In this article we present an application of the Ant Colony Optimization (ACO) metaheuristic to the single machine total weighted tardiness problem. First, we briefly discuss the constructive phase of ACO in which a colony of artificial ants generates a set of feasible solutions. Then, we introduce some simple but very effective local search. Last, we combine the constructive phase with local s...

The edges of an image define the image boundary. When the image is noisy, it does not become easy to identify the edges. Therefore, a method requests to be developed that can identify edges clearly in a noisy image. Many methods have been proposed earlier using filters, transforms and wavelets with Ant colony optimization (ACO) that detect edges. We here used ACO for edge detection of noisy ima...

We present in this paper a new ant based approach named AntClass for data clustering. This algorithm uses the stochastic principles of an ant colony in conjunction with the deterministic principles of the Kmeans algorithm. It first creates an initial partition using an improved ant-based approach, which does not require any information on the input data (such as the number of classes, or an ini...

Ant colony algorithm is a new type of biomimetic evolutionary algorithm, which has some outstanding characteristics like good robustness, parallelism and positive feedback. It has been widely used in many fields but it still has some weaknesses, such as slow-convergence and easily-falling into local extreme value. Therefore we propose a new algorithm which combines the quantum evolutionary algo...

Ant Colony Optimization is a probabilistic technique for finding the optimal path for reaching a destination using graphs. This algorithm was further developed to introduce the concept of Multiple Ant Colony Optimization technique in which ants were classified into various families that worked to provide optimal path solutions to the destination for their own family. This paper aims at proposin...

This paper research review Ant colony optimization (ACO) and Genetic Algorithm (GA), both are two powerful meta-heuristics. This paper explains some major defects of these two algorithm at first then proposes a new model for ACO in which, artificial ants use a quick genetic operator and accelerate their actions in selecting next state. Experimental results show that proposed hybrid algorithm is...

This Chapter summarizes some of the well known stigmergic computational techniques inspired by nature, mainly for optimization problems developed by mimicking social insects’ behavior. Some facts about social insects namely ants, bees and termites are presented with an emphasis on how they could interact and self organize for solving real world problems. We focused on ant colony optimization al...

If you want to cite this report, please use the following reference instead: Purpose-In this paper, a novel Ant Colony Optimization (ACO) approach to optimal control is proposed. The standard ACO algorithms have proven to be very powerful optimization metaheuristic for combinatorial optimization problems. They have been demonstrated to work well when applied to various NP-complete problems, suc...

Antnet is an agent based routing algorithm that is influenced from the unsophisticated and individual ant’s emergent behaviour. Ants (software agents) are used in antnet to collect information and to update the probabilistic distance vector routing table entries. Modified antnet algorithm has been introduced, which improve the throughput and average delay. Results shows that by detecting and dr...

Most previous artificial ant foraging algorithms have to date relied to some degree on a priori knowledge of the environment, in the form of explicit gradients generated by the nest, by hard-coding the nest location in an easily-discoverable place, or by imbuing the artificial ants with the knowledge of the nest direction. In contrast, the work presented solves ant foraging problems using two p...