Improved auto control ant colony optimization using lazy ant approach for grid scheduling problem

• Published in: Future generation computer systems Vol. 60; pp. 78 - 89
• Main Authors: Tiwari, Pawan Kumar, Vidyarthi, Deo Prakash
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.07.2016
• Subjects: Auto controlled Ant Colony Optimization (AACO); Grid Scheduling Problem (GSP); Particle Swarm Optimization (PSO); Quantum Genetic Algorithm (QGA); Simulated Annealing (SA); Quantum Genetic Algorithm (QGA); Auto controlled Ant Colony Optimization (AACO); Particle Swarm Optimization (PSO); Grid Scheduling Problem (GSP); Simulated Annealing (SA)
• ISSN: 0167-739X; 1872-7115
• DOI: 10.1016/j.future.2016.01.017

An auto controlled ant colony optimization algorithm controls the behavior of the ant colony algorithm automatically based on a priori heuristic. During the experimental study of auto controlled ACO algorithm on grid scheduling problem, it was observed that the induction of lazy ants not only reduces the time complexity of the algorithm but also produces better results on the given objectives. Lazy ants are basically a mutated version of active ants that remain alive till the fitter lazy ants are generated in the successive generations. This work presents an improved auto controlled ACO algorithm using the lazy ant concept. Performance study reveals the efficacy and the efficiency achieved by the proposed algorithm. A comparative study of the proposed method with some other recent meta-heuristics such as auto controlled ant colony optimization algorithm, genetic algorithm, quantum genetic algorithm, simulated annealing and particle swarm optimization for grid scheduling problem exhibits so. •This work proposes an improvement in auto controlled ant colony optimization method for grid scheduling problem.•Auto Control mechanism dynamically adapts changes in GSP through the ant.•The concept of Lazy Ants has been introduced, which fully exploits the solution around the best ant.•The algorithm is effective in producing better result as well as efficient in computational time over some contemporary ACO, PSO, GA, QGA and AACO for GSP.