Stefka Fidanova

Simulated Annealing for Grid Scheduling Problem

Grid computing is a form of distributed computing that involves coordinating and sharing computing, application, data storage or network resources across dynamic and geographically dispersed organizations. The goal of grid tasks scheduling is to achieve high system throughput and to match the application need with the available computing resources. This is matching of resources in a non-deterministically shared heterogeneous environment. The complexity of scheduling problem increases with the size of the grid and becomes highly difficult to solve effectively. To obtain good methods to solve this problem a new area of research is implemented. This area is based on developed heuristic techniques that provide an optimal or near optimal solution for large grids. In this paper we introduce a tasks scheduling algorithm for grid computing. The algorithm is based on simulated annealing method. The paper shows how to search for the best tasks scheduling for grid computing

Ant Algorithm for Optimal Sensor Deployment

Telecommunications is a general term for a vast array of technologies that send information over distances. Mobile phones, land lines, satellite phones and voice over Internet protocol are all telephony technologies - just one field of telecommunications. Radio, television and networks are a few more examples of telecommunication. Nowadays, the trend in telecommunication networks is having highly decentralized, multi-node networks. From small, geographically close, size-limited local area networks the evolution has led to the huge worldwide Internet. In this context Wireless Sensor Networks (WSN) have recently become a hot topic in research. When deploying a WSN, the positioning of the sensor nodes becomes one of the major concerns. One of the objectives is to achieve full coverage of the terrain (sensor field). Another objectives are also to use a minimum number of sensor nodes and to keep the connectivity of the network. In this paper we address a WSN deployment problem in which full coverage and connectivity are treated as constraints, while objective function is the number of the sensors. To solve it we propose Ant Colony Optimization (ACO) algorithm.

InterCriteria Analysis of ACO and GA Hybrid Algorithms

In this paper, the recently proposed approach for multicriteria decision making—InterCriteria Analysis (ICA)—is presented. The approach is based on the apparatus of the index matrices and the intuitionistic fuzzy sets. The idea of InterCriteria Analysis is applied to establish the relations and dependencies of considered parameters based on different criteria referred to various metaheuristic algorithms. A hybrid scheme using Genetic Algorithm (GA) and Ant Colony Optimization (ACO) is used for parameter identification of E. coli MC4110 fed-batch cultivation process model. In the hybrid GA-ACO, the GA is used to find feasible solutions to the considered optimization problem. Further ACO exploits the information gathered by GA. This process obtains a solution, which is at least as good as—but usually better than—the best solution devised by GA. Moreover, a comparison with both the conventional GA and ACO identification results is presented. Based on ICA the obtained results are examined and conclusions about existing relations and dependencies between model parameters of the E. coli process and algorithms parameters and outcomes, such as number of individuals, number of generations, value of the objective function and computational time, are discussed.

Ant colony system approach for protein folding

The protein folding problem is a fundamental problem in computational molecular biology and biochemical physics. The high resolution 3D structure of a protein is the key to the understanding and manipulating of its biochemical and cellular functions. All information necessary to fold a protein to its native structure is contained in its amino-acid sequence. Even under simplified models, the problem is NP-hard and the standard computational approach are not powerful enough to search for the correct structure in the huge conformation space. Due to the complexity of the protein folding problem simplified models such as hydrophobic-polar (HP) model have become one of the major tools for studying protein structure. Various optimization methods have been applied on folding problem including Monte Carlo methods, evolutionary algorithm, ant colony optimization algorithm. In this work we develop an ant algorithm for 3D HP protein folding problem. It is based on very simple design choices in particular with respect to the solution components reinforced in the pheromone matrix. The achieved results are compared favorably with specialized state-of-the-art methods for this problem. Our empirical results indicate that our rather simple ant algorithm outperforms the existing results for standard benchmark instances from the literature. Furthermore, we compare our folding results with proteins with known folding.

Ant colony optimization for multiple knapsack problem and model bias

The Ant Colony Optimization (ACO) algorithms are being applied successfully to a wide range of problems. ACO algorithms could be good alternatives to existing algorithms for hard combinatorial optimization problems (COPs). In this paper we investigate the influence of model bias in model-based search as ACO. We present the effect of two different pheromone models for ACO algorithm to tackle the Multiple Knapsack Problem (MKP). The MKP is a subset problem and can be seen as a general model for any kind of binary problems with positive coefficients. The results show the importance of the pheromone model to quality of the solutions.

ACO Algorithm for MKP Using Various Heuristic Information

The ant colony optimization (ACO) algorithms are being applied successfully to diverse heavily constrained problems: traveling salesman problem, quadratic assignment problem. Early applications of ACO algorithms have been mainly concerned with solving ordering problems. In this paper, the principles of the ACO algorithm are applied to the multiple knapsack problem (MKP). In the first part of the paper we explain the basic principles of ACO algorithm. In the second part of the paper we propose different types of heuristic information and we compare the obtained results.

Adjoining independent computations

The reverse or adjoint mode of automatic differentiation is software engineering technique that permits efficient computation of gradients. However, this technique requires a lot of temporary memory. In this chapter, we present a refinement that reduces memory consumption in the case of parallel loops, and we give a proof of its correctness based on properties of the data-dependence graph of adjoint programs and parallel loops. This technique is particularly suitable for assembly loops that dominate in mesh-based computations. Application is done on the kernel of a realistic Navier-Stokes solver.

InterCriteria Analysis of a model parameters identification using genetic algorithm

In this paper we apply an approach based on the apparatus of the Index Matrices and the Intuitionistic Fuzzy Sets - namely InterCriteria Analysis. The main idea is to use the InterCriteria Analysis to establish the existing relations and dependencies of defined parameters in non-linear model of an E. coli fed-batch cultivation process. Moreover, based on results of series of identification procedures we observe the mutual relations between model parameters and considered optimization techniques outcomes, such as execution time and objective function value. Based on InterCriteria Analysis we examine the obtained identification results and discuss the conclusions about existing relations and dependencies between defined, in terms of InterCriteria Analysis, criteria.

ACO Algorithm with Additional Reinforcement

The aim of the paper is to develop the functionality of the ant colony optimization (ACO) algorithms by adding some diversification such as additional reinforcement of the pheromone. This diversification guides the search to areas in the search space which have not been yet explored and forces the ants to search for better solutions. In the ACO algorithms [1],[2] after the initialization, a main loop is repeated until a termination condition is met. In the beginning ants construct feasible solutions, then the pheromone trails are updated. Partial solutions are seen as states: each ant moves from a state i to another state j corresponding to a more complete partial solution.

Hybrid ACO algorithm for the GPS surveying problem

Ant Colony Optimization(ACO) has been used successfully to solve hard combinatorial optimization problems This metaheuristic method is inspired by the foraging behavior of ants, which manage to establish the shortest routes from their nest to feeding sources and back In this paper, we propose hybrid ACO approach to solve the Global Positioning System (GPS) surveying problem In designing GPS surveying network, a given set of earth points must be observed consecutively (schedule) The cost of the schedule is the sum of the time needed to go from one point to another The problem is to search for the best order in which this observation is executed Minimizing the cost of this schedule is the goal of this work Our results outperform those achieved by the best-so-far algorithms in the literature, and represent a new state of the art in this problem.