Solving multi-processor task scheduling problem using a combinatorial evolutionary algorithm

Adineh-Vand, Auob, Parandin, Fariborz, Rahimi Azghadi, Mostafa, and Khalilzadeh, Alireza (2009) Solving multi-processor task scheduling problem using a combinatorial evolutionary algorithm. In: Proceedings of the 9th Workshop on Models and Algorithms for Planning and Scheduling Problems, pp. 91-93. From: 9th Workshop on Models and Algorithms for Planning and Scheduling Problems, 29 June - 3 July 2009, Kerkrade, The Netherlands.

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Abstract

Scheduling problem in multiprocessor, parallel and distributed systems are placed in NP-hard problems arena. These scheduling problems are employed in different important applications such as information processing, whether forecasting, image processing, database systems, process control, economics, operation research, and other areas. The data for these applications should be disseminated on different processors. Consequently efficient communication and well-organized assignments of jobs to processors are our concerns in solving multiprocessor task scheduling problems. This paper presents a new scheduling method which uses a local search technique. This local search algorithm is a combinatorial algorithm which combines Shuffled Frog Leaping (SFL), and Civilization and Society algorithms (CSA). This local search technique is a general algorithm which has been used to solve other problems such as the TSP before this. In addition to this combinatorial local search algorithm, a heuristic method is used to increase convergence speed of the genetic algorithm. Simulation results show that the proposed combinatorial method works better than other well known scheduling approaches.

Item ID: 45709
Item Type: Conference Item (Presentation)
Date Deposited: 16 Aug 2017 02:20
FoR Codes: 08 INFORMATION AND COMPUTING SCIENCES > 0801 Artificial Intelligence and Image Processing > 080108 Neural, Evolutionary and Fuzzy Computation @ 100%
SEO Codes: 97 EXPANDING KNOWLEDGE > 970110 Expanding Knowledge in Technology @ 100%
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