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Evolutionary rough parallel multi-objective optimization algorithm

Maulik, Ujjwal and Sarkar, Anasua LU (2010) In Fundamenta Informaticae 99(1). p.13-27
Abstract

A hybrid unsupervised learning algorithm, which is termed as Parallel Rough-based Archived Multi-Objective Simulated Annealing (PARAMOSA), is proposed in this article. It comprises a judicious integration of the principles of the rough sets theory and the scalable distributed paradigm with the archived multi-objective simulated annealing approach. While the concept of boundary approximations of rough sets in this implementation, deals with the incompleteness in the dynamic classification method with the quality of classification coefficient as the classificatory competencemeasurement, the time-efficient parallel approach enables faster convergence of the Pareto-archived evolution strategy. It incorporates both the rough set-based... (More)

A hybrid unsupervised learning algorithm, which is termed as Parallel Rough-based Archived Multi-Objective Simulated Annealing (PARAMOSA), is proposed in this article. It comprises a judicious integration of the principles of the rough sets theory and the scalable distributed paradigm with the archived multi-objective simulated annealing approach. While the concept of boundary approximations of rough sets in this implementation, deals with the incompleteness in the dynamic classification method with the quality of classification coefficient as the classificatory competencemeasurement, the time-efficient parallel approach enables faster convergence of the Pareto-archived evolution strategy. It incorporates both the rough set-based dynamic archive classifi- cation method and the distributed implementation as a two-phase speedup strategy in this algorithm. A measure of the amount of domination between two solutions has been incorporated in this work to determine the acceptance probability of a new solution with an improvement in the spread of the non-dominated solutions in the Pareto-front by adopting rough sets theory. A complexity analysis of the proposed algorithm is provided. An extensive comparative study of the proposed algorithm with three other existing and well-known Multi-Objective Evolutionary Algorithms (MOEAs) demonstrate the effectiveness of the former with respect to four existing performance metrics and eleven benchmark test problems of varying degrees of difficulties. The superiority of this new parallel implementation over other algorithms also has been demonstrated in timing, which achieves a near optimal speedup with a minimal communication overhead.

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author
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publishing date
type
Contribution to journal
publication status
published
keywords
Amount of domination, Archive, Multi-objective optimization, Parallel algorithm, Rough sets theory, Simulated annealing
in
Fundamenta Informaticae
volume
99
issue
1
pages
13 - 27
publisher
IOS Press
external identifiers
  • scopus:77949595515
ISSN
0169-2968
DOI
10.3233/FI-2010-237
language
English
LU publication?
no
id
0cc6ea6e-e88c-48cb-a5c9-205510ba2809
date added to LUP
2018-10-09 09:57:23
date last changed
2020-01-13 01:01:30
@article{0cc6ea6e-e88c-48cb-a5c9-205510ba2809,
  abstract     = {<p>A hybrid unsupervised learning algorithm, which is termed as Parallel Rough-based Archived Multi-Objective Simulated Annealing (PARAMOSA), is proposed in this article. It comprises a judicious integration of the principles of the rough sets theory and the scalable distributed paradigm with the archived multi-objective simulated annealing approach. While the concept of boundary approximations of rough sets in this implementation, deals with the incompleteness in the dynamic classification method with the quality of classification coefficient as the classificatory competencemeasurement, the time-efficient parallel approach enables faster convergence of the Pareto-archived evolution strategy. It incorporates both the rough set-based dynamic archive classifi- cation method and the distributed implementation as a two-phase speedup strategy in this algorithm. A measure of the amount of domination between two solutions has been incorporated in this work to determine the acceptance probability of a new solution with an improvement in the spread of the non-dominated solutions in the Pareto-front by adopting rough sets theory. A complexity analysis of the proposed algorithm is provided. An extensive comparative study of the proposed algorithm with three other existing and well-known Multi-Objective Evolutionary Algorithms (MOEAs) demonstrate the effectiveness of the former with respect to four existing performance metrics and eleven benchmark test problems of varying degrees of difficulties. The superiority of this new parallel implementation over other algorithms also has been demonstrated in timing, which achieves a near optimal speedup with a minimal communication overhead.</p>},
  author       = {Maulik, Ujjwal and Sarkar, Anasua},
  issn         = {0169-2968},
  language     = {eng},
  month        = {03},
  number       = {1},
  pages        = {13--27},
  publisher    = {IOS Press},
  series       = {Fundamenta Informaticae},
  title        = {Evolutionary rough parallel multi-objective optimization algorithm},
  url          = {http://dx.doi.org/10.3233/FI-2010-237},
  doi          = {10.3233/FI-2010-237},
  volume       = {99},
  year         = {2010},
}