Ant colony optimization algorithm for dynamic scheduling of jobs in computational grid

Computational grid is gaining more importance due to the needs for large-scale computing capacity. In computational grid, job scheduling is one of the main factors affecting grid computing performance. Job scheduling problem is classified as an NP-hard problem.Such a problem can be solved only by using approximate algorithms such as heuristic and meta-heuristic algorithms.Among different optimization algorithms for job scheduling, ant colony system algorithm is a popular meta-heuristic algorithm which has the ability to solve different types of NP-hard problems.However, ant colony system algorithm has a deficiency in its heuristic function which affects the algorithm behavior in terms of finding the shortest connection between edges.This research focuses on a new heuristic function where information about recent ants’ discoveries has been considered.The new heuristic function has been integrated into the classical ant colony system algorithm.Furthermore, the enhanced algorithm has been implemented to solve the travelling salesman problem as well as in scheduling of jobs in computational grid.A simulator with dynamic environment feature to mimic real life application has been development to validate the proposed enhanced ant colony system algorithm. Experimental results show that the proposed enhanced algorithm produced better output in term of utilization and makespan in both domains

A hybrid ant colony optimization approach (hACO) for constructing load-balanced clusters

Nodes in an ad hoc network are usually organized into clusters, with each cluster being coordinated by a node acting as the cluster head (CH). Cluster members are one hop away from their CH. The collection of CHs give rise to a graph structure known as a dominating set. This paper proposes a hybrid ACO (hACO) approach that, when given a graph representing a network, selects a set of CHs in such a way that enables the remaining nodes to be distributed as evenly as possible to these CHs while ensuring that the maximum load a CH can take is maintained. Artificial ants are used to select the CHs. After a CH is selected, a heuristic is used to determine cluster member assignment. Solution quality is measured using a metric called the load balancing factor (LBF). We benchmark the performance of the hACO against a recently proposed genetic algorithm (GA) that addresses the same problem. Empirical results point to the fact that hACO consistently produced good solutions across the 41 problem instances. Even though GA gave the best solutions for several instances, its performance deteriorated for graphs with relatively higher density. We explain this behavior by examining the clusters produced by both methods