A-SNDP). Our encoding scheme is simple and allows large search space. Extensive simulation results on real large topology instances show that the proposed algorithm is much more efficient than the Tabu Search and other conventional Genetic Algorithm in terms of minimizing the network cost.

Improving Image Segmentation Using Genetic Algorithm

This paper presents a new approach to the problem of semantic segmentation of digital images. We aim to improve the performance of some state-of-the-art approaches for the task. We exploit a new version of texton feature [28], which can encode image texture and object layout for learning a robust classifier. We propose to use a genetic algorithm for the learning parameters of weak classifiers in a boosting learning set up. We conducted extensive experiments on benchmark image datasets and compared the segmentation results with current proposed systems. The experimental results show that the performance of our system is comparable to, or even outperforms, those state-of-the-art algorithms. This is a promising approach as in this empirical study we used only texture-layout filter responses as feature and a basic setting of genetic algorithm. The framework is simple and can be extended and improved for many learning problems.

New Mechanism of Combination Crossover Operators in Genetic Algorithm for Solving the Traveling Salesman Problem

Traveling salesman problem (TSP) is a well-known in computing field. There are many researches to improve the genetic algorithm for solving TSP. In this paper, we propose two new crossover operators and new mechanism of combination crossover operators in genetic algorithm for solving TSP. We experimented on TSP instances from TSP-Lib and compared the results of proposed algorithm with genetic algorithm(GA), which used MSCX. Experimental results show that, our proposed algorithm is better than the GA using MSCX on the min, mean cost values.

Genetic Algorithm for Solving Survivable Network Design with Simultaneous Unicast and Anycast Flows

We consider the survivable network design problem for simultaneous unicast and anycast flow requests. In this problem, a network is modeled by a connected, weighted and undirected graph with link cost follows All Capacities Modular Cost (ACMC) model. Given a set of flow demand, this problem aims at finding a set of connection with minimized network cost to protect the network against any single failure. This problem is proved to be NP-hard. In this paper, we propose a new Genetic Algorithm for solving the ACMC Survivable Network Design Problem (A-SNDP). Extensive simulation results on Polska, Germany and Atlanta network instances show that the proposed algorithm is much more efficient than the Tabu Search and other baseline algorithms such as FBB1 and FBB2 in terms of minimizing the network cost.