Rules discovery in fuzzy classifier systems with PSO for scheduling in grid computational infrastructures |
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| Affiliation: | 1. School of Computer Science and Technology, Xidian University, Xi’an, China;2. School of Computer Science and Technology, Xi’an Jiaotong University, Xi’an, China;3. School of Software, Xidian University, Xi’an, China;1. Electric and Electronics Engineering Department, Bilecik Şeyh Edebali University, Turkey;2. Computer Engineering Department, Dumlupınar University, Turkey;1. School of Information Science & Engineering, Central South University, Changsha, Hunan 410083, China;2. Research Organization of Information and Systems, 4-3-13 Toranomon, Minato-ku, Tokyo 105-0001, Japan;3. The Institute of Statistical Mathematics, 10-3 Midori-cho, Tachikawa, Tokyo 190-8562, Japan;4. Hunan Province Higher Education Key Laboratory of Power System Safety Operation and Control, Changsha University of Science and Technology, Changsha, Hunan 410004, China;5. School of Business, Central South University, Changsha, Hunan 410083, China;6. Collaborative Innovation Center of Resource-Conserving & Environment-Friendly Society and Ecological Civilization, Changsha, Hunan 410083, China;1. International Islamic University, Islamabad, Pakistan;2. Gwangju Institute of Science and Technology, South Korea |
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| Abstract: | Particle swarm optimization (PSO) is a bio-inspired optimization strategy founded on the movement of particles within swarms. PSO can be encoded in a few lines in most programming languages, it uses only elementary mathematical operations, and it is not costly as regards memory demand and running time. This paper discusses the application of PSO to rules discovery in fuzzy classifier systems (FCSs) instead of the classical genetic approach and it proposes a new strategy, Knowledge Acquisition with Rules as Particles (KARP). In KARP approach every rule is encoded as a particle that moves in the space in order to cooperate in obtaining high quality rule bases and in this way, improving the knowledge and performance of the FCS. The proposed swarm-based strategy is evaluated in a well-known problem of practical importance nowadays where the integration of fuzzy systems is increasingly emerging due to the inherent uncertainty and dynamism of the environment: scheduling in grid distributed computational infrastructures. Simulation results are compared to those of classical genetic learning for fuzzy classifier systems and the greater accuracy and convergence speed of classifier discovery systems using KARP is shown. |
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| Keywords: | Fuzzy computing Fuzzy classifier systems Particle swarm optimization Grid computing |
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