Ant colony optimization (ACO) algorithms are computational problem-solving methods that are inspired by the complex behaviors of ant colonies; specifically, the ways in which ants interact with each other and their environment to optimize the overall performance of the ant colony. Our eventual goal is to develop and experiment with ACO methods that can more effectively adapt to dynamically chan...

In this study, we propose an optimization method by the cooperative mechanism of ant and aphid as a new Ant Colony Optimization (ACO). This algorithm is named Ant Colony Optimization with Cooperative Aphid (ACOCA). In ACOCA algorithm, the aphid searches neighborhood solutions. This solution information is treated as a honey obtained from the aphid and the honey affects the search of ACO. Moreov...

Test case prioritization is a difficult problem of Software Engineering, since several factors may be considered in order to find the best order for test cases. Search-based techniques have been applied to find solutions for test case prioritization problem. Some of these works apply Ant Colony based algorithms, but the precedence of test cases was not considered. We propose an Ant Colony Optim...

Aimed at the problems that classical ant colony algorithm is easy to fall into local optimal, this thesis puts forward a new AODV routing protocol based on improved geneticant colony algorithms (IGAACA-AODV) by introducing genetic algorithm (GA) to improve ant colony algorithm, and combining with the characteristics of AODV routing protocols in Ad Hoc network. First of all, the proposed algorit...

Army ants form nomadic insect colonies whose chief food is other social insects. Here we compare the rate of army ant raids with the average density of their potential prey from 28 New World subtropical and tropical localities. We estimate that army ant raids occur at the rate of 1.22 m per day in tropical forests. Army ant raid rates increased with primary productivity, and with the density of...

A novel version of Ant Colony Optimization (ACO) algorithms for solving continuous space problems is presented in this paper. The basic structure and concepts of the originally reported ACO are preserved and adaptation of the algorithm to the case of continuous space is implemented within the general framework. The stigmergic communication is simulated through considering certain direction vect...

Support Vector Machines are considered to be excellent patterns classification techniques. The process of classifying a pattern with high classification accuracy counts mainly on tuning Support Vector Machine parameters which are the generalization error parameter and the kernel function parameter. Tuning these parameters is a complex process and Ant Colony Optimization can be used to overcome ...

In this paper we apply the concept of parallel processing to enhance the performance of the Ant Colony System algorithm. New exchange strategies based on a weighting scheme are introduced under three different types of interactions. A search assessment technique based on a team consensus methodology is developed to study the influence of these strategies on the search behavior. This technique d...

One merit of genetic algorithm is fast overall searching, but this algorithm usually results in low efficiency because of large quantities of redundant codes. The advantages of ant colony algorithm are strong suitability and good robustness while its disadvantages are tendency to stagnation, slow speed of convergence. Put forward based on improved ant colony algorithm for wireless sensor networ...

Recently, various sophisticated strategies adapted to current network conditions have been proposed for channel allocation based on intelligent techniques such as evolutionary and genetic algorithms. These approaches constitute heuristic solutions to resource management problems in modern cellular systems. On the other hand, the ant colony optimization approach has been proposed for solving sev...