Ant Colony Optimization Inspired Swarm Optimization for Grid Task Scheduling
| Autor: | Yin-Mou Shen, Ruey-Maw Chen, Ching-Te Wang |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
020203 distributed computing
Mathematical optimization Meta-optimization Computer science Ant colony optimization algorithms Particle swarm optimization Swarm behaviour 02 engineering and technology Parallel metaheuristic Derivative-free optimization 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Multi-swarm optimization Metaheuristic |
| Zdroj: | 2016 International Symposium on Computer, Consumer and Control (IS3C). |
| DOI: | 10.1109/is3c.2016.122 |
| Popis: | Metaheuristic algorithm is efficient for solving NP-complete problems such as scheduling. An algorithm called the exploration control particle swarm optimization (ECPSO) is proposed to solve grid task scheduling, a new velocity update rule on the basis of exploration capability is suggested, an exploration control probability (ECP) parameter is designed to control the search behaviour of particles to improve search efficiency. Meanwhile, a novel local search is also suggested for further improving the search efficiency. The proposed scheme has advantages of exploration preserving during search, complexity and computation time reduced and easy implementation. The experimental results indicate that the ECPSO proposed can effectively solve grid task scheduling problems. |
| Databáze: | OpenAIRE |
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