Improved auto control ant colony optimization using lazy ant approach for grid scheduling problem |
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| Affiliation: | 1. Culverhouse College of Commerce, ISM Department, The University of Alabama, Box 870226, Tuscaloosa, AL 35487, USA;2. College of Business, ASOR Department, Bowling Green State University, 355 Business Administration, Bowling Green, OH 43403, USA;1. MoE Key Laboratory of Complex Systems Modeling and Simulation, School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310018, China;2. Jiangsu Key Laboratory of Image and Video Understanding for Social Safety, Nanjing University of Science and Technology, Nanjing 210094, China;3. Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China;1. Department of Computer Science, University of Holguín, Holguín, Cuba;2. Department of Computer Science, Universidad Central de Las Villas, Villa Clara, Cuba;3. Department of Computer Science and Numerical Analysis, University of Córdoba, Córdoba, Spain;4. Department of Computer Science, King Abdulaziz University, Jeddah, Saudi Arabia |
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| Abstract: | 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. |
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| Keywords: | Auto controlled Ant Colony Optimization (AACO) Grid Scheduling Problem (GSP) Quantum Genetic Algorithm (QGA) Simulated Annealing (SA) Particle Swarm Optimization (PSO) |
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