A Study On Evolutionary Optimization Based Scheduling Algorithm Techniques For Parallel Processors

A B S T R A C T Parallel processing is a field in which different systems run together to save the time of the processing and to increase the performance of the system. The processor assignment part is also known as clustering in the literature when there is no limitation in the number of processors and the architecture is completely connected. Soft Computing is the fusion of methodologies that were designed to model and enable solutions to real world problems, which are not modeled or too difficult to model, mathematically. Soft computing techniques provide an ability to make decisions and learning from the reliable data or expert’s experience. Moreover, soft computing techniques can cope up with a variety of environmental and stability related uncertainties and various soft computing techniques that are discussed in this paper are Genetic Algorithms, Fuzzy Logic, Ant Colony Optimization, Particle’s Swarm Optimization and Artificial Bee Colony Optimization.

[1]  J. C. Dunn,et al.  A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters , 1973 .

[2]  Gabriele Kotsis,et al.  Parallelization strategies for the ant system , 1998 .

[3]  Cyril Fonlupt,et al.  Parallel Ant Colonies for Combinatorial Optimization Problems , 1999, IPPS/SPDP Workshops.

[4]  J. Kennedy,et al.  Population structure and particle swarm performance , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[5]  Dervis Karaboga,et al.  AN IDEA BASED ON HONEY BEE SWARM FOR NUMERICAL OPTIMIZATION , 2005 .

[6]  James C. Bezdek,et al.  Pattern Recognition with Fuzzy Objective Function Algorithms , 1981, Advanced Applications in Pattern Recognition.

[7]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[8]  Tiago Ferra de Sousa,et al.  Particle Swarm based Data Mining Algorithms for classification tasks , 2004, Parallel Comput..

[9]  Francisco Herrera,et al.  Gradual distributed real-coded genetic algorithms , 2000, IEEE Trans. Evol. Comput..

[10]  L. Darrell Whitley,et al.  Serial and Parallel Genetic Algorithms as Function Optimizers , 1993, ICGA.

[11]  Marco Dorigo,et al.  Ant system: optimization by a colony of cooperating agents , 1996, IEEE Trans. Syst. Man Cybern. Part B.

[12]  Thomas Stützle,et al.  The Ant Colony Optimization Metaheuristic: Algorithms, Applications, and Advances , 2003 .

[13]  Xin Yao,et al.  Evolving artificial neural networks , 1999, Proc. IEEE.

[14]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[15]  Inderjit S. Dhillon,et al.  A Data-Clustering Algorithm on Distributed Memory Multiprocessors , 1999, Large-Scale Parallel Data Mining.

[16]  Thomas Stützle,et al.  Parallelization Strategies for Ant Colony Optimization , 1998, PPSN.