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.
(Less)
- author
- Maulik, Ujjwal
and Sarkar, Anasua
LU
- publishing date
- 2010-03-25
- 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
- 2022-02-15 05:13:11
@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}},
keywords = {{Amount of domination; Archive; Multi-objective optimization; Parallel algorithm; Rough sets theory; Simulated annealing}},
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}},
}