This paper sets up an Artificial Bee Colony (ABC) algorithm for evolving ant direction Ant Colony Optimization (ACO) to solve the Economic Power Dispatch (EPD) problem. The method ABC-ACO employs the bee colony to find a suitable and the best values operators to improve the ACO. The feasibility of the proposed approach was tested on IEEE 57-bus system. The proposed approach simulation results, ...

A hybrid population based Ant Colony Optimization (ACO) algorithm PFold-P-ACO for protein folding in the HP model is proposed in this paper. This is the first population based ACO algorithm in the bioinformatics. It is shown experimentally that the algorithms achieves on nearly all test sequences at least comparable results to other state of the art algorithms. Compared to the state of the art ...

Cloud Computing is growing rapidly and clients are demanding more services with better results. Therefore, load balancing as well as task scheduling in the Cloud has become a very interesting and important research area. In this paper, we present proposed approach for improving parameters in ant colonies function. The suggested definition of a new computational paradigm, is called Ant Colony Op...

Applications of the Ant Colony Optimization (ACO) to solve image processing problem with a reference to a new automatic enhancement technique based on real-coded particle ant colony is proposed in this paper. The enhancement process is a non-linear optimization problem with several constraints. The objective of the proposed ACO is to maximize an objective fitness criterion in order to enhance t...

-The social insect metaphor for solving diverse problems has become an emerging issue in the current years emphasizing on stochastic construction process building the solution probabilistically. Ant Colony Optimization (ACO) is an algorithm inspired by the foraging behavior of ants wherein ants deposits a volatile chemical call pheromone on the ground surface for the purpose of foraging and col...

In this paper we analyze the application of the Ant Colony Optimization to the Minimum Weight Vertex Covering Problem. We use the software system that we developed and implemented different standard ACO algorithms to this problem: Ant Colony System, the use of Elitism, Rank based approach and the MinMax system. We have made a comparative assessment of the effectiveness of these algorithms to th...

Ant Colony Optimization is more suitable for combinatorial optimization problems. ACO is successfully applied to the traveling salesman problem, and multistage decision making of ACO has an edge over other conventional methods. In this paper, the authors propose the Evolving Ant Colony Optimization (EACO) method for solving unit commitment (UC) problem. The EACO employs Genetic Algorithm (GA) f...

We describe an Ant Colony Optimization (ACO) algorithm, ANT-MPE, for the most probable explanation problem in Bayesian network inference. After tuning its parameters settings, we compare ANTMPE with four other sampling and local search-based approximate algorithms: Gibbs Sampling, Forward Sampling, Multistart Hillclimbing, and Tabu Search. Experimental results on both artificial and real networ...

This paper explains the dynamic behaviour of ant colony optimization based Hybrid Active Power Filter (HAPF) accomplished for harmonic compensation under varying source/load conditions. ACO based controller is employed to extract fundamental component of current from non-sinusoidal currents of the considered supply system. Ant colony optimization (ACO) is a novel searching technique used in opt...

Combining ant colony optimization (ACO) and multiobjective evolutionary algorithm based on decomposition (MOEA/D), this paper proposes a multiobjective evolutionary algorithm, MOEA/D-ACO. Following other MOEA/D-like algorithms, MOEA/D-ACO decomposes an multiobjective optimization problem into a number of single objective optimization problems. Each ant (i.e. agent) is responsible for solving on...