This paper presents a method of optimized PID parameter self-adapted ant colony algorithm with aberrance gene, based on ant colony algorithm. This method overcomes genetic algorithm’s defects of repeated iteration, slower solving efficiency, ordinary ant colony algorithm’s defects of slow convergence speed, easy to get stagnate, and low ability of full search. For a given system, the results of...

Job scheduling is one of the main factors affecting grid computing performance. Job scheduling problem classified as an NP-hard problem. Such a problem can be solved only by using approximate algorithms such as heuristic and meta-heuristic algorithms. Ant colony system algorithm is a meta-heuristic algorithm which has the ability to solve differenttypes of NP-hard problems.However, ant colony s...

This paper describes GRAF-ANT (Graphical Framework for Ant Colony Optimization), an objectoriented C# framework for developing ant colony systems that we have developed. While developing this framework, abstractions that are necessary for ant colony optimization algorithms were analyzed, as well as the features that their implementing classes should have. During creation of these classes, sever...

Solving large traveling salesman problem (TSP) in an efficient way is a challenging area for the researchers of computer science. This paper presents a modified version of the ant colony system (ACS) algorithm called Red-Black Ant Colony System (RB-ACS) for the solutions of TSP which is the most prominent member of the combinatorial optimization problem. RB-ACS uses the concept of ant colony sy...

-In this paper a hybrid variant of meta-heuristic algorithm ant colony optimization (ACO) is used. Approximate solutions to quadratic assignment problem have been proved very efficient. Different variants of ant colony optimization have been applied to QAP. But in this paper a hybrid approach is proposed which is combination of Ant system and Max-Min Ant system to take benefits of both the meth...