Parallel Computation Approaches to Optimize Learning Systems

Document Type

Article

Publication Date

7-2011

Publication Title

International Journal of Electronics and Telecommunications

Volume

57

Issue

2

First page number:

223

Last page number:

228

Abstract

This paper is devoted to the total tardiness minimization scheduling problem, where the efficiency of a processor increases due to its learning. Such problems model real-life settings that occur in the presence of a human learning (industry, manufacturing, management) and in some computer systems. However, the increasing growth of significant achievements in the field of artificial intelligence and machine learning is a premise that the human-like learning will be present in mechanized industrial processes that are controlled or performed by machines as well as in the greater number of multi-agent computer systems. Therefore, the optimization algorithms dedicated in this paper for scheduling problems with learning are not only the answer for present day scheduling problems (where human plays important role), but they are also a step forward to the improvement of self-learning and adapting systems that undeniably will occur in a new future. To solve the analysed problem, we propose parallel computation approaches that are based on NEH, tabu search and simulated annealing algorithms. The numerical analysis confirm high accuracy of these methods and show that the presented approaches significantly decrease running times of simulated annealing and tabu search and also reduce the running times of NEH.

Keywords

Artificial intelligence; Heuristic programming; Machine learning; Parallel computers; Parallel scheduling (Computer scheduling)

Disciplines

Computer and Systems Architecture | Electrical and Computer Engineering | Electrical and Electronics | Signal Processing | Systems and Communications

Language

English

Permissions

Use Find in Your Library, contact the author, or use interlibrary loan to garner a copy of the article. Publisher copyright policy allows author to archive post-print (author’s final manuscript). When post-print is available or publisher policy changes, the article will be deposited

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