Performance comparison of discrete particle swarm optimisation and shuffled frog leaping algorithm in multiprocessor task scheduling problem

Particle swarm optimisation (PSO) and Shuffled frog leaping (SFL) are Swarm Intelligence (SI) based algorithms. SI algorithms are stochastic based optimisation techniques that imitate process inspired from nature. This paper presents a comparative performance of two recent SI based optimisation algorithms such as discrete PSO (DPSO) and SFL in task scheduling problem. Task scheduling (TS) is a complex combinatorial optimisation problem and known to be NP-hard. It is an important challenging issue in distributed systems. Make span, mean flow time and reliability cost are performance criteria used to evaluate the efficiency of the DPSO and SFL algorithms for scheduling independent tasks in distributed systems. Computational simulations are done based on a set of benchmark instances to assess the performance of the algorithms.

[1]  Domagoj Jakobovic,et al.  Dynamic Scheduling with Genetic Programming , 2006, EuroGP.

[2]  Hong He,et al.  A novel discrete particle swarm optimization algorithm for meta-task assignment in heterogeneous computing systems , 2011, Microprocess. Microsystems.

[3]  Jon Atli Benediktsson,et al.  Automatic registration of multi-temporal remote sensing images based on nature-inspired techniques , 2014 .

[4]  Kevin E Lansey,et al.  Optimization of Water Distribution Network Design Using the Shuffled Frog Leaping Algorithm , 2003 .

[5]  Sudhanshu Prakash Tiwari,et al.  Grid Scheduling Using PSO with SPV Rule , 2012 .

[6]  Wu Yang,et al.  An Improved Shuffled Frog Leaping Algorithm for Grid Task Scheduling , 2011, 2011 International Conference on Network Computing and Information Security.

[7]  Donald E. Grierson,et al.  Comparison among five evolutionary-based optimization algorithms , 2005, Adv. Eng. Informatics.

[8]  Sakti Prasad Ghoshal,et al.  Radiation pattern optimization for concentric circular antenna array with central element feeding using craziness-based particle swarm optimization , 2010 .

[9]  K. Umamaheswari,et al.  Shuffled Frog Leaping Algorithm in Distributed System , 2015 .

[10]  Xiao Qin,et al.  Dynamic, reliability-driven scheduling of parallel real-time jobs in heterogeneous systems , 2001, International Conference on Parallel Processing, 2001..

[11]  Dan Simon,et al.  Oppositional biogeography-based optimization for combinatorial problems , 2011, 2011 IEEE Congress of Evolutionary Computation (CEC).

[12]  Fahd Alharbi,et al.  Simple Scheduling Algorithm with Load Balancing for Grid Computing , 2012 .

[13]  José Gabriel Ramírez-Torres,et al.  A Statistical Study of the Effects of Neighborhood Topologies in Particle Swarm Optimization , 2011 .

[14]  Ji Liu,et al.  Grouping-Shuffling Particle Swarm Optimization: An Improved PSO for Continuous Optimization , 2010, ICSI.

[15]  Yuehui Chen,et al.  A Task Scheduling Algorithm Based on PSO for Grid Computing , 2008 .

[16]  Garrison W. Greenwood,et al.  Scheduling tasks in multiprocessor systems using evolutionary strategies , 1994, Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence.

[17]  C. Christober Asir Rajan,et al.  Hybrid: Particle Swarm Optimization–Genetic Algorithm and Particle Swarm Optimization–Shuffled Frog Leaping Algorithm for long-term generator maintenance scheduling , 2015 .

[18]  Yuping Wang,et al.  Grid Independent Task Scheduling Multi-Objective Optimization Model and Genetic Algorithm , 2010, J. Comput..

[19]  Jian Xie,et al.  Independent Tasks Scheduling Based on Genetic Algorithm in Cloud Computing , 2009, 2009 5th International Conference on Wireless Communications, Networking and Mobile Computing.

[20]  Angel Eduardo Muñoz Zavala,et al.  A Comparison Study of PSO Neighborhoods , 2012, EVOLVE.

[21]  Howard Jay Siegel,et al.  Representing Task and Machine Heterogeneities for Heterogeneous Computing Systems , 2000 .

[22]  V. Mani,et al.  Multiobjective Discrete Particle Swarm Optimization for Multisensor Image Alignment , 2013, IEEE Geoscience and Remote Sensing Letters.

[23]  Ling Ding,et al.  A task scheduling algorithm for heterogeneous systems using ACO , 2013, 2013 2nd International Symposium on Instrumentation and Measurement, Sensor Network and Automation (IMSNA).

[24]  Francisco Herrera,et al.  A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms , 2011, Swarm Evol. Comput..

[25]  David B. Fogel,et al.  Using evolutionary programming to schedule tasks on a suite of heterogeneous computers , 1996, Comput. Oper. Res..

[26]  Fei Li,et al.  PSO based Hierarchical Task Scheduling with QoS Preference Awareness in Cloud Storage Environment , 2014, J. Softw..