“Making Concessions in Order to Gain Advantages” Improved Ant Colony Optimization for Improving Job Scheduling Problems

Abstract

Ant Colony Optimization (ACO) converges on the optimal path with pheromones cumulating and updating, adopting the mechanism of distributed parallel search. Ant colony system is well self-adoptive and dynamic with making full use of current feedback, which is similar to the dynamic performance of the grid and is proved to be an effective algorithm to solve scheduling problems. But the existing ant colony algorithm can not solve the scheduling problems liking misusing good performance resources for minor purposes. This paper presents a “making concessions in order to gain advantages” algorithm—an improved algorithm based on Ant Colony Optimization (ACO) algorithm for job scheduling problems. Experimental results show that improved ACO approach can solve the problem and outperform ACO.