Improved strategies of multi-objective differential evolution (MODE) for multi-objective optimization

Ashish M. Gujarathi; B. V. Babu

Improved strategies of multi-objective differential evolution (MODE) for multi-objective optimization

Ashish M. Gujarathi^*, B. V. Babu

^*Corresponding author for this work

Petroleum and Chemical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

7 Citations (Scopus)

Abstract

Multi-objective optimization using an evolutionary computation technique is used extensively for solving conflicting multi-objective optimization problems. In this work, an improved strategy of multi-objective differential evolution (MODE) where the mutation strategy is changed to a trigonometric mutation approach is proposed. The proposed strategy along with other well known strategies of MODE is used to compare the performance metrics (such as convergence and divergence) with other evolutionary algorithms from the literature. The Pareto optimal solutions are obtained for benchmark test functions and are compared using several strategies of MODE. Improved strategies of MODE show a competitive performance when compared with other evolutionary multi-objective optimization algorithms (EMOAs).

Original language	English
Title of host publication	Proceedings of the 4th Indian International Conference on Artificial Intelligence, IICAI 2009
Pages	933-948
Number of pages	16
Publication status	Published - 2009
Event	4th Indian International Conference on Artificial Intelligence, IICAI 2009 - Tumkur, India Duration: Dec 16 2009 → Dec 18 2009

Other

Other	4th Indian International Conference on Artificial Intelligence, IICAI 2009
Country	India
City	Tumkur
Period	12/16/09 → 12/18/09

Keywords

Differential evolution
Evolutionary algorithms
Multi-objective optimization
Optimization
Trigonometric mutation

ASJC Scopus subject areas

Artificial Intelligence

Fingerprint Dive into the research topics of 'Improved strategies of multi-objective differential evolution (MODE) for multi-objective optimization'. Together they form a unique fingerprint.

Cite this

@inproceedings{9540a7484ffe4e22be9d77d16b500c60,

title = "Improved strategies of multi-objective differential evolution (MODE) for multi-objective optimization",

abstract = "Multi-objective optimization using an evolutionary computation technique is used extensively for solving conflicting multi-objective optimization problems. In this work, an improved strategy of multi-objective differential evolution (MODE) where the mutation strategy is changed to a trigonometric mutation approach is proposed. The proposed strategy along with other well known strategies of MODE is used to compare the performance metrics (such as convergence and divergence) with other evolutionary algorithms from the literature. The Pareto optimal solutions are obtained for benchmark test functions and are compared using several strategies of MODE. Improved strategies of MODE show a competitive performance when compared with other evolutionary multi-objective optimization algorithms (EMOAs).",

keywords = "Differential evolution, Evolutionary algorithms, Multi-objective optimization, Optimization, Trigonometric mutation",

author = "Gujarathi, {Ashish M.} and Babu, {B. V.}",

year = "2009",

language = "English",

isbn = "9780972741279",

pages = "933--948",

booktitle = "Proceedings of the 4th Indian International Conference on Artificial Intelligence, IICAI 2009",

note = "4th Indian International Conference on Artificial Intelligence, IICAI 2009 ; Conference date: 16-12-2009 Through 18-12-2009",

}

TY - GEN

T1 - Improved strategies of multi-objective differential evolution (MODE) for multi-objective optimization

AU - Gujarathi, Ashish M.

AU - Babu, B. V.

PY - 2009

Y1 - 2009

N2 - Multi-objective optimization using an evolutionary computation technique is used extensively for solving conflicting multi-objective optimization problems. In this work, an improved strategy of multi-objective differential evolution (MODE) where the mutation strategy is changed to a trigonometric mutation approach is proposed. The proposed strategy along with other well known strategies of MODE is used to compare the performance metrics (such as convergence and divergence) with other evolutionary algorithms from the literature. The Pareto optimal solutions are obtained for benchmark test functions and are compared using several strategies of MODE. Improved strategies of MODE show a competitive performance when compared with other evolutionary multi-objective optimization algorithms (EMOAs).

AB - Multi-objective optimization using an evolutionary computation technique is used extensively for solving conflicting multi-objective optimization problems. In this work, an improved strategy of multi-objective differential evolution (MODE) where the mutation strategy is changed to a trigonometric mutation approach is proposed. The proposed strategy along with other well known strategies of MODE is used to compare the performance metrics (such as convergence and divergence) with other evolutionary algorithms from the literature. The Pareto optimal solutions are obtained for benchmark test functions and are compared using several strategies of MODE. Improved strategies of MODE show a competitive performance when compared with other evolutionary multi-objective optimization algorithms (EMOAs).

KW - Differential evolution

KW - Evolutionary algorithms

KW - Multi-objective optimization

KW - Optimization

KW - Trigonometric mutation

UR - http://www.scopus.com/inward/record.url?scp=84861862636&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84861862636&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:84861862636

SN - 9780972741279

SP - 933

EP - 948

BT - Proceedings of the 4th Indian International Conference on Artificial Intelligence, IICAI 2009

T2 - 4th Indian International Conference on Artificial Intelligence, IICAI 2009

Y2 - 16 December 2009 through 18 December 2009

ER -