Performance Analysis of Artificial Bee-Colony Algorithm for Routing and Wavelength Assignment in DWDM Transport Network

Setting up lightpaths for a set of requested connection of wavelength division multiplexing (WDM) network, is by routing and assigning wavelengths to each connection. So as to minimize the use of network resources or maximize the traffic served, is called the routing and wavelength assignment (RWA) problem. A new idea based on Artificial Bee Colony (ABC) algorithm is introduced for solving RWA problem which is known to be an NP-hard problem. In the proposed ABC-RWA approach every food source represents a possible and feasible candidate lightpath between each original and destination node span in demand matrix. The situation of the food source is modified by some artificial bee in the population where the aim is to discover the places of food sources. The food source with the highest nectar value seems to be a solution which is evaluated by the fitness function. This thesis proposes solutions to solve the RWA problem using artificial bee-colony algorithm in order to achieve better performance of the network connection to serve a given demand matrix of an optical network to reach RWA global solution. The work will evaluate the path length (propagation delay) for solving RWA problem with ABC algorithm in a real-world optical networks test bench to find optimal routes for connection request in demand matrix according to objective function and some physical and operational constraints in Dense Wavelength Division Multiplexing (DWDM) optical networks. Based on simulation with several generated traffic distribution, ABC algorithm can be used to solve routing and wavelength problem at DWDM transport network as shown that in line with iteration process the path length observed toward minimum value. The number of iteration needed to reach the fitness value depends on several parameter such as number of connection request, number of wavelength and alternative path, the distribution of generated traffic and also population size

Robust fuzzy PSS design using ABC

This paper presents an Artificial Bee Colony (ABC) algorithm to tune optimal rule-base of a Fuzzy Power System Stabilizer (FPSS) which leads to damp low frequency oscillation following disturbances in power systems. Thus, extraction of an appropriate set of rules or selection of an optimal set of rules from the set of possible rules is an important and essential step toward the design of any successful fuzzy logic controller. Consequently, in this paper, an ABC based rule generation method is proposed for automated fuzzy PSS design to improve power system stability and reduce the design effort. The effectiveness of the proposed method is demonstrated on a 3-machine 9-bus standard power system in comparison with the Genetic Algorithm based tuned FPSS under different loading condition through ITAE performance indices

Block matching algorithm for motion estimation based on Artificial Bee Colony (ABC)

Block matching (BM) motion estimation plays a very important role in video coding. In a BM approach, image frames in a video sequence are divided into blocks. For each block in the current frame, the best matching block is identified inside a region of the previous frame, aiming to minimize the sum of absolute differences (SAD). Unfortunately, the SAD evaluation is computationally expensive and represents the most consuming operation in the BM process. Therefore, BM motion estimation can be approached as an optimization problem, where the goal is to find the best matching block within a search space. The simplest available BM method is the full search algorithm (FSA) which finds the most accurate motion vector through an exhaustive computation of SAD values for all elements of the search window. Recently, several fast BM algorithms have been proposed to reduce the number of SAD operations by calculating only a fixed subset of search locations at the price of poor accuracy. In this paper, a new algorithm based on Artificial Bee Colony (ABC) optimization is proposed to reduce the number of search locations in the BM process. In our algorithm, the computation of search locations is drastically reduced by considering a fitness calculation strategy which indicates when it is feasible to calculate or only estimate new search locations. Since the proposed algorithm does not consider any fixed search pattern or any other movement assumption as most of other BM approaches do, a high probability for finding the true minimum (accurate motion vector) is expected. Conducted simulations show that the proposed method achieves the best balance over other fast BM algorithms, in terms of both estimation accuracy and computational cost.Comment: 22 Pages. arXiv admin note: substantial text overlap with arXiv:1405.4721, arXiv:1406.448

The design and applications of the african buffalo algorithm for general optimization problems

Optimization, basically, is the economics of science. It is concerned with the need to maximize profit and minimize cost in terms of time and resources needed to execute a given project in any field of human endeavor. There have been several scientific investigations in the past several decades on discovering effective and efficient algorithms to providing solutions to the optimization needs of mankind leading to the development of deterministic algorithms that provide exact solutions to optimization problems. In the past five decades, however, the attention of scientists has shifted from the deterministic algorithms to the stochastic ones since the latter have proven to be more robust and efficient, even though they do not guarantee exact solutions. Some of the successfully designed stochastic algorithms include Simulated Annealing, Genetic Algorithm, Ant Colony Optimization, Particle Swarm Optimization, Bee Colony Optimization, Artificial Bee Colony Optimization, Firefly Optimization etc. A critical look at these ‘efficient’ stochastic algorithms reveals the need for improvements in the areas of effectiveness, the number of several parameters used, premature convergence, ability to search diverse landscapes and complex implementation strategies. The African Buffalo Optimization (ABO), which is inspired by the herd management, communication and successful grazing cultures of the African buffalos, is designed to attempt solutions to the observed shortcomings of the existing stochastic optimization algorithms. Through several experimental procedures, the ABO was used to successfully solve benchmark optimization problems in mono-modal and multimodal, constrained and unconstrained, separable and non-separable search landscapes with competitive outcomes. Moreover, the ABO algorithm was applied to solve over 100 out of the 118 benchmark symmetric and all the asymmetric travelling salesman’s problems available in TSPLIB95. Based on the successful experimentation with the novel algorithm, it is safe to conclude that the ABO is a worthy contribution to the scientific literature

Active power loss minimization in electric power systems through chaotic artificial bee colony algorithm

Optimizacija reaktivne snage - Reactive power optimization (RPO) - osnovno je područje istraživanja u svrhu sigurnog i ekonomičnog rada energetskih sustava. RPO se može primijeniti za smanjenje gubitaka aktivne snage, reguliranje napona te za optimizaciju energetskih koeficijenata u energetskim sustavima. Funkcija ne-linearnog gubitka snage koristi se kao funkcija cilja koju treba smanjiti. U ovom se radu algoritam Kaotične umjetne kolonije pčela - Chaotic Artificial Bee Colony (CABC) - primjenjuje za smanjenje gubitka aktivne snage u energetskim sustavima. Rabe se kaotične mape kao što su logistička mapa i Henon mapa. CABS se primjenjuje na provjeravanim sustavima IEEE 6-sabirnice i IEEE 30-sabirnice i daju se rezultati. Provjerom rezultata ustanovilo se da primjena kritičnih vrijednosti stabilnosti dobivenih pomoću CABS može rezultirati dobrim potencijalnim rješenjima. Rezultati simulacije su obećavajući i pokazuju učinkovitost primijenjenog pristupa.Reactive power optimization (RPO) is a major field of study to ensure power systems for operating in a secure and economical manner. RPO can be used for decreasing of active power losses, voltage control, and for the optimization of the power coefficients in power systems. The non-linear power loss function is used as an object function that will be minimized. In this study Chaotic Artificial Bee Colony (CABC) algorithm is used to minimize the active power loss of power systems. Chaotic maps such as logistic map and Henon map are used against the random number generator. CABC is applied on the IEEE6-bus and IEEE 30-bus test systems and the results are shown. Accordingly, the results have been evaluated and observed that the stability critical values found by CABC can be used to produce good potential solutions. Simulation results are promising and show the effectiveness of the applied approach