FEATURE SELECTION AND CLUSTERING BY MULTI-OBJECTIVE OPTIMIZATION

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DARYABARI SEYEDEH MOHTARAM; RAMEZANI FARHAD

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Journal Paper

Paper Information

Journal: JOURNAL OF ADVANCES IN COMPUTER RESEARCH Year:2017 | Volume:8 | Issue:3 (29) Page(s): 69-77

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Information Journal Paper

Title

FEATURE SELECTION AND CLUSTERING BY MULTI-OBJECTIVE OPTIMIZATION

Author(s)

DARYABARI SEYEDEH MOHTARAM | RAMEZANI FARHAD | Issue Writer Certificate

Keywords

CLUSTERING

DATA MINING

FEATURE SELECTION

MULTI-OBJECTIVE OPTIMIZATION

NSGA-II

Abstract

Here we discuss the problem of distribution of Real time processes on multiprocessor with on-time maximum job accomplished. Scientists have been searching for producing optimized scheduling. This is an example of NP problems. This is not practical to approach this kind of problems with heuristic approach thus we must use meta-heuristic algorithms. These algorithms present many sets of answers in order to make options for scheduler, to choose the best process assignment to processor. Two examples are Branch and Bound, and Task Graph Algorithms. By studying the ant colony, Genetics and PSO Algorithms, we will design and consider several methods for our purpose and use them to produce Job assignment Scheduler, on processors. Each of these algorithms will provide us with a specific designing method and help us to make a scheduler engine of real time processes assignment on processors. We will compare each program to the first heuristic one, to assess the manufactured programs. In comparisons which are based on lost processes, Colony approach has 11.94 %, PSO approach 11.19 %, and Genetic approach has 7.52 % less process lost in compare to heuristic approach. It worth mention that 20 files each of which containing 50 Real time process have been used in these experiments.

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APA: Copy

DARYABARI, SEYEDEH MOHTARAM, & RAMEZANI, FARHAD. (2017). FEATURE SELECTION AND CLUSTERING BY MULTI-OBJECTIVE OPTIMIZATION. JOURNAL OF ADVANCES IN COMPUTER RESEARCH, 8(3 (29)), 69-77. SID. https://sid.ir/paper/328879/en

Vancouver: Copy

DARYABARI SEYEDEH MOHTARAM, RAMEZANI FARHAD. FEATURE SELECTION AND CLUSTERING BY MULTI-OBJECTIVE OPTIMIZATION. JOURNAL OF ADVANCES IN COMPUTER RESEARCH[Internet]. 2017;8(3 (29)):69-77. Available from: https://sid.ir/paper/328879/en

IEEE: Copy

SEYEDEH MOHTARAM DARYABARI, and FARHAD RAMEZANI, “FEATURE SELECTION AND CLUSTERING BY MULTI-OBJECTIVE OPTIMIZATION,” JOURNAL OF ADVANCES IN COMPUTER RESEARCH, vol. 8, no. 3 (29), pp. 69–77, 2017, [Online]. Available: https://sid.ir/paper/328879/en

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