In this paper, we implement Ant Colony Optimization (ACO) for sequence alignment. ACO is a meta-heuristic recently developed for nearest neighbor approximations in large, NP-hard search spaces. Here we use a genetic algorithm approach to evolve the best parameters for an ACO designed to align two sequences. We then used the best parameters found to interpolate approximate optimal parameters for...

Problem statement: Efficient scheduling of the tasks to heterogeneous processors for any application is critical in order to achieve high performance. Finding a feasible schedule for a given task set to a set of heterogeneous processors without exceeding the capacity of the processors, in general, is NP-Hard. Even if there are many conventional approaches available, people have been looking at ...

Abstract—Ant Colony Optimization (ACO) is a promising modern approach to the unused combinatorial optimization. Here ACO is applied to finding the shortest during communication link failure. In this paper, the performances of the prim’s and ACO algorithm are made. By comparing the time complexity and program execution time as set of parameters, we demonstrate the pleasant performance of ACO in ...

Ant Colony Optimization (ACO) is a promising new approach to combinatorial optimization. Here ACO is applied to the traveling salesman problem (TSP). Using a genetic algorithm (GA) to nd the best set of parameters, we demonstrate the good performance of ACO in nding good solutions

In this paper is introduce "flying" ants in Ant Colony Optimization (ACO). In traditional ACO algorithms the ants construct their solution regarding one step forward. In proposed ACO algorithm, the ants make their decision, regarding more than one step forward, but they include only one new element in their solutions.

This article proposes a novel MPPT algorithm based on the firefly and elite ant system-trained Elman neural network (FA-EAS-ElmanNN). First, position of fireflies is randomly initialized by (FA), meanwhile individuals with higher attractiveness degree value are selected as optimal solution. Second, extra pheromones artificially released to boost positive feedback effect convergence rate system ...

This paper uses a modified Ant Colony Optimization (ACO) algorithm to price simple financial derivatives. We use ants to find the optimum time to exercise an option. Our algorithm searches the solution space to find optimum solution under some user defined constraints. In this preliminary study we show that the modified ACO works in predicting an optimum exercise time of a simple vanilla option.