For the shortcoming that the PI controller parameters can’t been dynamic tuning in constant voltage control system of doubly-fed wind turbines, a PI controller parameters dynamic tuning strategy based on the ant colony optimization (ACO) algorithm is presented. This strategy makes the two parameters in PI controller as the ant of the ant colony, the controlled absolute error integral function t...

This paper describes a new Ant Colony Optimization (ACO) algorithm for solving Graph Matching Problems, the goal of which is to find the best matching between vertices of multi-labeled graphs. This new ACO algorithm is experimentally compared with greedy and reactive tabu approaches on subgraph isomorphism problems and on multivalent graph matching problems.

We propose a new algorithm based on the Ant Colony Optimization (ACO) meta-heuristic for the Multidimensional Knapsack Problem, the goal of which is to find a subset of objects that maximizes a given objective function while satisfying some resource constraints. We show that our new algorithm obtains better results than two other ACO algorithms on most instances.

distribution centers (dcs) play important role in maintaining the uninterrupted flow of goods and materials between the manufacturers and their customers.this paper proposes a mathematical model as the bi-objective capacitated multi-vehicle allocation of customers to distribution centers. an evolutionary algorithm named non-dominated sorting ant colony optimization (nsaco) is used as the optimi...

Genetic Algorithms (GAs) were introduced by Holland as a computational analogy of adaptive systems. GAs are search procedures based on the mechanics of natural selection and natural genetics. Ant Colony Optimization (ACO) is a metaheuristic inspired by the foraging behavior of ant colonies. ACO was introduced by Dorigo and has evolved significantly in the last few years. Both algorithms have sh...

We present a hardware implementation of population-based ant colony optimization (P-ACO) on field-programmable gate arrays (FPGAs). The ant colony optimization meta-heuristic is adopted from the natural foraging behavior of real ants and has been used to find good solutions to a wide spectrum of combinatorial optimization problems. We describe the P-ACO algorithm and present a circuit architect...

Ant Colony Optimization (ACO) algorithm has evolved as the most popular way to attack the combinatorial problems. The ACO algorithm employs multi agents called ants that are capable of finding optimal solution for a given problem instances. These ants at each step of the computation make probabilistic choices to include good solution component in partially 1 / 4

Ant colony optimization (ACO) algorithms have proved to be able to adapt to dynamic optimization problems (DOPs) when stagnation behaviour is addressed. Usually, permutation-encoded DOPs, e.g., dynamic travelling salesman problems, are addressed using ACO algorithms whereas binary-encoded DOPs, e.g., dynamic knapsack problems, are tackled by evolutionary algorithms (EAs). This is because of the...

Ant Colony Optimization (ACO) is a meta-heuristic iterative algorithm used to solve different combinatorial optimization problems. In this method, a number of artificial ants build solutions for an optimization problem and exchange information on their quality through a communication scheme that is similar to the one adopted by real ants. In this paper, Ant Colony Optimization is used to solve ...

Ant Colony Optimization (ACO) a nature-inspired metaheuristic algorithm has been successfully applied in the traveling salesman problem (TSP) and a variety of combinatorial problems. In fact, ACO can effectively fit to discrete optimization problems and exploit pre-knowledge of the problems for a faster convergence. We present an improved version of ACO with a kind of Genetic semi-random-restar...