Ant colony optimisation for task matching and scheduling

Communication-aware task scheduling and voltage selection for total energy minimization in a multiprocessor system using Ant Colony Optimization

“…On the other hand, metaheuristics were applied to generalized scheduling problems [24,4,28,37,32,25,38,18,26,7,2,30,33,16,40]. Increasing studies about soft computing techniques or metaheuristics based on Monte Carlo methods, such as genetic algorithm and ACO, were due to the NP hardness of the combinatorial problems.…”

“…Different from an iterative search of neighborhood solutions based on local search algorithms, the metaheuristics performed a coarse-grained search or global search for candidate solutions. In particular, several works in [38,18,26,7,2,5] applied ACO to task scheduling for multiprocessor systems statically. However, those works focused only on performance optimization in order to minimize the completion time of applications.…”

An Analysis of Task Scheduling in Cloud Computing using Evolutionary and Swarm-based Algorithms

“…Chiang, Lee, Lee and Chou (2006) [21] proposed an algorithm ACO-TMS that adopted a new state transition rule that reduced the time required when finding the satisfactory scheduling results, along with a local search procedure to help improve the scheduling results. Li, Xu, Zhao, Dong and Wang (2011) [22] proposed a cloud task scheduling policy based on Load Balancing Ant Colony Optimization (LBACO) algorithm that aimed at stabilizing the whole workload, besides optimizing the makespan of the tasks set.…”

“…This rule gives a feedback mechanism to speed up convergence, and also prevents premature solution stagnation. Due to the elaborate characteristics of ACO, various algorithms based on the ACO meta-heuristic have been applied to many difficult optimization problems [21].…”

Optimization of System’s Performance with Kernel Tracing by Cohort Intelligence

“…Scheduling is central to an Operating-system's design and constitutes an important topic in the computer science curriculum. Heuristics like Genetic Algorithm (GA) [2] [3], Ant Colony Optimization (ACO) [4] [5] have been implemented on the scheduler to optimize system performance like maximize resource utilization, minimal execution time etc. which shows better results.…”